,
Denes Turei [cre, aut] ,
Attila Gabor [aut] ,
Diego Mananes [aut] ,
Aurelien Dugourd [aut] | Title: | OmniPath web service client and more |
|---|---|
| Description: | A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github). |
| Authors: | Alberto Valdeolivas [aut] ,
Denes Turei [cre, aut] ,
Attila Gabor [aut] ,
Diego Mananes [aut] ,
Aurelien Dugourd [aut] |
| Maintainer: | Denes Turei <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 3.15.0 |
| Built: | 2024-11-18 03:38:38 UTC |
| Source: | https://github.com/bioc/OmnipathR |
These options describe the default settings for OmnipathR so you do not need to pass these parameters at each function call. Currently the only option useful for the public web service at omnipathdb.org is “omnipathr.license“. If you are a for-profit user set it to “'commercial'“ to make sure all the data you download from OmniPath is legally allowed for commercial use. Otherwise just leave it as it is: “'academic'“. If you don't use omnipathdb.org but within your organization you deployed your own pypath server and want to share data whith a limited availability to outside users, you may want to use a password. For this you can use the “omnipathr.password“ option. Also if you want the R package to work from another pypath server instead of omnipathdb.org, you can change the option “omnipathr.url“.
.omnipathr_options_defaults.omnipathr_options_defaults
An object of class list of length 25.
Nothing, this is not a function but a list.
All UniProt ACs for one organism
all_uniprot_acs(organism = 9606, reviewed = TRUE)all_uniprot_acs(organism = 9606, reviewed = TRUE)
organism |
Character or integer: name or identifier of the organism. |
reviewed |
Retrieve only reviewed ('TRUE'), only unreviewed ('FALSE') or both ('NULL'). |
Character vector of UniProt accession numbers.
human_swissprot_acs <- all_uniprot_acs() human_swissprot_acs[1:5] # [1] "P51451" "A6H8Y1" "O60885" "Q9Y3X0" "P22223" length(human_swissprot_acs) # [1] 20376 mouse_swissprot_acs <- all_uniprot_acs("mouse")human_swissprot_acs <- all_uniprot_acs() human_swissprot_acs[1:5] # [1] "P51451" "A6H8Y1" "O60885" "Q9Y3X0" "P22223" length(human_swissprot_acs) # [1] 20376 mouse_swissprot_acs <- all_uniprot_acs("mouse")
Retrieves a table from UniProt with all proteins for a certain organism.
all_uniprots(fields = "accession", reviewed = TRUE, organism = 9606L)all_uniprots(fields = "accession", reviewed = TRUE, organism = 9606L)
fields |
Character vector of fields as defined by UniProt. For possible values please refer to https://www.uniprot.org/help/return_fields |
reviewed |
Retrieve only reviewed ('TRUE'), only unreviewed ('FALSE') or both ('NULL'). |
organism |
Character or integer: name or identifier of the organism. |
Data frame (tibble) with the requested UniProt entries and fields.
human_swissprot_entries <- all_uniprots(fields = 'id') human_swissprot_entries # # A tibble: 20,396 x 1 # `Entry name` # <chr> # 1 OR4K3_HUMAN # 2 O52A1_HUMAN # 3 O2AG1_HUMAN # 4 O10S1_HUMAN # 5 O11G2_HUMAN # # . with 20,386 more rowshuman_swissprot_entries <- all_uniprots(fields = 'id') human_swissprot_entries # # A tibble: 20,396 x 1 # `Entry name` # <chr> # 1 OR4K3_HUMAN # 2 O52A1_HUMAN # 3 O2AG1_HUMAN # 4 O10S1_HUMAN # 5 O11G2_HUMAN # # . with 20,386 more rows
Starting from the selected nodes, recursively walks the ontology tree until it reaches the root. Collects all visited nodes, which are the ancestors (parents) of the starting nodes.
ancestors( terms, db_key = "go_basic", ids = TRUE, relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates", "negatively_regulates") )ancestors( terms, db_key = "go_basic", ids = TRUE, relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates", "negatively_regulates") )
terms |
Character vector of ontology term IDs or names. A mixture of IDs and names can be provided. |
db_key |
Character: key to identify the ontology database. For the
available keys see |
ids |
Logical: whether to return IDs or term names. |
relations |
Character vector of ontology relation types. Only these relations will be used. |
Note: this function relies on the database manager, the first call might
take long because of the database load process. Subsequent calls within
a short period should be faster. See get_ontology_db.
Character vector of ontology IDs. If the input terms are all
root nodes, NULL is returned. The starting nodes won't be
included in the result unless some of them are ancestors of other
starting nodes.
ancestors('GO:0005035', ids = FALSE) # [1] "molecular_function" # [2] "transmembrane signaling receptor activity" # [3] "signaling receptor activity" # [4] "molecular transducer activity"ancestors('GO:0005035', ids = FALSE) # [1] "molecular_function" # [2] "transmembrane signaling receptor activity" # [3] "signaling receptor activity" # [4] "molecular transducer activity"
Annotations are often useful in a network context, e.g. one might want to
label the interacting partners by their pathway membership. This function
takes a network data frame and joins an annotation data frame from both
the left and the right side, so both the source and target molecular
entities will be labeled by their annotations. If one entity has many
annotations these will yield many rows, hence the interacting pairs won't
be unique across the data frame any more. Also if one entity has really
many annotations the resulting data frame might be huge, we recommend to
be careful with that. Finally, if you want to do the same but with
intercell annotations, there is the import_intercell_network
function.
annotated_network( network = NULL, annot = NULL, network_args = list(), annot_args = list(), ... )annotated_network( network = NULL, annot = NULL, network_args = list(), annot_args = list(), ... )
network |
Behaviour depends on type: if list, will be passed as
arguments to |
annot |
Either the name of an annotation resource (for a list of
available resources call |
network_args |
List: if 'network' is a resource name, pass these
additional arguments to |
annot_args |
List: if 'annot' is a resource name, pass these
additional arguments to |
... |
Column names selected from the annotation data frame (passed
to |
A data frame of interactions with annotations for both interacting entities.
signalink_with_pathways <- annotated_network("SignaLink3", "SignaLink_pathway")signalink_with_pathways <- annotated_network("SignaLink3", "SignaLink_pathway")
A full list of annotation resources, keys and values.
annotation_categories()annotation_categories()
A data frame with resource names, annotation key labels and for each key all possible values.
annot_cat <- annotation_categories() annot_cat # # A tibble: 46,307 x 3 # source label value # <chr> <chr> <chr> # 1 connectomeDB2020 role ligand # 2 connectomeDB2020 role receptor # 3 connectomeDB2020 location ECM # 4 connectomeDB2020 location plasma membrane # 5 connectomeDB2020 location secreted # 6 KEGG-PC pathway Alanine, aspartate and glutamate metabolism # 7 KEGG-PC pathway Amino sugar and nucleotide sugar metabolism # 8 KEGG-PC pathway Aminoacyl-tRNA biosynthesis # 9 KEGG-PC pathway Arachidonic acid metabolism # 10 KEGG-PC pathway Arginine and proline metabolismannot_cat <- annotation_categories() annot_cat # # A tibble: 46,307 x 3 # source label value # <chr> <chr> <chr> # 1 connectomeDB2020 role ligand # 2 connectomeDB2020 role receptor # 3 connectomeDB2020 location ECM # 4 connectomeDB2020 location plasma membrane # 5 connectomeDB2020 location secreted # 6 KEGG-PC pathway Alanine, aspartate and glutamate metabolism # 7 KEGG-PC pathway Amino sugar and nucleotide sugar metabolism # 8 KEGG-PC pathway Aminoacyl-tRNA biosynthesis # 9 KEGG-PC pathway Arachidonic acid metabolism # 10 KEGG-PC pathway Arginine and proline metabolism
Get the names of the resources from https://omnipathdb.org/annotations.
annotation_resources(dataset = NULL, ...)annotation_resources(dataset = NULL, ...)
dataset |
ignored for this query type |
... |
optional additional arguments |
character vector with the names of the annotation resources
annotation_resources()annotation_resources()
Protein and gene annotations about function, localization, expression, structure and other properties, from the https://omnipathdb.org/annotations endpoint of the OmniPath web service. Note: there might be also a few miRNAs annotated; a vast majority of protein complex annotations are inferred from the annotations of the members: if all members carry the same annotation the complex inherits.
annotations(proteins = NULL, wide = FALSE, ...)annotations(proteins = NULL, wide = FALSE, ...)
proteins |
Vector containing the genes or proteins for whom annotations will be retrieved (UniProt IDs or HGNC Gene Symbols or miRBase IDs). It is also possible to donwload annotations for protein complexes. To do so, write 'COMPLEX:' right before the genesymbols of the genes integrating the complex. Check the vignette for examples. |
wide |
Convert the annotation table to wide format, which
corresponds more or less to the original resource. If the data comes
from more than one resource a list of wide tables will be returned.
See examples at |
... |
Arguments passed on to
|
Downloading the full annotations
dataset is disabled by default because the size of this data is
around 1GB. We recommend to retrieve the annotations for a set of proteins
or only from a few resources, depending on your interest. You can always
download the full database from
https://archive.omnipathdb.org/omnipath_webservice_annotations__recent.tsv
using any standard R or readr method.
A data frame or list of data frames:
If wide=FALSE (default), all the requested resources
will be in a single long format data frame.
If wide=TRUE: one or more data frames with columns
specific to the requested resources. If more than one resources
is requested a list of data frames is returned.
annotations <- annotations( proteins = c("TP53", "LMNA"), resources = c("HPA_subcellular") )annotations <- annotations( proteins = c("TP53", "LMNA"), resources = c("HPA_subcellular") )
Query the Ensembl BioMart web service
biomart_query( attrs = NULL, filters = NULL, transcript = FALSE, peptide = FALSE, gene = FALSE, dataset = "hsapiens_gene_ensembl" )biomart_query( attrs = NULL, filters = NULL, transcript = FALSE, peptide = FALSE, gene = FALSE, dataset = "hsapiens_gene_ensembl" )
attrs |
Character vector: one or more Ensembl attribute names. |
filters |
Character vector: one or more Ensembl filter names. |
transcript |
Logical: include Ensembl transcript IDs in the result. |
peptide |
Logical: include Ensembl peptide IDs in the result. |
gene |
Logical: include Ensembl gene IDs in the result. |
dataset |
Character: An Ensembl dataset name. |
Data frame with the query result
cel_genes <- biomart_query( attrs = c("external_gene_name", "start_position", "end_position"), gene = TRUE, dataset = "celegans_gene_ensembl" ) cel_genes # # A tibble: 46,934 × 4 # ensembl_gene_id external_gene_name start_position end_position # <chr> <chr> <dbl> <dbl> # 1 WBGene00000001 aap-1 5107843 5110183 # 2 WBGene00000002 aat-1 9599178 9601695 # 3 WBGene00000003 aat-2 9244402 9246360 # 4 WBGene00000004 aat-3 2552260 2557736 # 5 WBGene00000005 aat-4 6272529 6275721 # # . with 46,924 more rowscel_genes <- biomart_query( attrs = c("external_gene_name", "start_position", "end_position"), gene = TRUE, dataset = "celegans_gene_ensembl" ) cel_genes # # A tibble: 46,934 × 4 # ensembl_gene_id external_gene_name start_position end_position # <chr> <chr> <dbl> <dbl> # 1 WBGene00000001 aap-1 5107843 5110183 # 2 WBGene00000002 aat-1 9599178 9601695 # 3 WBGene00000003 aat-2 9244402 9246360 # 4 WBGene00000004 aat-3 2552260 2557736 # 5 WBGene00000005 aat-4 6272529 6275721 # # . with 46,924 more rows
BioPlex provides four interaction datasets: version 1.0, 2.0, 3.0 and HCT116 version 1.0. This function downloads all of them, merges them to one data frame, removes the duplicates (based on unique pairs of UniProt IDs) and separates the isoform numbers from the UniProt IDs. More details at https://bioplex.hms.harvard.edu/interactions.php.
bioplex_all(unique = TRUE)bioplex_all(unique = TRUE)
unique |
Logical. Collapse the duplicate interactions into single rows or keep them as they are. In case of merging duplicate records the maximum p value will be choosen for each record. |
Data frame (tibble) with interactions.
bioplex_interactions <- bioplex_all() bioplex_interactions # # A tibble: 195,538 x 11 # UniprotA IsoformA UniprotB IsoformB GeneA GeneB SymbolA SymbolB # <chr> <int> <chr> <int> <dbl> <dbl> <chr> <chr> # 1 A0AV02 2 Q5K4L6 NA 84561 11000 SLC12A8 SLC27A3 # 2 A0AV02 2 Q8N5V2 NA 84561 25791 SLC12A8 NGEF # 3 A0AV02 2 Q9H6S3 NA 84561 64787 SLC12A8 EPS8L2 # 4 A0AV96 2 O00425 2 54502 10643 RBM47 IGF2BP3 # 5 A0AV96 2 O00443 NA 54502 5286 RBM47 PIK3C2A # 6 A0AV96 2 O43426 NA 54502 8867 RBM47 SYNJ1 # 7 A0AV96 2 O75127 NA 54502 26024 RBM47 PTCD1 # 8 A0AV96 2 O95208 2 54502 22905 RBM47 EPN2 # 9 A0AV96 2 O95900 NA 54502 26995 RBM47 TRUB2 # 10 A0AV96 2 P07910 2 54502 3183 RBM47 HNRNPC # # . with 195,528 more rows, and 3 more variables: p_wrong <dbl>, # # p_no_interaction <dbl>, p_interaction <dbl>bioplex_interactions <- bioplex_all() bioplex_interactions # # A tibble: 195,538 x 11 # UniprotA IsoformA UniprotB IsoformB GeneA GeneB SymbolA SymbolB # <chr> <int> <chr> <int> <dbl> <dbl> <chr> <chr> # 1 A0AV02 2 Q5K4L6 NA 84561 11000 SLC12A8 SLC27A3 # 2 A0AV02 2 Q8N5V2 NA 84561 25791 SLC12A8 NGEF # 3 A0AV02 2 Q9H6S3 NA 84561 64787 SLC12A8 EPS8L2 # 4 A0AV96 2 O00425 2 54502 10643 RBM47 IGF2BP3 # 5 A0AV96 2 O00443 NA 54502 5286 RBM47 PIK3C2A # 6 A0AV96 2 O43426 NA 54502 8867 RBM47 SYNJ1 # 7 A0AV96 2 O75127 NA 54502 26024 RBM47 PTCD1 # 8 A0AV96 2 O95208 2 54502 22905 RBM47 EPN2 # 9 A0AV96 2 O95900 NA 54502 26995 RBM47 TRUB2 # 10 A0AV96 2 P07910 2 54502 3183 RBM47 HNRNPC # # . with 195,528 more rows, and 3 more variables: p_wrong <dbl>, # # p_no_interaction <dbl>, p_interaction <dbl>
This dataset contains ~71,000 interactions detected in HCT116 cells using 5,522 baits. More details at https://bioplex.hms.harvard.edu/interactions.php.
bioplex_hct116_1()bioplex_hct116_1()
Data frame (tibble) with interactions.
bioplex_interactions <- bioplex_hct116_1() nrow(bioplex_interactions) # [1] 70966 colnames(bioplex_interactions) # [1] "GeneA" "GeneB" "UniprotA" "UniprotB" # [5] "SymbolA" "SymbolB" "p_wrong" "p_no_interaction" # [9] "p_interaction"bioplex_interactions <- bioplex_hct116_1() nrow(bioplex_interactions) # [1] 70966 colnames(bioplex_interactions) # [1] "GeneA" "GeneB" "UniprotA" "UniprotB" # [5] "SymbolA" "SymbolB" "p_wrong" "p_no_interaction" # [9] "p_interaction"
This dataset contains ~24,000 interactions detected in HEK293T cells using 2,594 baits. More details at https://bioplex.hms.harvard.edu/interactions.php.
bioplex1()bioplex1()
Data frame (tibble) with interactions.
bioplex_interactions <- bioplex1() nrow(bioplex_interactions) # [1] 23744 colnames(bioplex_interactions) # [1] "GeneA" "GeneB" "UniprotA" "UniprotB" # [5] "SymbolA" "SymbolB" "p_wrong" "p_no_interaction" # [9] "p_interaction"bioplex_interactions <- bioplex1() nrow(bioplex_interactions) # [1] 23744 colnames(bioplex_interactions) # [1] "GeneA" "GeneB" "UniprotA" "UniprotB" # [5] "SymbolA" "SymbolB" "p_wrong" "p_no_interaction" # [9] "p_interaction"
This dataset contains ~56,000 interactions detected in HEK293T cells using 5,891 baits. More details at https://bioplex.hms.harvard.edu/interactions.php
bioplex2()bioplex2()
Data frame (tibble) with interactions.
bioplex_interactions <- bioplex2() nrow(bioplex_interactions) # [1] 56553 colnames(bioplex_interactions) # [1] "GeneA" "GeneB" "UniprotA" "UniprotB" # [5] "SymbolA" "SymbolB" "p_wrong" "p_no_interaction" # [9] "p_interaction"bioplex_interactions <- bioplex2() nrow(bioplex_interactions) # [1] 56553 colnames(bioplex_interactions) # [1] "GeneA" "GeneB" "UniprotA" "UniprotB" # [5] "SymbolA" "SymbolB" "p_wrong" "p_no_interaction" # [9] "p_interaction"
This dataset contains ~120,000 interactions detected in HEK293T cells using 10,128 baits. More details at https://bioplex.hms.harvard.edu/interactions.php.
bioplex3()bioplex3()
Data frame (tibble) with interactions.
bioplex_interactions <- bioplex3() nrow(bioplex_interactions) # [1] 118162 colnames(bioplex_interactions) # [1] "GeneA" "GeneB" "UniprotA" "UniprotB" # [5] "SymbolA" "SymbolB" "p_wrong" "p_no_interaction" # [9] "p_interaction"bioplex_interactions <- bioplex3() nrow(bioplex_interactions) # [1] 118162 colnames(bioplex_interactions) # [1] "GeneA" "GeneB" "UniprotA" "UniprotB" # [5] "SymbolA" "SymbolB" "p_wrong" "p_no_interaction" # [9] "p_interaction"
These motifs can be added to a BMA canvas.
bma_motif_es(edge_seq, G, granularity = 2)bma_motif_es(edge_seq, G, granularity = 2)
edge_seq |
An igraph edge sequence. |
G |
An igraph graph object. |
granularity |
Numeric: granularity value. |
Character: BMA motifs as a single string.
interactions <- omnipath(resources = "ARN") graph <- interaction_graph(interactions) motifs <- bma_motif_es(igraph::E(graph)[1], graph)interactions <- omnipath(resources = "ARN") graph <- interaction_graph(interactions) motifs <- bma_motif_es(igraph::E(graph)[1], graph)
Intended to parallel print_path_vs
bma_motif_vs(node_seq, G)bma_motif_vs(node_seq, G)
node_seq |
An igraph node sequence. |
G |
An igraph graph object. |
Character: BMA motifs as a single string.
interactions <- omnipath(resources = "ARN") graph <- interaction_graph(interactions) bma_string <- bma_motif_vs( igraph::all_shortest_paths( graph, from = 'ULK1', to = 'ATG13' )$res, graph )interactions <- omnipath(resources = "ARN") graph <- interaction_graph(interactions) bma_string <- bma_motif_vs( igraph::all_shortest_paths( graph, from = 'ULK1', to = 'ATG13' )$res, graph )
Process the GEMs from Wang et al., 2021 (https://github.com/SysBioChalmers) into convenient tables.
chalmers_gem(organism = "Human", orphans = TRUE)chalmers_gem(organism = "Human", orphans = TRUE)
organism |
Character or integer: an organism (taxon) identifier. Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus), 10116 (Rattus norvegicus), 7955 (Danio rerio), 7227 (Drosophila melanogaster) and 6239 (Caenorhabditis elegans). |
orphans |
Logical: include orphan reactions (reactions without known enzyme). |
List containing the following elements:
reactions: tibble of reaction data;
metabolites: tibble of metabolite data;
reaction_ids: translation table of reaction identifiers;
metabolite_ids: translation table of metabolite identifiers;
S: Stoichiometric matrix (sparse).
Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul 27;118(30):e2102344118. doi: doi:10.1073/pnas.2102344118.
gem <- chalmers_gem()gem <- chalmers_gem()
Metabolite ID translation tables from Chalmers Sysbio
chalmers_gem_id_mapping_table(to, from = "metabolicatlas", organism = "Human")chalmers_gem_id_mapping_table(to, from = "metabolicatlas", organism = "Human")
to |
Character: type of ID to translate to, either label used internally in this package, or a column name from "metabolites.tsv" distributed by Chalmers Sysbio. NSE is supported. |
from |
Character: type of ID to translate from, same format as "to". |
organism |
Character or integer: name or identifier of the organism. Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus), 10116 (Rattus norvegicu), 7955 (Danio rerio), 7227 (Drosophila melanogaster) and 6239 (Caenorhabditis elegans). |
Tibble with two columns, "From" and "To", with the corresponding ID types.
chalmers_gem_id_mapping_table('metabolicatlas', 'hmdb')chalmers_gem_id_mapping_table('metabolicatlas', 'hmdb')
Metabolite identifier type label used in Chalmers Sysbio GEM
chalmers_gem_id_type(label)chalmers_gem_id_type(label)
label |
Character: an ID type label, as shown in the table at
|
Character: the Chalmers GEM specific ID type label, or the input unchanged if it could not be translated (still might be a valid identifier name). These labels should be column names from the "metabolites.tsv" distributed with the GEMs.
chalmers_gem_id_type("metabolicatlas") # [1] "metsNoComp"chalmers_gem_id_type("metabolicatlas") # [1] "metsNoComp"
Metabolites from the Chalmers SysBio GEM (Wang et al., 2021)
chalmers_gem_metabolites(organism = "Human")chalmers_gem_metabolites(organism = "Human")
organism |
Character or integer: an organism (taxon) identifier. Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus), 10116 (Rattus norvegicu), 7955 (Danio rerio), 7227 (Drosophila melanogaster) and 6239 (Caenorhabditis elegans). |
Data frame of metabolite identifiers.
Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul 27;118(30):e2102344118. doi: doi:10.1073/pnas.2102344118.
chalmers_gem_metabolites()chalmers_gem_metabolites()
Processing GEMs from Wang et al., 2021 (https://github.com/SysBioChalmers) to generate PKN for COSMOS
chalmers_gem_network( organism_or_gem = "Human", metab_max_degree = 400L, protein_ids = c("uniprot", "genesymbol"), metabolite_ids = c("hmdb", "kegg") )chalmers_gem_network( organism_or_gem = "Human", metab_max_degree = 400L, protein_ids = c("uniprot", "genesymbol"), metabolite_ids = c("hmdb", "kegg") )
organism_or_gem |
Character or integer or list or data frame: either
an organism (taxon) identifier or a list containing the “reactions“
data frame as it is provided by |
metab_max_degree |
Degree cutoff used to prune metabolites with high degree assuming they are cofactors (400 by default). |
protein_ids |
Character: translate the protein identifiers to these ID types. Each ID type results two extra columns in the output, for the "a" and "b" sides of the interaction, respectively. The default ID type for proteins is Esembl Gene ID, and by default UniProt IDs and Gene Symbols are included. |
metabolite_ids |
Character: translate the protein identifiers to these ID types. Each ID type results two extra columns in the output, for the "a" and "b" sides of the interaction, respectively. The default ID type for metabolites is Metabolic Atlas ID, and HMDB IDs and KEGG IDs are included. |
Data frame (tibble) of gene-metabolite interactions.
Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul 27;118(30):e2102344118. doi: doi:10.1073/pnas.2102344118.
gem <- chalmers_gem_network()gem <- chalmers_gem_network()
Downloads and imports the matlab file containing the genome scale metabolic models created by Chalmers SysBio.
chalmers_gem_raw(organism = "Human")chalmers_gem_raw(organism = "Human")
organism |
Character or integer: name or identifier of the organism. Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus), 10116 (Rattus norvegicu), 7955 (Danio rerio), 7227 (Drosophila melanogaster) and 6239 (Caenorhabditis elegans). |
The Matlab object is parsed into a nested list containing a number of vectors and two sparse matrices. The top level contains a single element under the name "ihuman" for human; under this key there is an array of 31 elements. These elements are labeled by the row names of the array.
Matlab object containing the GEM.
Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul 27;118(30):e2102344118. doi: doi:10.1073/pnas.2102344118.
chalmers_gem_raw()chalmers_gem_raw()
Reactions from the Chalmers SysBio GEM (Wang et al., 2021)
chalmers_gem_reactions(organism = "Human")chalmers_gem_reactions(organism = "Human")
organism |
Character or integer: an organism (taxon) identifier. Supported taxons are 9606 (Homo sapiens), 10090 (Mus musculus), 10116 (Rattus norvegicu), 7955 (Danio rerio), 7227 (Drosophila melanogaster) and 6239 (Caenorhabditis elegans). |
Data frame of reaction identifiers.
Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci U S A. 2021 Jul 27;118(30):e2102344118. doi: doi:10.1073/pnas.2102344118.
chalmers_gem_reactions()chalmers_gem_reactions()
Common (English) names of organisms
common_name(name)common_name(name)
name |
Vector with any kind of organism name or identifier, can be also mixed type. |
Character vector with common (English) taxon names, NA if a name in the input could not be found.
common_name(c(10090, "cjacchus", "Vicugna pacos")) # [1] "Mouse" "White-tufted-ear marmoset" "Alpaca"common_name(c(10090, "cjacchus", "Vicugna pacos")) # [1] "Mouse" "White-tufted-ear marmoset" "Alpaca"
This function returns all the molecular complexes where an input set of genes participate. User can choose to retrieve every complex where any of the input genes participate or just retrieve these complexes where all the genes in input set participate together.
complex_genes(complexes = complexes(), genes, all_genes = FALSE)complex_genes(complexes = complexes(), genes, all_genes = FALSE)
complexes |
Data frame of protein complexes (obtained using
|
genes |
Character: search complexes where these genes present. |
all_genes |
Logical: select only complexes where all of the genes present together. By default complexes where any of the genes can be found are returned. |
Data frame of complexes
complexes <- complexes(resources = c("CORUM", "hu.MAP")) query_genes <- c("LMNA", "BANF1") complexes_with_query_genes <- complex_genes(complexes, query_genes)complexes <- complexes(resources = c("CORUM", "hu.MAP")) query_genes <- c("LMNA", "BANF1") complexes_with_query_genes <- complex_genes(complexes, query_genes)
Get the names of the resources from https://omnipathdb.org/complexes
complex_resources(dataset = NULL)complex_resources(dataset = NULL)
dataset |
ignored for this query type |
character vector with the names of the databases
complex_resources()complex_resources()
A comprehensive dataset of protein complexes from the https://omnipathdb.org/complexes endpoint of the OmniPath web service.
complexes(...)complexes(...)
... |
Arguments passed on to
|
A data frame of protein complexes.
cplx <- complexes(resources = c("CORUM", "hu.MAP"))cplx <- complexes(resources = c("CORUM", "hu.MAP"))
Compiles a table of binary interactions from ConsensusPathDB (http://cpdb.molgen.mpg.de/) and translates the UniProtKB ACs to Gene Symbols.
consensuspathdb_download(complex_max_size = 4, min_score = 0.9)consensuspathdb_download(complex_max_size = 4, min_score = 0.9)
complex_max_size |
Numeric: do not expand complexes with a higher number of elements than this. ConsensusPathDB does not contain conventional interactions but lists of participants, which might be members of complexes. Some records include dozens of participants and expanding them to binary interactions result thousands, sometimes hundreds of thousands of interactions from one single record. At the end, this process consumes >10GB of memory and results rather unusable data, hence it is recommended to limit the complex sizes at some low number. |
min_score |
Numeric: each record in ConsensusPathDB comes with a confidence score, expressing the amount of evidences. The default value, a minimum score of 0.9 retains approx. the top 30 percent of the interactions. |
Data frame (tibble) with interactions.
## Not run: cpdb_data <- consensuspathdb_download( complex_max_size = 1, min_score = .99 ) nrow(cpdb_data) # [1] 252302 colnames(cpdb_data) # [1] "databases" "references" "uniprot_a" "confidence" "record_id" # [6] "uniprot_b" "in_complex" "genesymbol_a" "genesymbol_b" cpdb_data # # A tibble: 252,302 x 9 # databases references uniprot_a confidence record_id uniprot_b in_com # <chr> <chr> <chr> <dbl> <int> <chr> <lgl> # 1 Reactome NA SUMF2_HU. 1 1 SUMF1_HU. TRUE # 2 Reactome NA SUMF1_HU. 1 1 SUMF2_HU. TRUE # 3 DIP,Reac. 22210847,. STIM1_HU. 0.998 2 TRPC1_HU. TRUE # 4 DIP,Reac. 22210847,. TRPC1_HU. 0.998 2 STIM1_HU. TRUE # # . with 252,292 more rows, and 2 more variables: genesymbol_a <chr>, # # genesymbol_b <chr ## End(Not run)## Not run: cpdb_data <- consensuspathdb_download( complex_max_size = 1, min_score = .99 ) nrow(cpdb_data) # [1] 252302 colnames(cpdb_data) # [1] "databases" "references" "uniprot_a" "confidence" "record_id" # [6] "uniprot_b" "in_complex" "genesymbol_a" "genesymbol_b" cpdb_data # # A tibble: 252,302 x 9 # databases references uniprot_a confidence record_id uniprot_b in_com # <chr> <chr> <chr> <dbl> <int> <chr> <lgl> # 1 Reactome NA SUMF2_HU. 1 1 SUMF1_HU. TRUE # 2 Reactome NA SUMF1_HU. 1 1 SUMF2_HU. TRUE # 3 DIP,Reac. 22210847,. STIM1_HU. 0.998 2 TRPC1_HU. TRUE # 4 DIP,Reac. 22210847,. TRPC1_HU. 0.998 2 STIM1_HU. TRUE # # . with 252,292 more rows, and 2 more variables: genesymbol_a <chr>, # # genesymbol_b <chr ## End(Not run)
Downloads interaction data from ConsensusPathDB
consensuspathdb_raw_table()consensuspathdb_raw_table()
Data frame (tibble) with interactions.
cpdb_raw <- consensuspathdb_raw_table()cpdb_raw <- consensuspathdb_raw_table()
Acquire a cookie if necessary
cookie( url, init_url = NULL, post = NULL, payload = NULL, init_post = NULL, init_payload = NULL, curl_verbose = FALSE )cookie( url, init_url = NULL, post = NULL, payload = NULL, init_post = NULL, init_payload = NULL, curl_verbose = FALSE )
url |
Character. URL to download to get the cookie. |
init_url |
Character. An initial URL to download to get the cookie, before downloading “url“ with the cookie. |
post |
List: HTTP POST parameters. |
payload |
Data to send as payload. |
init_post |
List: HTTP POST parameters for “init_url“. |
init_payload |
Data to send as payload with “init_url“. |
curl_verbose |
Logical. Perform CURL requests in verbose mode for debugging purposes. |
A list with cache file path, cookies and response headers.
The prior knowledge network (PKN) used by COSMOS is a network of heterogenous causal interactions: it contains protein-protein, reactant-enzyme and enzyme-product interactions. It is a combination of multiple resources:
Genome-scale metabolic model (GEM) from Chalmers Sysbio (Wang et al., 2021.)
Network of chemical-protein interactions from STITCH (https://stitch.embl.de/)
Protein-protein interactions from Omnipath (Türei et al., 2021)
This function downloads, processes and combines the resources above. With all downloads and processing the build might take 30-40 minutes. Data is cached at various levels of processing, shortening processing times. With all data downloaded and HMDB ID translation data preprocessed, the build takes 3-4 minutes; the complete PKN is also saved in the cache, if this is available, loading it takes only a few seconds.
cosmos_pkn( organism = "human", protein_ids = c("uniprot", "genesymbol"), metabolite_ids = c("hmdb", "kegg"), chalmers_gem_metab_max_degree = 400L, stitch_score = 700L, ... )cosmos_pkn( organism = "human", protein_ids = c("uniprot", "genesymbol"), metabolite_ids = c("hmdb", "kegg"), chalmers_gem_metab_max_degree = 400L, stitch_score = 700L, ... )
organism |
Character or integer: name or NCBI Taxonomy ID of an organism. Supported organisms vary by resource: the Chalmers GEM is available only for human, mouse, rat, fish, fly and worm. OmniPath can be translated by orthology, but for non-vertebrate or less researched taxa very few orthologues are available. STITCH is available for a large number of organisms, please refer to their web page: https://stitch.embl.de/. |
protein_ids |
Character: translate the protein identifiers to these ID types. Each ID type results two extra columns in the output, for the "source" and "target" sides of the interaction, respectively. The default ID type for proteins depends on the resource, hence the "source" and "target" columns are heterogenous. By default UniProt IDs and Gene Symbols are included. The Gene Symbols used in the COSMOS PKN are provided by Ensembl, and do not completely agree with the ones provided by UniProt and used in OmniPath data by default. |
metabolite_ids |
Character: translate the metabolite identifiers to these ID types. Each ID type results two extra columns in the output, for the "source" and "target" sides of the interaction, respectively. The default ID type for metabolites depends on the resource, hence the "source" and "target" columns are heterogenous. By default HMDB IDs and KEGG IDs are included. |
chalmers_gem_metab_max_degree |
Numeric: remove metabolites from the Chalmers GEM network with defgrees larger than this. Useful to remove cofactors and over-promiscuous metabolites. |
stitch_score |
Include interactions from STITCH with combined confidence score larger than this. |
... |
Further parameters to |
A data frame of binary causal interations with effect signs, resource specific attributes and translated to the desired identifiers. The “record_id“ column identifies the original records within each resource. If one “record_id“ yields multiple records in the final data frame, it is the result of one-to-many ID translation or other processing steps. Before use, it is recommended to select one pair of ID type columns (by combining the preferred ones) and perform “distinct“ by the identifier columns and sign.
Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, et al. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proceedings of the National Academy of Sciences. 2021 Jul 27;118(30):e2102344118.
Türei D, Valdeolivas A, Gul L, Palacio‐Escat N, Klein M, Ivanova O, et al. Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis. Molecular Systems Biology. 2021 Mar;17(3):e9923.
## Not run: human_cosmos <- cosmos_pkn(organism = "human") ## End(Not run)## Not run: human_cosmos <- cosmos_pkn(organism = "human") ## End(Not run)
The OmniPath intercell database annotates individual proteins and
complexes, and we combine these annotations with network interactions
on the client side, using import_intercell_network. The
architecture of this database is complex, aiming to cover a broad range
of knowledge on various levels of details and confidence. We can use the
intercell_consensus_filter and
filter_intercell_network functions for automated, data
driven quality filtering, in order to enrich the cell-cell communication
network in higher confidence interactions. However, for many users, a
simple combination of the most established, expert curated ligand-receptor
resources, provided by this function, fits better their purpose.
curated_ligand_receptor_interactions( curated_resources = c("Guide2Pharma", "HPMR", "ICELLNET", "Kirouac2010", "CellTalkDB", "CellChatDB", "connectomeDB2020"), cellphonedb = TRUE, cellinker = TRUE, talklr = TRUE, signalink = TRUE, ... )curated_ligand_receptor_interactions( curated_resources = c("Guide2Pharma", "HPMR", "ICELLNET", "Kirouac2010", "CellTalkDB", "CellChatDB", "connectomeDB2020"), cellphonedb = TRUE, cellinker = TRUE, talklr = TRUE, signalink = TRUE, ... )
curated_resources |
Character vector of the resource names which are considered to be expert curated. You can include any post-translational network resource here, but if you include non ligand-receptor or non curated resources, the result will not fulfill the original intention of this function. |
cellphonedb |
Logical: include the curated interactions from CellPhoneDB (not the whole CellPhoneDB but a subset of it). |
cellinker |
Logical: include the curated interactions from Cellinker (not the whole Cellinker but a subset of it). |
talklr |
Logical: include the curated interactions from talklr (not the whole talklr but a subset of it). |
signalink |
Logical: include the ligand-receptor interactions from SignaLink. These are all expert curated. |
... |
Passed to |
Some resources are a mixture of curated and bulk imported interactions, and sometimes it's not trivial to separate these, we take care of these here. This function does not use the intercell database of OmniPath, but retrieves and filters a handful of network resources. The returned data frame has the layout of interactions (network) data frames, and the source and target partners implicitly correspond to ligand and receptor. The data frame shows all resources and references for all interactions, but each interaction is supported by at least one ligand-receptor resource which is supposed to based on expert curation in a ligand-receptor context.
A data frame similar to interactions (network) data frames, the source and target partners being ligand and receptor, respectively.
lr <- curated_ligand_receptor_interactions() lrlr <- curated_ligand_receptor_interactions() lr
Statistics about literature curated ligand-receptor interactions
curated_ligrec_stats(...)curated_ligrec_stats(...)
... |
Passed to |
The data frame contains the total number of interactions, the number of interactions which overlap with the set of curated interactions (curated_overlap), the number of interactions with literature references from the given resource (literature) and the number of interactions which are curated by the given resource (curated_self). This latter we defined according to our best knowledge, in many cases it's not possible to distinguish curated interactions). All these numbers are also presented as a percent of the total. Importantly, here we consider interactions curated only if they've been curated in a cell-cell communication context.
A data frame with estimated counts of curated ligand-receptor interactions for each L-R resource.
curated_ligand_receptor_interactions
clr <- curated_ligrec_stats() clrclr <- curated_ligrec_stats() clr
The 'annotations_summary' and 'intercell_summary' query types return detailed information on the contents of these databases. It includes all the available resources, fields and values in the database.
database_summary(query_type, return_df = FALSE)database_summary(query_type, return_df = FALSE)
query_type |
Character: either "annotations" or "intercell". |
return_df |
Logical: return a data frame instead of list. |
Summary of the database contents: the available resources, fields, and their possible values. As a nested list if format is "json", otherwise a data frame.
annotations_summary <- database_summary('annotations')annotations_summary <- database_summary('annotations')
From logical columns for each dataset, here we create a column that is a list of character vectors, containing dataset labels.
datasets_one_column(data, remove_logicals = TRUE)datasets_one_column(data, remove_logicals = TRUE)
data |
Interactions data frame with dataset columns (i.e. queried with the option 'fields = "datasets"'). |
remove_logicals |
Logical: remove the per dataset logical columns. |
The input data frame with the new column "datasets" added.
Starting from the selected nodes, recursively walks the ontology tree until it reaches the leaf nodes. Collects all visited nodes, which are the descendants (children) of the starting nodes.
descendants( terms, db_key = "go_basic", ids = TRUE, relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates", "negatively_regulates") )descendants( terms, db_key = "go_basic", ids = TRUE, relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates", "negatively_regulates") )
terms |
Character vector of ontology term IDs or names. A mixture of IDs and names can be provided. |
db_key |
Character: key to identify the ontology database. For the
available keys see |
ids |
Logical: whether to return IDs or term names. |
relations |
Character vector of ontology relation types. Only these relations will be used. |
Note: this function relies on the database manager, the first call might
take long because of the database load process. Subsequent calls within
a short period should be faster. See get_ontology_db.
Character vector of ontology IDs. If the input terms are all
leaves NULL is returned. The starting nodes won't be included
in the result unless some of them are descendants of other starting
nodes.
descendants('GO:0005035', ids = FALSE) # [1] "tumor necrosis factor-activated receptor activity" # [2] "TRAIL receptor activity" # [3] "TNFSF11 receptor activity"descendants('GO:0005035', ids = FALSE) # [1] "tumor necrosis factor-activated receptor activity" # [2] "TRAIL receptor activity" # [3] "TNFSF11 receptor activity"
Ensembl dataset name from organism
ensembl_dataset(organism)ensembl_dataset(organism)
organism |
Character or integer: an organism (taxon) name or identifier. If an Ensembl dataset name is provided |
Character: name of an ensembl dataset.
ensembl_dataset(10090) # [1] "mmusculus_gene_ensembl"ensembl_dataset(10090) # [1] "mmusculus_gene_ensembl"
Identifier translation table from Ensembl
ensembl_id_mapping_table(to, from = "uniprot", organism = 9606)ensembl_id_mapping_table(to, from = "uniprot", organism = 9606)
to |
Character or symbol: target ID type. See Details for possible values. |
from |
Character or symbol: source ID type. See Details for possible values. |
organism |
Character or integer: NCBI Taxonomy ID or name of the organism (by default 9606 for human). |
The arguments to and from can be provided either
as character or as symbol (NSE). Their possible values are either Ensembl
attribute names or synonyms listed at translate_ids.
A data frame (tibble) with columns 'From' and 'To'.
ensp_up <- ensembl_id_mapping_table("ensp") ensp_up # # A tibble: 119,129 × 2 # From To # <chr> <chr> # 1 P03886 ENSP00000354687 # 2 P03891 ENSP00000355046 # 3 P00395 ENSP00000354499 # 4 P00403 ENSP00000354876 # 5 P03928 ENSP00000355265 # # . with 119,124 more rowsensp_up <- ensembl_id_mapping_table("ensp") ensp_up # # A tibble: 119,129 × 2 # From To # <chr> <chr> # 1 P03886 ENSP00000354687 # 2 P03891 ENSP00000355046 # 3 P00395 ENSP00000354499 # 4 P00403 ENSP00000354876 # 5 P03928 ENSP00000355265 # # . with 119,124 more rows
Ensembl identifier type label
ensembl_id_type(label)ensembl_id_type(label)
label |
Character: an ID type label, as shown in the table at
|
Character: the Ensembl specific ID type label, or the input unchanged if it could not be translated (still might be a valid identifier name). These labels should be valid Ensembl attribute names, directly usable in Ensembl queries.
ensembl_id_type("uniprot") # [1] "uniprotswissprot"ensembl_id_type("uniprot") # [1] "uniprotswissprot"
Ensembl identifiers of organisms
ensembl_name(name)ensembl_name(name)
name |
Vector with any kind of organism name or identifier, can be also mixed type. |
Character vector with Ensembl taxon names, NA if a name in the input could not be found.
ensembl_name(c(9606, "cat", "dog")) # [1] "hsapiens" "fcatus" "clfamiliaris" ensembl_name(c("human", "kitten", "cow")) # [1] "hsapiens" NA "btaurus"ensembl_name(c(9606, "cat", "dog")) # [1] "hsapiens" "fcatus" "clfamiliaris" ensembl_name(c("human", "kitten", "cow")) # [1] "hsapiens" NA "btaurus"
A table with various taxon names and identifiers: English common names, latin (scientific) names, Ensembl organism IDs and NCBI taxonomy IDs.
ensembl_organisms()ensembl_organisms()
A data frame with the above mentioned columns.
ens_org <- ensembl_organisms() ens_orgens_org <- ensembl_organisms() ens_org
A table with various taxon IDs and metadata about related Ensembl database contents, as shown at https://www.ensembl.org/info/about/species.html. The "Taxon ID" column contains the NCBI Taxonomy identifiers.
ensembl_organisms_raw()ensembl_organisms_raw()
The table described above as a data frame.
ens_org <- ensembl_organisms_raw() ens_orgens_org <- ensembl_organisms_raw() ens_org
Orthologous gene pairs from Ensembl
ensembl_orthology( organism_a = 9606, organism_b = 10090, attrs_a = NULL, attrs_b = NULL, colrename = TRUE )ensembl_orthology( organism_a = 9606, organism_b = 10090, attrs_a = NULL, attrs_b = NULL, colrename = TRUE )
organism_a |
Character or integer: organism name or identifier for the left side organism. We query the Ensembl dataset of this organism and add the orthologues of the other organism to it. Ideally this is the organism you translate from. |
organism_b |
Character or integer: organism name or identifier for the right side organism. We add orthology information of this organism to the gene records of the left side organism. |
attrs_a |
Further attributes about organism_a genes. Will be simply added to the attributes list. |
attrs_b |
Further attributes about organism_b genes (orthologues). The available attributes are: "associated_gene_name", "chromosome", "chrom_start", "chrom_end", "wga_coverage", "goc_score", "perc_id_r1", "perc_id", "subtype". Attributes included by default: "ensembl_gene", "ensembl_peptide", "canonical_transcript_protein", "orthology_confidence" and "orthology_type". |
colrename |
Logical: replace prefixes from organism_b attribute column names, so the returned table always have the same column names, no matter the organism. E.g. for mouse these columns all have the prefix "mmusculus_homolog_", which this option changes to "b_". |
Only the records with orthology information are returned. The order of columns is the following: defaults of organism_a, extra attributes of organism_b, defaults of organism_b, extra attributes of organism_b.
A data frame of orthologous gene pairs with gene, transcript and peptide identifiers and confidence values.
## Not run: sffish <- ensembl_orthology( organism_b = 'Siamese fighting fish', attrs_a = 'external_gene_name', attrs_b = 'associated_gene_name' ) sffish # # A tibble: 175,608 × 10 # ensembl_gene_id ensembl_transcript_id ensembl_peptide. external_gene_n. # <chr> <chr> <chr> <chr> # 1 ENSG00000277196 ENST00000621424 ENSP00000481127 NA # 2 ENSG00000277196 ENST00000615165 ENSP00000482462 NA # 3 ENSG00000278817 ENST00000613204 ENSP00000482514 NA # 4 ENSG00000274847 ENST00000400754 ENSP00000478910 MAFIP # 5 ENSG00000273748 ENST00000612919 ENSP00000479921 NA # # . with 175,603 more rows, and 6 more variables: # # b_ensembl_peptide <chr>, b_ensembl_gene <chr>, # # b_orthology_type <chr>, b_orthology_confidence <dbl>, # # b_canonical_transcript_protein <chr>, b_associated_gene_name <chr> # ## End(Not run)## Not run: sffish <- ensembl_orthology( organism_b = 'Siamese fighting fish', attrs_a = 'external_gene_name', attrs_b = 'associated_gene_name' ) sffish # # A tibble: 175,608 × 10 # ensembl_gene_id ensembl_transcript_id ensembl_peptide. external_gene_n. # <chr> <chr> <chr> <chr> # 1 ENSG00000277196 ENST00000621424 ENSP00000481127 NA # 2 ENSG00000277196 ENST00000615165 ENSP00000482462 NA # 3 ENSG00000278817 ENST00000613204 ENSP00000482514 NA # 4 ENSG00000274847 ENST00000400754 ENSP00000478910 MAFIP # 5 ENSG00000273748 ENST00000612919 ENSP00000479921 NA # # . with 175,603 more rows, and 6 more variables: # # b_ensembl_peptide <chr>, b_ensembl_gene <chr>, # # b_orthology_type <chr>, b_orthology_confidence <dbl>, # # b_canonical_transcript_protein <chr>, b_associated_gene_name <chr> # ## End(Not run)
Converts a network to igraph object unless it is already one
ensure_igraph(network)ensure_igraph(network)
network |
Either an OmniPath interaction data frame, or an igraph graph object. |
An igraph graph object.
Transforms the a data frame with enzyme-substrate relationships
(obtained by enzyme_substrate) to an igraph graph object.
enzsub_graph(enzsub)enzsub_graph(enzsub)
enzsub |
Data frame created by |
An igraph directed graph object.
enzsub <- enzyme_substrate(resources = c('PhosphoSite', 'SIGNOR')) enzsub_g <- enzsub_graph(enzsub = enzsub)enzsub <- enzyme_substrate(resources = c('PhosphoSite', 'SIGNOR')) enzsub_g <- enzsub_graph(enzsub = enzsub)
Get the names of the enzyme-substrate relationship resources available in https://omnipathdb.org/enzsub
enzsub_resources(dataset = NULL)enzsub_resources(dataset = NULL)
dataset |
ignored for this query type |
character vector with the names of the enzyme-substrate resources
enzsub_resources()enzsub_resources()
Imports the enzyme-substrate (more exactly, enzyme-PTM) relationship database from https://omnipathdb.org/enzsub. These are mostly kinase-substrate relationships, with some acetylation and other types of PTMs.
enzyme_substrate(...)enzyme_substrate(...)
... |
Arguments passed on to
|
A data frame of enzymes and their PTM substrates.
enzsub <- enzyme_substrate( resources = c("PhosphoSite", "SIGNOR"), organism = 9606 )enzsub <- enzyme_substrate( resources = c("PhosphoSite", "SIGNOR"), organism = 9606 )
Downloads interactions from EVEX, a versatile text mining resource (http://evexdb.org). Translates the Entrez Gene IDs to Gene Symbols and combines the interactions and references into a single data frame.
evex_download( min_confidence = NULL, remove_negatives = TRUE, top_confidence = NULL )evex_download( min_confidence = NULL, remove_negatives = TRUE, top_confidence = NULL )
min_confidence |
Numeric: a threshold for confidence scores. EVEX confidence scores span roughly from -3 to 3. By providing a numeric value in this range the lower confidence interactions can be removed. If NULL no filtering performed. |
remove_negatives |
Logical: remove the records with the "negation" attribute set. |
top_confidence |
Confidence cutoff as quantile (a number between 0 and 1). If NULL no filtering performed. |
Data frame (tibble) with interactions.
evex_interactions <- evex_download() evex_interactions # # A tibble: 368,297 x 13 # general_event_id source_entrezge. target_entrezge. confidence negation # <dbl> <chr> <chr> <dbl> <dbl> # 1 98 8651 6774 -1.45 0 # 2 100 8431 6774 -1.45 0 # 3 205 6261 6263 0.370 0 # 4 435 1044 1045 -1.09 0 # . with 368,287 more rows, and 8 more variables: speculation <dbl>, # coarse_type <chr>, coarse_polarity <chr>, refined_type <chr>, # refined_polarity <chr>, source_genesymbol <chr>, # target_genesymbol <chr>, references <chr>evex_interactions <- evex_download() evex_interactions # # A tibble: 368,297 x 13 # general_event_id source_entrezge. target_entrezge. confidence negation # <dbl> <chr> <chr> <dbl> <dbl> # 1 98 8651 6774 -1.45 0 # 2 100 8431 6774 -1.45 0 # 3 205 6261 6263 0.370 0 # 4 435 1044 1045 -1.09 0 # . with 368,287 more rows, and 8 more variables: speculation <dbl>, # coarse_type <chr>, coarse_polarity <chr>, refined_type <chr>, # refined_polarity <chr>, source_genesymbol <chr>, # target_genesymbol <chr>, references <chr>
Show evidences for an interaction
evidences( partner_a, partner_b, interactions = NULL, directed = FALSE, open = TRUE, browser = NULL, max_pages = 25L )evidences( partner_a, partner_b, interactions = NULL, directed = FALSE, open = TRUE, browser = NULL, max_pages = 25L )
partner_a |
Identifier or name of one interacting partner. The order of the partners matter only if 'directed' is 'TRUE'. For both partners, vectors of more than one identifiers can be passed. |
partner_b |
Identifier or name of the other interacting partner. |
interactions |
An interaction data frame. If not provided, all
interactions will be loaded within this function, but that takes
noticeable time. If a 'list' is provided, it will be used as
parameters for |
directed |
Logical: does the direction matter? If 'TRUE', only a → b interactions will be shown. |
open |
Logical: open online articles in a web browser. |
browser |
Character: override the web browser executable used to open online articles. |
max_pages |
Numeric: largest number of pages to open. This is to prevent opening hundreds or thousands of pages at once. |
If the number of references is larger than 'max_pages', the most recent ones will be opened. URLs are passed to the browser in order of decreasing publication date, though browsers do not seem to respect the order at all. In addition Firefox, if it's not open already, tends to randomly open empty tab for the first or last URL, have no idea what to do about it.
Nothing.
## Not run: evidences('CALM1', 'TRPC1', list(datasets = 'omnipath')) ## End(Not run)## Not run: evidences('CALM1', 'TRPC1', list(datasets = 'omnipath')) ## End(Not run)
Extracts all unique values of an extra attribute occuring in this data frame.
extra_attr_values(data, key)extra_attr_values(data, key)
data |
An interaction data frame with extra_attrs column. |
key |
The name of an extra attribute. |
Note, at the end we unlist the result, which means it works well for attributes which are atomic vectors but gives not so useful result if the attribute values are more complex objects. As the time of writing this, no such complex extra attribute exist in OmniPath.
A vector, most likely character, with the unique values of the extra attribute occuring in the data frame.
op <- omnipath(fields = "extra_attrs") extra_attr_values(op, SIGNOR_mechanism)op <- omnipath(fields = "extra_attrs") extra_attr_values(op, SIGNOR_mechanism)
Interaction data frames might have an 'extra_attrs' column if this field has been requested in the query by passing the ‘fields = ’extra_attrs' argument. This column contains resource specific attributes for the interactions. The names of the attributes consist of the name of the resource and the name of the attribute, separated by an underscore. This function returns the names of the extra attributes available in the provided data frame.
extra_attrs(data)extra_attrs(data)
data |
An interaction data frame, as provided by any of the
|
Character: the names of the extra attributes in the data frame.
i <- omnipath(fields = "extra_attrs") extra_attrs(i)i <- omnipath(fields = "extra_attrs") extra_attrs(i)
New columns from extra attributes
extra_attrs_to_cols(data, ..., flatten = FALSE, keep_empty = TRUE)extra_attrs_to_cols(data, ..., flatten = FALSE, keep_empty = TRUE)
data |
An interaction data frame. |
... |
The names of the extra attributes; NSE is supported. Custom column names can be provided as argument names. |
flatten |
Logical: unnest the list column even if some records have multiple values for the attributes; these will yield multiple records in the resulted data frame. |
keep_empty |
Logical: if 'flatten' is 'TRUE', shall we keep the records which do not have the attribute? |
Data frame with the new column created; the new column is list type if one interaction might have multiple values of the attribute, or character type if
i <- omnipath(fields = "extra_attrs") extra_attrs_to_cols(i, Cellinker_type, Macrophage_type) extra_attrs_to_cols( i, Cellinker_type, Macrophage_type, flatten = TRUE, keep_empty = FALSE )i <- omnipath(fields = "extra_attrs") extra_attrs_to_cols(i, Cellinker_type, Macrophage_type) extra_attrs_to_cols( i, Cellinker_type, Macrophage_type, flatten = TRUE, keep_empty = FALSE )
Keeps only those records which are supported by any of the resources of interest.
filter_by_resource(data, resources = NULL)filter_by_resource(data, resources = NULL)
data |
A data frame downloaded from the OmniPath web service (interactions, enzyme-substrate or complexes). |
resources |
Character vector with resource names to keep. |
The data frame filtered.
interactions <- omnipath() signor <- filter_by_resource(interactions, resources = "SIGNOR")interactions <- omnipath() signor <- filter_by_resource(interactions, resources = "SIGNOR")
Filter evidences by dataset, resource and license
filter_evidences(data, ..., datasets = NULL, resources = NULL, exclude = NULL)filter_evidences(data, ..., datasets = NULL, resources = NULL, exclude = NULL)
data |
An interaction data frame with some columns containing evidences as nested lists. |
... |
The evidences columns to filter: tidyselect syntax is supported. By default the columns "evidences", "positive", "negative", "directed" and "undirected" are filtered, if present. |
datasets |
A character vector of dataset names. |
resources |
A character vector of resource names. |
exclude |
Character vector of resource names to be excluded. |
The input data frame with the evidences in the selected columns filtered.
Filter interactions by extra attribute values
filter_extra_attrs(data, ..., na_ok = TRUE)filter_extra_attrs(data, ..., na_ok = TRUE)
data |
An interaction data frame with extra_attrs column. |
... |
Extra attribute names and values. The contents of the extra attribute name for each record will be checked against the values provided. The check by default is a set intersection: if any element is common between the user provided values and the values of the extra attribute for the record, the record will be matched. Alternatively, any value can be a custom function which accepts the value of the extra attribute and returns a single logical value. Finally, if the extra attribute name starts with a dot, the result of the check will be negated. |
na_ok |
Logical: keep the records which do not have the extra attribute. Typically these are the records which are not from the resource providing the extra attribute. |
The input data frame with records removed according to the filtering criteria.
cl <- post_translational( resources = "Cellinker", fields = "extra_attrs" ) # Only cell adhesion interactions from Cellinker filter_extra_attrs(cl, Cellinker_type = "Cell adhesion") op <- omnipath(fields = "extra_attrs") # Any mechanism except phosphorylation filter_extra_attrs(op, .SIGNOR_mechanism = "phosphorylation")cl <- post_translational( resources = "Cellinker", fields = "extra_attrs" ) # Only cell adhesion interactions from Cellinker filter_extra_attrs(cl, Cellinker_type = "Cell adhesion") op <- omnipath(fields = "extra_attrs") # Any mechanism except phosphorylation filter_extra_attrs(op, .SIGNOR_mechanism = "phosphorylation")
Filters a data frame retrieved by intercell.
filter_intercell( data, categories = NULL, resources = NULL, parent = NULL, scope = NULL, aspect = NULL, source = NULL, transmitter = NULL, receiver = NULL, secreted = NULL, plasma_membrane_peripheral = NULL, plasma_membrane_transmembrane = NULL, proteins = NULL, causality = NULL, topology = NULL, ... )filter_intercell( data, categories = NULL, resources = NULL, parent = NULL, scope = NULL, aspect = NULL, source = NULL, transmitter = NULL, receiver = NULL, secreted = NULL, plasma_membrane_peripheral = NULL, plasma_membrane_transmembrane = NULL, proteins = NULL, causality = NULL, topology = NULL, ... )
data |
An intercell annotation data frame as provided by
|
categories |
Character: allow only these values in the |
resources |
Character: allow records only from these resources. |
parent |
Character: filter for records with these parent categories. |
scope |
Character: filter for records with these annotation scopes.
Possible values are |
aspect |
Character: filter for records with these annotation aspects.
Possible values are |
source |
Character: filter for records with these annotation sources.
Possible values are |
transmitter |
Logical: if |
receiver |
Logical: works the same way as |
secreted |
Logical: works the same way as |
plasma_membrane_peripheral |
Logical: works the same way as
|
plasma_membrane_transmembrane |
Logical: works the same way as
|
proteins |
Character: filter for annotations of these proteins. Gene symbols or UniProt IDs can be used. |
causality |
Character: filter for records with these causal roles.
Possible values are |
topology |
Character: filter for records with these localization
topologies. Possible values are |
... |
Ignored. |
The intercell annotation data frame filtered according to the specified conditions.
ic <- intercell() ic <- filter_intercell( ic, transmitter = TRUE, secreted = TRUE, scope = "specific" )ic <- intercell() ic <- filter_intercell( ic, transmitter = TRUE, secreted = TRUE, scope = "specific" )
The intercell database of OmniPath covers a very broad range of possible
ways of cell to cell communication, and the pieces of information, such as
localization, topology, function and interaction, are combined from many,
often independent sources. This unavoidably result some weird and
unexpected combinations which are false positives in the context of
intercellular communication. intercell_network
provides a shortcut (high_confidence) to do basic quality filtering.
For custom filtering or experimentation with the parameters we offer this
function.
filter_intercell_network( network, transmitter_topology = c("secreted", "plasma_membrane_transmembrane", "plasma_membrane_peripheral"), receiver_topology = "plasma_membrane_transmembrane", min_curation_effort = 2, min_resources = 1, min_references = 0, min_provenances = 1, consensus_percentile = 50, loc_consensus_percentile = 30, ligand_receptor = FALSE, simplify = FALSE, unique_pairs = FALSE, omnipath = TRUE, ligrecextra = TRUE, kinaseextra = FALSE, pathwayextra = FALSE, ... )filter_intercell_network( network, transmitter_topology = c("secreted", "plasma_membrane_transmembrane", "plasma_membrane_peripheral"), receiver_topology = "plasma_membrane_transmembrane", min_curation_effort = 2, min_resources = 1, min_references = 0, min_provenances = 1, consensus_percentile = 50, loc_consensus_percentile = 30, ligand_receptor = FALSE, simplify = FALSE, unique_pairs = FALSE, omnipath = TRUE, ligrecextra = TRUE, kinaseextra = FALSE, pathwayextra = FALSE, ... )
network |
An intercell network data frame, as provided by
|
transmitter_topology |
Character vector: topologies allowed for the entities in transmitter role. Abbreviations allowed: "sec", "pmtm" and "pmp". |
receiver_topology |
Same as |
min_curation_effort |
Numeric: a minimum value of curation effort (resource-reference pairs) for network interactions. Use zero to disable filtering. |
min_resources |
Numeric: minimum number of resources for interactions. The value 1 means no filtering. |
min_references |
Numeric: minimum number of references for interactions. Use zero to disable filtering. |
min_provenances |
Numeric: minimum number of provenances (either resources or references) for interactions. Use zero or one to disable filtering. |
consensus_percentile |
Numeric: percentile threshold for the consensus
score of generic categories in intercell annotations. The consensus
score is the number of resources supporting the classification of an
entity into a category based on combined information of many resources.
Here you can apply a cut-off, keeping only the annotations supported
by a higher number of resources than a certain percentile of each
category. If |
loc_consensus_percentile |
Numeric: similar to
|
ligand_receptor |
Logical. If |
simplify |
Logical: keep only the most often used columns. This function combines a network data frame with two copies of the intercell annotation data frames, all of them already having quite some columns. With this option we keep only the names of the interacting pair, their intercellular communication roles, and the minimal information of the origin of both the interaction and the annotations. |
unique_pairs |
Logical: instead of having separate rows for each
pair of annotations, drop the annotations and reduce the data frame to
unique interacting pairs. See |
omnipath |
Logical: shortcut to include the omnipath dataset in the interactions query. |
ligrecextra |
Logical: shortcut to include the ligrecextra dataset in the interactions query. |
kinaseextra |
Logical: shortcut to include the kinaseextra dataset in the interactions query. |
pathwayextra |
Logical: shortcut to include the pathwayextra dataset in the interactions query. |
... |
If |
An intercell network data frame filtered.
icn <- intercell_network() icn_f <- filter_intercell_network( icn, consensus_percentile = 75, min_provenances = 3, simplify = TRUE )icn <- intercell_network() icn_f <- filter_intercell_network( icn, consensus_percentile = 75, min_provenances = 3, simplify = TRUE )
Finds all paths up to length 'maxlen' between specified groups of vertices. This function is needed only becaues igraph's 'all_shortest_paths' finds only the shortest, not any path up to a defined length.
find_all_paths( graph, start, end, attr = NULL, mode = 'OUT', maxlen = 2, progress = TRUE )find_all_paths( graph, start, end, attr = NULL, mode = 'OUT', maxlen = 2, progress = TRUE )
graph |
An igraph graph object. |
start |
Integer or character vector with the indices or names of one or more start vertices. |
end |
Integer or character vector with the indices or names of one or more end vertices. |
attr |
Character: name of the vertex attribute to identify the vertices by. Necessary if 'start' and 'end' are not igraph vertex ids but for example vertex names or labels. |
mode |
Character: IN, OUT or ALL. Default is OUT. |
maxlen |
Integer: maximum length of paths in steps, i.e. if maxlen = 3, then the longest path may consist of 3 edges and 4 nodes. |
progress |
Logical: show a progress bar. |
List of vertex paths, each path is a character or integer vector.
interactions <- import_omnipath_interactions() graph <- interaction_graph(interactions) paths <- find_all_paths( graph = graph, start = c('EGFR', 'STAT3'), end = c('AKT1', 'ULK1'), attr = 'name' )interactions <- import_omnipath_interactions() graph <- interaction_graph(interactions) paths <- find_all_paths( graph = graph, start = c('EGFR', 'STAT3'), end = c('AKT1', 'ULK1'), attr = 'name' )
Recreate interaction records from evidences columns
from_evidences(data, .keep = FALSE)from_evidences(data, .keep = FALSE)
data |
An interaction data frame from the OmniPath web service with evidences column. |
.keep |
Logical: keep the original "evidences" column when unnesting to separate columns by direction. |
The OmniPath interaction data frames specify interactions primarily by three columns: "is_directed", "is_stimulation" and "is_inhibition". Besides these, there are the "sources" and "references" columns that are always included in data frames created by OmnipathR and list the resources and literature references for each interaction, respectively. The optional "evidences" column is required to find out which of the resources and references support the direction or effect sign of the interaction. To properly recover information for arbitrary subsets of resources or datasets, the evidences can be filtered first, and then the standard data frame columns can be reconstructed from the selected evidences. This function is able to do the latter. It expects either an "evidences" column or evidences in their wide format 4 columns layout. It overwrites the standard columns of interaction records based on data extracted from the evidences, including the "curation_effort" and "consensus..." columns.
Note: The "curation_effort" might be calculated slightly differently from the version included in the OmniPath web service. Here we count the resources and the also add the number of references for each resource. E.g. a resource without any literatur reference counts as 1, while a resource with 3 references adds 4 to the value of the curation effort.
Note: If the "evidences" column has been already unnested to
multiple columns ("positive", "negative", etc.) by
unnest_evidences, then these will be used;
otherwise, the column will be unnested within this function.
Note: This function (or rather its wrapper,
only_from) is automatically applied if the 'strict_evidences'
argument is passed to any function querying interactions (see
omnipath-interactions).
A copy of the input data frame with all the standard columns describing the direction, effect, resources and references of the interactions recreated based on the contents of the nested list evidences column(s).
## Not run: ci <- collectri(evidences = TRUE) ci <- unnest_evidences(ci) ci <- filter_evidences(datasets = 'collectri') ci <- from_evidences(ci) # the three lines above are equivalent to only_from(ci) # and all the four lines above is equivalent to: # collectri(strict_evidences = TRUE) ## End(Not run)## Not run: ci <- collectri(evidences = TRUE) ci <- unnest_evidences(ci) ci <- filter_evidences(datasets = 'collectri') ci <- from_evidences(ci) # the three lines above are equivalent to only_from(ci) # and all the four lines above is equivalent to: # collectri(strict_evidences = TRUE) ## End(Not run)
Databases are resources which might be costly to load but can be used many times by functions which usually automatically load and retrieve them from the database manager. Each database has a lifetime and will be unloaded automatically upon expiry.
get_db(key, param = NULL, reload = FALSE, ...)get_db(key, param = NULL, reload = FALSE, ...)
key |
Character: the key of the database to load. For a list of
available keys see |
param |
List: override the defaults or pass further parameters to
the database loader function. See the loader functions and their
default parameters in |
reload |
Reload the database if |
... |
Arguments for the loader function of the database. These override the default arguments. |
An object with the database contents. The exact format depends on the database, most often it is a data frame or a list.
organisms <- get_db('organisms')organisms <- get_db('organisms')
Retrieves an ontology database with relations in the desired data
structure. The database is automatically loaded and the requested data
structure is constructed if necessary. The databases stay loaded up to a
certain time period (see the option omnipathr.db_lifetime). Hence
the first one of repeated calls to this function might take long and the
subsequent ones should be really quick.
get_ontology_db(key, rel_fmt = "tbl", child_parents = TRUE)get_ontology_db(key, rel_fmt = "tbl", child_parents = TRUE)
key |
Character: key of the ontology database. For the available keys
see |
rel_fmt |
Character: the data structure of the ontology relations. Posible values are 1) "tbl" a data frame, 2) "lst" a list or 3) "gra" a graph. |
child_parents |
Logical: whether the ontology relations should point
from child to parents ( |
A list with the following elements: 1) "names" a table with term IDs and names; 2) "namespaces" a table to connect term IDs and namespaces they belong to; 3) "relations" a table with relations between terms and their parent terms; 4) "subsets" a table with terms and the subsets they are part of; 5) "obsolete" character vector with all the terms labeled as obsolete.
go <- get_ontology_db('go_basic', child_parents = FALSE)go <- get_ontology_db('go_basic', child_parents = FALSE)
For an igraph graph object returns its giant component.
giant_component(graph)giant_component(graph)
graph |
An igraph graph object. |
An igraph graph object containing only the giant component.
interactions <- import_post_translational_interactions() graph <- interaction_graph(interactions) graph_gc <- giant_component(graph)interactions <- import_post_translational_interactions() graph <- interaction_graph(interactions) graph_gc <- giant_component(graph)
Gene Ontology is an ontology of gene subcellular localizations, molecular functions and involvement in biological processes. Gene products across many organisms are annotated with the ontology terms. This function downloads the gene-ontology term associations for certain model organisms or all organisms. For a description of the columns see http://geneontology.org/docs/go-annotation-file-gaf-format-2.2/.
go_annot_download(organism = "human", aspects = c("C", "F", "P"), slim = NULL)go_annot_download(organism = "human", aspects = c("C", "F", "P"), slim = NULL)
organism |
Character: either "chicken", "cow", "dog", "human", "pig" or "uniprot_all". |
aspects |
Character vector with some of the following elements: "C" (cellular component), "F" (molecular function) and "P" (biological process). Gene Ontology is three separate ontologies called as three aspects. By this parameter you can control which aspects to include in the output. |
slim |
Character: if not |
A tibble (data frame) of annotations as it is provided by the database
goa_data <- go_annot_download() goa_data # # A tibble: 606,840 x 17 # db db_object_id db_object_symbol qualifier go_id db_ref # <fct> <chr> <chr> <fct> <chr> <chr> # 1 UniProt. A0A024RBG1 NUDT4B NA GO:000. GO_REF:00. # 2 UniProt. A0A024RBG1 NUDT4B NA GO:000. GO_REF:00. # 3 UniProt. A0A024RBG1 NUDT4B NA GO:004. GO_REF:00. # 4 UniProt. A0A024RBG1 NUDT4B NA GO:005. GO_REF:00. # 5 UniProt. A0A024RBG1 NUDT4B NA GO:005. GO_REF:00. # # . with 606,830 more rows, and 11 more variables: # # evidence_code <fct>, with_or_from <chr>, aspect <fct>, # # db_object_name <chr>, db_object_synonym <chr>, # # db_object_type <fct>, taxon <fct>, date <date>, # # assigned_by <fct>, annotation_extension <chr>, # # gene_product_from_id <chr>goa_data <- go_annot_download() goa_data # # A tibble: 606,840 x 17 # db db_object_id db_object_symbol qualifier go_id db_ref # <fct> <chr> <chr> <fct> <chr> <chr> # 1 UniProt. A0A024RBG1 NUDT4B NA GO:000. GO_REF:00. # 2 UniProt. A0A024RBG1 NUDT4B NA GO:000. GO_REF:00. # 3 UniProt. A0A024RBG1 NUDT4B NA GO:004. GO_REF:00. # 4 UniProt. A0A024RBG1 NUDT4B NA GO:005. GO_REF:00. # 5 UniProt. A0A024RBG1 NUDT4B NA GO:005. GO_REF:00. # # . with 606,830 more rows, and 11 more variables: # # evidence_code <fct>, with_or_from <chr>, aspect <fct>, # # db_object_name <chr>, db_object_synonym <chr>, # # db_object_type <fct>, taxon <fct>, date <date>, # # assigned_by <fct>, annotation_extension <chr>, # # gene_product_from_id <chr>
GO slims are subsets of the full GO which "give a broad overview of the ontology content without the detail of the specific fine grained terms". In order to annotate genes with GO slim terms, we take the annotations and search all ancestors of the terms up to the root of the ontology tree. From the ancestors we select the terms which are part of the slim subset.
go_annot_slim( organism = "human", slim = "generic", aspects = c("C", "F", "P"), cache = TRUE )go_annot_slim( organism = "human", slim = "generic", aspects = c("C", "F", "P"), cache = TRUE )
organism |
Character: either "chicken", "cow", "dog", "human", "pig" or "uniprot_all". |
slim |
Character: the GO subset (GO slim) name. Available GO slims are: "agr" (Alliance for Genomics Resources), "generic", "aspergillus", "candida", "drosophila", "chembl", "metagenomic", "mouse", "plant", "pir" (Protein Information Resource), "pombe" and "yeast". |
aspects |
Character vector with some of the following elements: "C" (cellular component), "F" (molecular function) and "P" (biological process). Gene Ontology is three separate ontologies called as three aspects. By this parameter you can control which aspects to include in the output. |
cache |
Logical: Load the result from cache if available. |
Building the GO slim is resource intensive in its current implementation.
For human annotation and generic GO slim it might take around 20 minutes.
The result is saved into the cache so next time loading the data from
there is really quick. If the cache option is FALSE the
data will be built fresh (the annotation and ontology files still might
come from cache), and the newly build GO slim will overwrite the cache
instance.
A tibble (data frame) of genes annotated with ontology terms in in the GO slim (subset).
## Not run: goslim <- go_annot_slim(organism = 'human', slim = 'generic') goslim # # A tibble: 276,371 x 8 # db db_object_id db_object_symbol go_id aspect db_object_name # <fct> <chr> <chr> <chr> <fct> <chr> # 1 UniPr. A0A024RBG1 NUDT4B GO:0. F Diphosphoinosito. # 2 UniPr. A0A024RBG1 NUDT4B GO:0. F Diphosphoinosito. # 3 UniPr. A0A024RBG1 NUDT4B GO:0. C Diphosphoinosito. # 4 UniPr. A0A024RBG1 NUDT4B GO:0. C Diphosphoinosito. # 5 UniPr. A0A024RBG1 NUDT4B GO:0. C Diphosphoinosito. # # . with 276,366 more rows, and 2 more variables: # # db_object_synonym <chr>, db_object_type <fct> ## End(Not run)## Not run: goslim <- go_annot_slim(organism = 'human', slim = 'generic') goslim # # A tibble: 276,371 x 8 # db db_object_id db_object_symbol go_id aspect db_object_name # <fct> <chr> <chr> <chr> <fct> <chr> # 1 UniPr. A0A024RBG1 NUDT4B GO:0. F Diphosphoinosito. # 2 UniPr. A0A024RBG1 NUDT4B GO:0. F Diphosphoinosito. # 3 UniPr. A0A024RBG1 NUDT4B GO:0. C Diphosphoinosito. # 4 UniPr. A0A024RBG1 NUDT4B GO:0. C Diphosphoinosito. # 5 UniPr. A0A024RBG1 NUDT4B GO:0. C Diphosphoinosito. # # . with 276,366 more rows, and 2 more variables: # # db_object_synonym <chr>, db_object_type <fct> ## End(Not run)
The Gene Ontology tree
go_ontology_download( basic = TRUE, tables = TRUE, subset = NULL, relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates", "negatively_regulates") )go_ontology_download( basic = TRUE, tables = TRUE, subset = NULL, relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates", "negatively_regulates") )
basic |
Logical: use the basic or the full version of GO. As written on the GO home page: "the basic version of the GO is filtered such that the graph is guaranteed to be acyclic and annotations can be propagated up the graph. The relations included are is a, part of, regulates, negatively regulates and positively regulates. This version excludes relationships that cross the 3 GO hierarchies. This version should be used with most GO-based annotation tools." |
tables |
In the result return data frames or nested lists. These later can be converted to each other if necessary. However converting from table to list is faster. |
subset |
Character: the GO subset (GO slim) name. GO slims are
subsets of the full GO which "give a broad overview of the ontology
content without the detail of the specific fine grained terms". This
option, if not |
relations |
Character vector: the relations to include in the processed data. |
A list with the following elements: 1) "names" a list with
terms as names and names as values; 2) "namespaces" a list with
terms as names and namespaces as values; 3) "relations" a list with
relations between terms: terms are keys, values are lists with
relations as names and character vectors of related terms as
values; 4) "subsets" a list with terms as keys and character
vectors of subset names as values (or NULL if the term
does not belong to any subset); 5) "obsolete" character vector
with all the terms labeled as obsolete. If the tables
parameter is TRUE, "names", "namespaces", "relations"
and "subsets" will be data frames (tibbles).
# retrieve the generic GO slim, a small subset of the full ontology go <- go_ontology_download(subset = 'generic')# retrieve the generic GO slim, a small subset of the full ontology go <- go_ontology_download(subset = 'generic')
Convert an igraph graph object to interaction data frame. This is the
reverse of the operation done by thje interaction_graph
function. Networks can be easily converted to igraph objects, then
you can make use of all igaph methods, and at the end, get back the
interactions in a data frame, along with all new edge and node attributes.
graph_interaction(graph, implode = FALSE)graph_interaction(graph, implode = FALSE)
graph |
An igraph graph object created formerly from an OmniPath interactions data frame. |
implode |
Logical: restore the original state of the list type columns by imploding them to character vectors, subitems separated by semicolons. |
An interaction data frame.
Downloads ligand-receptor interactions from the Guide to Pharmacology (IUPHAR/BPS) database (https://www.guidetopharmacology.org/).
guide2pharma_download()guide2pharma_download()
A tibble (data frame) of interactions as it is provided by the database
g2p_data <- guide2pharma_download() g2p_data # # A tibble: 21,586 x 38 # target target_id target_gene_sym. target_uniprot target_ensembl_. # <chr> <dbl> <chr> <chr> <chr> # 1 12S-L. 1387 ALOX12 P18054 ENSG00000108839 # 2 15-LO. 1388 ALOX15 P16050 ENSG00000161905 # 3 15-LO. 1388 ALOX15 P16050 ENSG00000161905 # 4 15-LO. 1388 ALOX15 P16050 ENSG00000161905 # # . with 21,576 more rows, and 33 more variables: target_ligand <chr>, # # target_ligand_id <chr>, target_ligand_gene_symbol <chr>, # ... (truncated)g2p_data <- guide2pharma_download() g2p_data # # A tibble: 21,586 x 38 # target target_id target_gene_sym. target_uniprot target_ensembl_. # <chr> <dbl> <chr> <chr> <chr> # 1 12S-L. 1387 ALOX12 P18054 ENSG00000108839 # 2 15-LO. 1388 ALOX15 P16050 ENSG00000161905 # 3 15-LO. 1388 ALOX15 P16050 ENSG00000161905 # 4 15-LO. 1388 ALOX15 P16050 ENSG00000161905 # # . with 21,576 more rows, and 33 more variables: target_ligand <chr>, # # target_ligand_id <chr>, target_ligand_gene_symbol <chr>, # ... (truncated)
Downloads a single network dataset from Harmonizome https://maayanlab.cloud/Harmonizome.
harmonizome_download(dataset)harmonizome_download(dataset)
dataset |
The dataset part of the URL. Please refer to the download section of the Harmonizome webpage. |
Data frame (tibble) with interactions.
harmonizome_data <- harmonizome_download('phosphositeplus') harmonizome_data # # A tibble: 6,013 x 7 # source source_desc source_id target target_desc target_id weight # <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl> # 1 TP53 na 7157 STK17A na 9263 1 # 2 TP53 na 7157 TP53RK na 112858 1 # 3 TP53 na 7157 SMG1 na 23049 1 # 4 UPF1 na 5976 SMG1 na 23049 1 # # . with 6,003 more rowsharmonizome_data <- harmonizome_download('phosphositeplus') harmonizome_data # # A tibble: 6,013 x 7 # source source_desc source_id target target_desc target_id weight # <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl> # 1 TP53 na 7157 STK17A na 9263 1 # 2 TP53 na 7157 TP53RK na 112858 1 # 3 TP53 na 7157 SMG1 na 23049 1 # 4 UPF1 na 5976 SMG1 na 23049 1 # # . with 6,003 more rows
Tells if an interaction data frame has an extra_attrs column
has_extra_attrs(data)has_extra_attrs(data)
data |
An interaction data frame. |
Logical: TRUE if the data frame has the "extra_attrs" column.
i <- omnipath(fields = "extra_attrs") has_extra_attrs(i)i <- omnipath(fields = "extra_attrs") has_extra_attrs(i)
Identifier translation table from HMDB
hmdb_id_mapping_table(to, from, entity_type = "metabolite")hmdb_id_mapping_table(to, from, entity_type = "metabolite")
to |
Character or symbol: target ID type. See Details for possible values. |
from |
Character or symbol: source ID type. See Details for possible values. |
entity_type |
Character: "gene" and "smol" are short symbols for proteins, genes and small molecules respectively. Several other synonyms are also accepted. |
The arguments to and from can be provided either
as character or as symbol (NSE). Their possible values are either HMDB XML
tag names or synonyms listed at id_types.
A data frame (tibble) with columns 'From' and 'To'.
hmdb_kegg <- hmdb_id_mapping_table("kegg", "hmdb") hmdb_kegghmdb_kegg <- hmdb_id_mapping_table("kegg", "hmdb") hmdb_kegg
HMDB identifier type label
hmdb_id_type(label)hmdb_id_type(label)
label |
Character: an ID type label, as shown in the table at
|
Character: the HMDB specific ID type label, or the input unchanged if it could not be translated (still might be a valid identifier name). These labels should be valid HMDB field names, as used in HMDB XML files.
hmdb_id_type("hmdb") # [1] "accession"hmdb_id_type("hmdb") # [1] "accession"
Field names for the HMDB metabolite dataset
hmdb_metabolite_fields()hmdb_metabolite_fields()
Character vector of field names.
hmdb_metabolite_fields()hmdb_metabolite_fields()
Field names for the HMDB proteins dataset
hmdb_protein_fields()hmdb_protein_fields()
Character vector of field names.
hmdb_protein_fields()hmdb_protein_fields()
Download a HMDB XML file and process it into a table
hmdb_table(dataset = "metabolites", fields = NULL)hmdb_table(dataset = "metabolites", fields = NULL)
dataset |
Character: name of an HMDB XML dataset, such as "metabolites", "proteins", "urine", "serum", "csf", "saliva", "feces", "sweat". |
fields |
Character: fields to extract from the XML. This is a very
minimal parser that is able to extract the text content of simple fields
and multiple value fields which contain a list of leaves within one
container tag under the record tag. A full list of fields available in
HMDB is available by the |
A data frame (tibble) with each column corresponding to a field.
hmdb_table()hmdb_table()
Orthologous pairs of genes for a pair of organisms from NCBI HomoloGene, using one identifier type.
homologene_download( target = 10090L, source = 9606L, id_type = "genesymbol", hgroup_size = FALSE )homologene_download( target = 10090L, source = 9606L, id_type = "genesymbol", hgroup_size = FALSE )
target |
Character or integer: name or ID of the target organism. |
source |
Character or integer: name or ID of the source organism. |
id_type |
Symbol or character: identifier type, possible values are "genesymbol", "entrez", "refseqp" or "gi". |
hgroup_size |
Logical: include a column with the size of the homology groups. This column distinguishes one-to-one and one-to-many or many-to-many mappings. |
The operation of this function is symmetric, *source* and *target* are interchangeable but determine the column layout of the output. The column "hgroup" is a numberic identifier of the homology groups. Most of the groups consist of one pair of orthologous genes (one-to-one mapping), and a few of them multiple ones (one-to-many or many-to-many mappings).
A data frame with orthologous identifiers between the two organisms.
chimp_human <- homologene_download(chimpanzee, human, refseqp) chimp_human # # A tibble: 17,737 × 3 # hgroup refseqp_source refseqp_target # <int> <chr> <chr> # 1 3 NP_000007.1 NP_001104286.1 # 2 5 NP_000009.1 XP_003315394.1 # 3 6 NP_000010.1 XP_508738.2 # 4 7 NP_001096.1 XP_001145316.1 # 5 9 NP_000014.1 XP_523792.2 # # . with 17,732 more rowschimp_human <- homologene_download(chimpanzee, human, refseqp) chimp_human # # A tibble: 17,737 × 3 # hgroup refseqp_source refseqp_target # <int> <chr> <chr> # 1 3 NP_000007.1 NP_001104286.1 # 2 5 NP_000009.1 XP_003315394.1 # 3 6 NP_000010.1 XP_508738.2 # 4 7 NP_001096.1 XP_001145316.1 # 5 9 NP_000014.1 XP_523792.2 # # . with 17,732 more rows
Organisms in NCBI HomoloGene
homologene_organisms(name_type = "ncbi")homologene_organisms(name_type = "ncbi")
name_type |
Character: type of the returned name or identifier. Many synonyms are accepted, the shortest ones: "latin", "ncbi", "common", "ensembl". Case unsensitive. |
Not all NCBI Taxonomy IDs can be translated to common or latin names. It means some organisms will be missing if translated to those name types. In the future we will address this issue, until then if you want to see all organisms use NCBI Taxonomy IDs.
A character vector of organism names.
Retrieves NCBI HomoloGene data without any processing. Processed tables are more useful for most purposes, see below other functions that provide those. Genes of various organisms are grouped into homology groups ("hgroup" column). Organisms are identified by NCBI Taxonomy IDs, genes are identified by four different identifier types.
homologene_raw()homologene_raw()
A data frame as provided by NCBI HomoloGene.
hg <- homologene_raw() hg # # A tibble: 275,237 × 6 # hgroup ncbi_taxid entrez genesymbol gi refseqp # <int> <int> <chr> <chr> <chr> <chr> # 1 3 9606 34 ACADM 4557231 NP_000007.1 # 2 3 9598 469356 ACADM 160961497 NP_001104286.1 # 3 3 9544 705168 ACADM 109008502 XP_001101274.1 # 4 3 9615 490207 ACADM 545503811 XP_005622188.1 # 5 3 9913 505968 ACADM 115497690 NP_001068703.1 # # . with 275,232 more rows # which organisms are available? common_name(unique(hg$ncbi_taxid)) # [1] "Human" "Chimpanzee" "Macaque" "Dog" "Cow" "Mouse" "Rat" "Zebrafish" # [9] "D. melanogaster" "Caenorhabditis elegans (PRJNA13758)" # [11] "Tropical clawed frog" "Chicken" # ...and 9 more organisms with missing English names.hg <- homologene_raw() hg # # A tibble: 275,237 × 6 # hgroup ncbi_taxid entrez genesymbol gi refseqp # <int> <int> <chr> <chr> <chr> <chr> # 1 3 9606 34 ACADM 4557231 NP_000007.1 # 2 3 9598 469356 ACADM 160961497 NP_001104286.1 # 3 3 9544 705168 ACADM 109008502 XP_001101274.1 # 4 3 9615 490207 ACADM 545503811 XP_005622188.1 # 5 3 9913 505968 ACADM 115497690 NP_001068703.1 # # . with 275,232 more rows # which organisms are available? common_name(unique(hg$ncbi_taxid)) # [1] "Human" "Chimpanzee" "Macaque" "Dog" "Cow" "Mouse" "Rat" "Zebrafish" # [9] "D. melanogaster" "Caenorhabditis elegans (PRJNA13758)" # [11] "Tropical clawed frog" "Chicken" # ...and 9 more organisms with missing English names.
Orthologous pairs of UniProt IDs for a pair of organisms, based on NCBI HomoloGene data.
homologene_uniprot_orthology(target = 10090L, source = 9606L, by = entrez, ...)homologene_uniprot_orthology(target = 10090L, source = 9606L, by = entrez, ...)
target |
Character or integer: name or ID of the target organism. |
source |
Character or integer: name or ID of the source organism. |
by |
Symbol or character: the identifier type in NCBI HomoloGene to use. Possible values are "refseqp", "entrez", "genesymbol", "gi". |
... |
Further arguments passed to |
A data frame with orthologous pairs of UniProt IDs.
homologene_uniprot_orthology(by = genesymbol) # # A tibble: 14,235 × 2 # source target # <chr> <chr> # 1 P11310 P45952 # 2 P49748 P50544 # 3 P24752 Q8QZT1 # 4 Q04771 P37172 # 5 Q16586 P82350 # # . with 14,230 more rowshomologene_uniprot_orthology(by = genesymbol) # # A tibble: 14,235 × 2 # source target # <chr> <chr> # 1 P11310 P45952 # 2 P49748 P50544 # 3 P24752 Q8QZT1 # 4 Q04771 P37172 # 5 Q16586 P82350 # # . with 14,230 more rows
Human Phenotype Ontology (HPO) provides a standardized vocabulary of phenotypic abnormalities encountered in human disease. Each term in the HPO describes a phenotypic abnormality. HPO currently contains over 13,000 terms and over 156,000 annotations to hereditary diseases. See more at https://hpo.jax.org/app/.
hpo_download()hpo_download()
A tibble (data frame) of annotations as it is provided by the database
hpo_data <- hpo_download() hpo_data # # A tibble: 231,738 x 9 # entrez_gene_id entrez_gene_symb. hpo_term_id hpo_term_name # <dbl> <chr> <chr> <chr> # 1 8192 CLPP HP:0000013 Hypoplasia of the ute. # 2 8192 CLPP HP:0004322 Short stature # 3 8192 CLPP HP:0000786 Primary amenorrhea # 4 8192 CLPP HP:0000007 Autosomal recessive i. # 5 8192 CLPP HP:0000815 Hypergonadotropic hyp. # # . with 231,733 more rows, and 5 more variables: # # frequency_raw <chr>, frequency_hpo <chr>, info_gd_source <chr>, # # gd_source <chr>, disease_id <chr>hpo_data <- hpo_download() hpo_data # # A tibble: 231,738 x 9 # entrez_gene_id entrez_gene_symb. hpo_term_id hpo_term_name # <dbl> <chr> <chr> <chr> # 1 8192 CLPP HP:0000013 Hypoplasia of the ute. # 2 8192 CLPP HP:0004322 Short stature # 3 8192 CLPP HP:0000786 Primary amenorrhea # 4 8192 CLPP HP:0000007 Autosomal recessive i. # 5 8192 CLPP HP:0000815 Hypergonadotropic hyp. # # . with 231,733 more rows, and 5 more variables: # # frequency_raw <chr>, frequency_hpo <chr>, info_gd_source <chr>, # # gd_source <chr>, disease_id <chr>
HTRIdb (https://www.lbbc.ibb.unesp.br/htri/) is a database of literature curated human TF-target interactions. As the database is recently offline, the data is distributed by the OmniPath rescued data repository (https://rescued.omnipathdb.org/).
htridb_download()htridb_download()
Data frame (tibble) with interactions.
htridb_data <- htridb_download() htridb_data # # A tibble: 18,630 x 7 # OID GENEID_TF SYMBOL_TF GENEID_TG SYMBOL_TG TECHNIQUE # <dbl> <dbl> <chr> <dbl> <chr> <chr> # 1 32399 142 PARP1 675 BRCA2 Electrophoretic Mobi. # 2 32399 142 PARP1 675 BRCA2 Chromatin Immunoprec. # 3 28907 196 AHR 1543 CYP1A1 Chromatin Immunoprec. # 4 29466 196 AHR 1543 CYP1A1 Electrophoretic Mobi. # 5 28911 196 AHR 1543 CYP1A1 Chromatin Immunoprec. # # . with 18,620 more rows, and 1 more variable: PUBMED_ID <chr>htridb_data <- htridb_download() htridb_data # # A tibble: 18,630 x 7 # OID GENEID_TF SYMBOL_TF GENEID_TG SYMBOL_TG TECHNIQUE # <dbl> <dbl> <chr> <dbl> <chr> <chr> # 1 32399 142 PARP1 675 BRCA2 Electrophoretic Mobi. # 2 32399 142 PARP1 675 BRCA2 Chromatin Immunoprec. # 3 28907 196 AHR 1543 CYP1A1 Chromatin Immunoprec. # 4 29466 196 AHR 1543 CYP1A1 Electrophoretic Mobi. # 5 28911 196 AHR 1543 CYP1A1 Chromatin Immunoprec. # # . with 18,620 more rows, and 1 more variable: PUBMED_ID <chr>
List available ID translation resources
id_translation_resources()id_translation_resources()
A character vector with the names of the available ID translation resources.
id_translation_resources()id_translation_resources()
ID types and synonyms in identifier translation
id_types()id_types()
Data frame with 4 columns: the ID type labels in the resource, their synonyms in OmniPath (this package), the name of the ID translation resource, and the entity type.
id_types()id_types()
Downloads the data by inbiomap_raw, extracts the
UniProt IDs, Gene Symbols and scores and removes the irrelevant columns.
inbiomap_download(...)inbiomap_download(...)
... |
Passed to |
A data frame (tibble) of interactions.
## Not run: inbiomap_interactions <- inbiomap_download() inbiomap_interactions ## End(Not run) # # A tibble: 625,641 x 7 # uniprot_a uniprot_b genesymbol_a genesymbol_b inferred score1 score2 # <chr> <chr> <chr> <chr> <lgl> <dbl> <dbl> # 1 A0A5B9 P01892 TRBC2 HLA-A FALSE 0.417 0.458 # 2 A0AUZ9 Q96CV9 KANSL1L OPTN FALSE 0.155 0.0761 # 3 A0AV02 P24941 SLC12A8 CDK2 TRUE 0.156 0.0783 # 4 A0AV02 Q00526 SLC12A8 CDK3 TRUE 0.157 0.0821 # 5 A0AV96 P0CG48 RBM47 UBC FALSE 0.144 0.0494 # # . with 625,631 more rows## Not run: inbiomap_interactions <- inbiomap_download() inbiomap_interactions ## End(Not run) # # A tibble: 625,641 x 7 # uniprot_a uniprot_b genesymbol_a genesymbol_b inferred score1 score2 # <chr> <chr> <chr> <chr> <lgl> <dbl> <dbl> # 1 A0A5B9 P01892 TRBC2 HLA-A FALSE 0.417 0.458 # 2 A0AUZ9 Q96CV9 KANSL1L OPTN FALSE 0.155 0.0761 # 3 A0AV02 P24941 SLC12A8 CDK2 TRUE 0.156 0.0783 # 4 A0AV02 Q00526 SLC12A8 CDK3 TRUE 0.157 0.0821 # 5 A0AV96 P0CG48 RBM47 UBC FALSE 0.144 0.0494 # # . with 625,631 more rows
Downloads the data from https://inbio-discover.com/map.html#downloads in tar.gz format, extracts the PSI MITAB table and returns it as a data frame.
inbiomap_raw(curl_verbose = FALSE)inbiomap_raw(curl_verbose = FALSE)
curl_verbose |
Logical. Perform CURL requests in verbose mode for debugging purposes. |
A data frame (tibble) with the extracted interaction table.
## Not run: inbiomap_psimitab <- inbiomap_raw() ## End(Not run)## Not run: inbiomap_psimitab <- inbiomap_raw() ## End(Not run)
Datasets in the OmniPath Interactions database
interaction_datasets()interaction_datasets()
Character: labels of interaction datasets.
interaction_datasets()interaction_datasets()
Transforms the interactions data frame to an igraph graph object.
interaction_graph(interactions = interactions)interaction_graph(interactions = interactions)
interactions |
data.frame created by |
An igraph graph object.
interactions <- import_omnipath_interactions(resources = c('SignaLink3')) g <- interaction_graph(interactions)interactions <- import_omnipath_interactions(resources = c('SignaLink3')) g <- interaction_graph(interactions)
Names of the resources available in https://omnipathdb.org/interactions.
interaction_resources(dataset = NULL)interaction_resources(dataset = NULL)
dataset |
a dataset within the interactions query type. Currently available datasets are 'omnipath', 'kinaseextra', 'pathwayextra', 'ligrecextra', 'collectri', 'dorothea', 'tf_target', 'tf_mirna', 'mirnatarget', 'lncrna_mrna' and 'small_molecule_protein'. |
Character: names of the interaction resources.
interaction_resources()interaction_resources()
Interaction types in the OmniPath Interactions database
interaction_types()interaction_types()
Character: labels of interaction types.
interaction_types()interaction_types()
Roles of proteins in intercellular communication from the https://omnipathdb.org/intercell endpoint of the OmniPath web service. It provides information on the roles in inter-cellular signaling. E.g. if a protein is a ligand, a receptor, an extracellular matrix (ECM) component, etc.
intercell( categories = NULL, parent = NULL, scope = NULL, aspect = NULL, source = NULL, transmitter = NULL, receiver = NULL, secreted = NULL, plasma_membrane_peripheral = NULL, plasma_membrane_transmembrane = NULL, proteins = NULL, topology = NULL, causality = NULL, consensus_percentile = NULL, loc_consensus_percentile = NULL, ... )intercell( categories = NULL, parent = NULL, scope = NULL, aspect = NULL, source = NULL, transmitter = NULL, receiver = NULL, secreted = NULL, plasma_membrane_peripheral = NULL, plasma_membrane_transmembrane = NULL, proteins = NULL, topology = NULL, causality = NULL, consensus_percentile = NULL, loc_consensus_percentile = NULL, ... )
categories |
vector containing the categories to be retrieved.
All the genes belonging to those categories will be returned. For
further information about the categories see
|
parent |
vector containing the parent classes to be retrieved.
All the genes belonging to those classes will be returned. For
furter information about the main classes see
|
scope |
either 'specific' or 'generic' |
aspect |
either 'locational' or 'functional' |
source |
either 'resource_specific' or 'composite' |
transmitter |
logical, include only transmitters i.e. proteins delivering signal from a cell to its environment. |
receiver |
logical, include only receivers i.e. proteins delivering signal to the cell from its environment. |
secreted |
logical, include only secreted proteins |
plasma_membrane_peripheral |
logical, include only plasma membrane peripheral membrane proteins. |
plasma_membrane_transmembrane |
logical, include only plasma membrane transmembrane proteins. |
proteins |
limit the query to certain proteins |
topology |
topology categories: one or more of 'secreted' (sec), 'plasma_membrane_peripheral' (pmp), 'plasma_membrane_transmembrane' (pmtm) (both short or long notation can be used). |
causality |
'transmitter' (trans), 'receiver' (rec) or 'both' (both short or long notation can be used). |
consensus_percentile |
Numeric: a percentile cut off for the
consensus score of generic categories. The consensus score is the
number of resources supporting the classification of an entity into a
category based on combined information of many resources. Here you can
apply a cut-off, keeping only the annotations supported by a higher
number of resources than a certain percentile of each category. If
|
loc_consensus_percentile |
Numeric: similar to
|
... |
Arguments passed on to
|
A data frame of intercellular communication roles.
ecm_proteins <- intercell(categories = "ecm")ecm_proteins <- intercell(categories = "ecm")
Retrieves a list of categories from https://omnipathdb.org/intercell.
intercell_categories()intercell_categories()
character vector with the different intercell categories
intercell_categories()intercell_categories()
Quality filter for intercell annotations
intercell_consensus_filter( data, percentile = NULL, loc_percentile = NULL, topology = NULL )intercell_consensus_filter( data, percentile = NULL, loc_percentile = NULL, topology = NULL )
data |
A data frame with intercell annotations, as provided by
|
percentile |
Numeric: a percentile cut off for the consensus score
of composite categories. The consensus score is the number of
resources supporting the classification of an entity into a category
based on combined information of many resources. Here you can apply
a cut-off, keeping only the annotations supported by a higher number
of resources than a certain percentile of each category. If
|
loc_percentile |
Numeric: similar to |
topology |
Character vector: list of allowed topologies, possible values are *"secreted"*, *"plasma_membrane_peripheral"* and *"plasma_membrane_transmembrane"*. |
The data frame in data filtered by the consensus scores.
ligand_receptor <- intercell(parent = c("ligand", "receptor")) nrow(ligand_receptor) # [1] 50174 lr_q50 <- intercell_consensus_filter(ligand_receptor, 50) nrow(lr_q50) # [1] 42863ligand_receptor <- intercell(parent = c("ligand", "receptor")) nrow(ligand_receptor) # [1] 50174 lr_q50 <- intercell_consensus_filter(ligand_receptor, 50) nrow(lr_q50) # [1] 42863
Retrieves a list of the generic categories from https://omnipathdb.org/intercell.
intercell_generic_categories()intercell_generic_categories()
character vector with the different intercell main classes
intercell_generic_categories()intercell_generic_categories()
Imports an intercellular network by combining intercellular annotations
and protein interactions. First imports a network of protein-protein
interactions. Then, it retrieves annotations about the proteins
intercellular communication roles, once for the transmitter (delivering
information from the expressing cell) and second, the receiver (receiving
signal and relaying it towards the expressing cell) side. These 3 queries
can be customized by providing parameters in lists which will be passed to
the respective methods (omnipath_interactions for
the network and intercell for the
annotations). Finally the 3 data frames combined in a way that the source
proteins in each interaction annotated by the transmitter, and the target
proteins by the receiver categories. If undirected interactions present
(these are disabled by default) they will be duplicated, i.e. both
partners can be both receiver and transmitter.
intercell_network( interactions_param = list(), transmitter_param = list(), receiver_param = list(), resources = NULL, entity_types = NULL, ligand_receptor = FALSE, high_confidence = FALSE, simplify = FALSE, unique_pairs = FALSE, consensus_percentile = NULL, loc_consensus_percentile = NULL, omnipath = TRUE, ligrecextra = TRUE, kinaseextra = !high_confidence, pathwayextra = !high_confidence, ... )intercell_network( interactions_param = list(), transmitter_param = list(), receiver_param = list(), resources = NULL, entity_types = NULL, ligand_receptor = FALSE, high_confidence = FALSE, simplify = FALSE, unique_pairs = FALSE, consensus_percentile = NULL, loc_consensus_percentile = NULL, omnipath = TRUE, ligrecextra = TRUE, kinaseextra = !high_confidence, pathwayextra = !high_confidence, ... )
interactions_param |
a list with arguments for an interactions query;
|
transmitter_param |
a list with arguments for
|
receiver_param |
a list with arguments for
|
resources |
A character vector of resources to be applied to
both the interactions and the annotations. For example, |
entity_types |
Character, possible values are "protein", "complex" or both. |
ligand_receptor |
Logical. If |
high_confidence |
Logical: shortcut to do some filtering in order to
include only higher confidence interactions. The intercell database
of OmniPath covers a very broad range of possible ways of cell to cell
communication, and the pieces of information, such as localization,
topology, function and interaction, are combined from many, often
independent sources. This unavoidably result some weird and unexpected
combinations which are false positives in the context of intercellular
communication. This option sets some minimum criteria to remove most
(but definitely not all!) of the wrong connections. These criteria
are the followings: 1) the receiver must be plasma membrane
transmembrane; 2) the curation effort for interactions must be larger
than one; 3) the consensus score for annotations must be larger than
the 50 percentile within the generic category (you can override this
by |
simplify |
Logical: keep only the most often used columns. This function combines a network data frame with two copies of the intercell annotation data frames, all of them already having quite some columns. With this option we keep only the names of the interacting pair, their intercellular communication roles, and the minimal information of the origin of both the interaction and the annotations. |
unique_pairs |
Logical: instead of having separate rows for each
pair of annotations, drop the annotations and reduce the data frame to
unique interacting pairs. See |
consensus_percentile |
Numeric: a percentile cut off for the consensus
score of generic categories in intercell annotations. The consensus
score is the number of resources supporting the classification of an
entity into a category based on combined information of many resources.
Here you can apply a cut-off, keeping only the annotations supported
by a higher number of resources than a certain percentile of each
category. If |
loc_consensus_percentile |
Numeric: similar to
|
omnipath |
Logical: shortcut to include the omnipath dataset in the interactions query. |
ligrecextra |
Logical: shortcut to include the ligrecextra dataset in the interactions query. |
kinaseextra |
Logical: shortcut to include the kinaseextra dataset in the interactions query. |
pathwayextra |
Logical: shortcut to include the pathwayextra dataset in the interactions query. |
... |
If |
By default this function creates almost the largest possible network of
intercellular interactions. However, this might contain a large number
of false positives. Please refer to the documentation of the arguments,
especially high_confidence, and the
filter_intercell_network function. Note: if you restrict the query
to certain intercell annotation resources or small categories, it's not
recommended to use the consensus_percentile or
high_confidence options, instead filter the network with
filter_intercell_network for more consistent results.
A dataframe containing information about protein-protein interactions and the inter-cellular roles of the protiens involved in those interactions.
intercell_network <- intercell_network( interactions_param = list(datasets = 'ligrecextra'), receiver_param = list(categories = c('receptor', 'transporter')), transmitter_param = list(categories = c('ligand', 'secreted_enzyme')) )intercell_network <- intercell_network( interactions_param = list(datasets = 'ligrecextra'), receiver_param = list(categories = c('receptor', 'transporter')), transmitter_param = list(categories = c('ligand', 'secreted_enzyme')) )
Retrieves a list of the databases from https://omnipathdb.org/intercell.
intercell_resources(dataset = NULL)intercell_resources(dataset = NULL)
dataset |
ignored at this query type |
character vector with the names of the databases
intercell_resources()intercell_resources()
Full list of intercell categories and resources
intercell_summary()intercell_summary()
A data frame of categories and resources.
ic_cat <- intercell_categories() ic_cat # # A tibble: 1,125 x 3 # category parent database # <chr> <chr> <chr> # 1 transmembrane transmembrane UniProt_location # 2 transmembrane transmembrane UniProt_topology # 3 transmembrane transmembrane UniProt_keyword # 4 transmembrane transmembrane_predicted Phobius # 5 transmembrane_phobius transmembrane_predicted Almen2009 # # . with 1,120 more rowsic_cat <- intercell_categories() ic_cat # # A tibble: 1,125 x 3 # category parent database # <chr> <chr> <chr> # 1 transmembrane transmembrane UniProt_location # 2 transmembrane transmembrane UniProt_topology # 3 transmembrane transmembrane UniProt_keyword # 4 transmembrane transmembrane_predicted Phobius # 5 transmembrane_phobius transmembrane_predicted Almen2009 # # . with 1,120 more rows
Tells if the input has the typical format of ontology IDs, i.e. a code of capital letters, a colon, followed by a numeric code.
is_ontology_id(terms)is_ontology_id(terms)
terms |
Character vector with strings to check. |
A logical vector with the same length as the input.
is_ontology_id(c('GO:0000001', 'reproduction')) # [1] TRUE FALSEis_ontology_id(c('GO:0000001', 'reproduction')) # [1] TRUE FALSE
Check for SwissProt IDs
is_swissprot(uniprots, organism = 9606)is_swissprot(uniprots, organism = 9606)
uniprots |
Character vector of UniProt IDs. |
organism |
Character or integer: name or identifier of the organism. |
Logical vector TRUE for SwissProt IDs and FALSE for any other element.
is_swissprot(c("Q05BL1", "A0A654IBU3", "P00533")) # [1] FALSE FALSE TRUEis_swissprot(c("Q05BL1", "A0A654IBU3", "P00533")) # [1] FALSE FALSE TRUE
Check for TrEMBL IDs
is_trembl(uniprots, organism = 9606)is_trembl(uniprots, organism = 9606)
uniprots |
Character vector of UniProt IDs. |
organism |
Character or integer: name or identifier of the organism. |
Logical vector TRUE for TrEMBL IDs and FALSE for any other element.
is_trembl(c("Q05BL1", "A0A654IBU3", "P00533")) # [1] TRUE TRUE FALSEis_trembl(c("Q05BL1", "A0A654IBU3", "P00533")) # [1] TRUE TRUE FALSE
This function checks only the format of the IDs, no guarantee that these IDs exist in UniProt.
is_uniprot(identifiers)is_uniprot(identifiers)
identifiers |
Character: one or more identifiers (typically a single string, a vector or a data frame column). |
Logical: true if all elements in the input (except NAs) looks like valid UniProt IDs. If the input is not a character vector, 'FALSE' is returned.
is_uniprot(all_uniprot_acs()) # [1] TRUE is_uniprot("P00533") # [1] TRUE is_uniprot("pizza") # [1] FALSEis_uniprot(all_uniprot_acs()) # [1] TRUE is_uniprot("P00533") # [1] TRUE is_uniprot("pizza") # [1] FALSE
Information about a KEGG Pathway
kegg_info(pathway_id)kegg_info(pathway_id)
pathway_id |
Character: a KEGG Pathway identifier, e.g. "hsa04710".
For a complete list of IDs see |
List with the pathway information.
kegg_info('map00563')kegg_info('map00563')
Open a KEGG Pathway diagram in the browser
kegg_open(pathway_id)kegg_open(pathway_id)
pathway_id |
Character: a KEGG Pathway identifier, e.g. "hsa04710".
For a complete list of IDs see |
To open URLs in the web browser the "browser" option must to be set to a
a valid executable. You can check the value of this option by
getOption("browser"). If your browser is firefox and the executable
is located in the system path, you can set the option to point to it:
options(browser = "firefox"). To make it a permanent setting, you
can also include this in your .Rprofile file.
Returns NULL.
if(any(getOption('browser') != '')) kegg_open('hsa04710')if(any(getOption('browser') != '')) kegg_open('hsa04710')
Downloads all KEGG pathways and creates a table of protein-pathway annotations.
kegg_pathway_annotations(pathways = NULL)kegg_pathway_annotations(pathways = NULL)
pathways |
A table of KEGG pathways as produced by |
A data frame (tibble) with UniProt IDs and pathway names.
## Not run: kegg_pw_annot <- kegg_pathway_annotations() kegg_pw_annot # # A tibble: 7,341 x 4 # uniprot genesymbol pathway pathway_id # <chr> <chr> <chr> <chr> # 1 Q03113 GNA12 MAPK signaling pathway hsa04010 # 2 Q9Y4G8 RAPGEF2 MAPK signaling pathway hsa04010 # 3 Q13972 RASGRF1 MAPK signaling pathway hsa04010 # 4 O95267 RASGRP1 MAPK signaling pathway hsa04010 # 5 P62834 RAP1A MAPK signaling pathway hsa04010 # # . with 7,336 more rows ## End(Not run)## Not run: kegg_pw_annot <- kegg_pathway_annotations() kegg_pw_annot # # A tibble: 7,341 x 4 # uniprot genesymbol pathway pathway_id # <chr> <chr> <chr> <chr> # 1 Q03113 GNA12 MAPK signaling pathway hsa04010 # 2 Q9Y4G8 RAPGEF2 MAPK signaling pathway hsa04010 # 3 Q13972 RASGRF1 MAPK signaling pathway hsa04010 # 4 O95267 RASGRP1 MAPK signaling pathway hsa04010 # 5 P62834 RAP1A MAPK signaling pathway hsa04010 # # . with 7,336 more rows ## End(Not run)
Downloads one pathway diagram from the KEGG Pathways database in KGML format and processes the XML to extract the interactions.
kegg_pathway_download( pathway_id, process = TRUE, max_expansion = NULL, simplify = FALSE )kegg_pathway_download( pathway_id, process = TRUE, max_expansion = NULL, simplify = FALSE )
pathway_id |
Character: a KEGG pathway identifier, for example "hsa04350". |
process |
Logical: process the data or return it in raw format. processing means joining the entries and relations into a single data frame and adding UniProt IDs. |
max_expansion |
Numeric: the maximum number of relations
derived from a single relation record. As one entry might represent
more than one molecular entities, one relation might yield a large
number of relations in the processing. This happens in a combinatorial
way, e.g. if the two entries represent 3 and 4 entities, that results
12 relations. If |
simplify |
Logical: remove KEGG's internal identifiers and the pathway annotations, keep only unique interactions with direction and effect sign. |
A data frame (tibble) of interactions if process is
TRUE, otherwise a list with two data frames: "entries" is
a raw table of the entries while "relations" is a table of relations
extracted from the KGML file.
tgf_pathway <- kegg_pathway_download('hsa04350') tgf_pathway # # A tibble: 50 x 12 # source target type effect arrow relation_id kegg_id_source # <chr> <chr> <chr> <chr> <chr> <chr> <chr> # 1 51 49 PPrel activ. --> hsa04350:1 hsa:7040 hsa:. # 2 57 55 PPrel activ. --> hsa04350:2 hsa:151449 hs. # 3 34 32 PPrel activ. --> hsa04350:3 hsa:3624 hsa:. # 4 20 17 PPrel activ. --> hsa04350:4 hsa:4838 # 5 60 46 PPrel activ. --> hsa04350:5 hsa:4086 hsa:. # # . with 45 more rows, and 5 more variables: genesymbol_source <chr>, # # uniprot_source <chr>, kegg_id_target <chr>, # # genesymbol_target <chr>, uniprot_target <chr>tgf_pathway <- kegg_pathway_download('hsa04350') tgf_pathway # # A tibble: 50 x 12 # source target type effect arrow relation_id kegg_id_source # <chr> <chr> <chr> <chr> <chr> <chr> <chr> # 1 51 49 PPrel activ. --> hsa04350:1 hsa:7040 hsa:. # 2 57 55 PPrel activ. --> hsa04350:2 hsa:151449 hs. # 3 34 32 PPrel activ. --> hsa04350:3 hsa:3624 hsa:. # 4 20 17 PPrel activ. --> hsa04350:4 hsa:4838 # 5 60 46 PPrel activ. --> hsa04350:5 hsa:4086 hsa:. # # . with 45 more rows, and 5 more variables: genesymbol_source <chr>, # # uniprot_source <chr>, kegg_id_target <chr>, # # genesymbol_target <chr>, uniprot_target <chr>
Retrieves a list of available KEGG pathways.
kegg_pathway_list()kegg_pathway_list()
Data frame of pathway names and identifiers.
kegg_pws <- kegg_pathway_list() kegg_pws # # A tibble: 521 x 2 # id name # <chr> <chr> # 1 map01100 Metabolic pathways # 2 map01110 Biosynthesis of secondary metabolites # 3 map01120 Microbial metabolism in diverse environments # 4 map01200 Carbon metabolism # 5 map01210 2-Oxocarboxylic acid metabolism # 6 map01212 Fatty acid metabolism # 7 map01230 Biosynthesis of amino acids # # . with 514 more rowskegg_pws <- kegg_pathway_list() kegg_pws # # A tibble: 521 x 2 # id name # <chr> <chr> # 1 map01100 Metabolic pathways # 2 map01110 Biosynthesis of secondary metabolites # 3 map01120 Microbial metabolism in diverse environments # 4 map01200 Carbon metabolism # 5 map01210 2-Oxocarboxylic acid metabolism # 6 map01212 Fatty acid metabolism # 7 map01230 Biosynthesis of amino acids # # . with 514 more rows
Downloads all pathway diagrams in the KEGG Pathways database in KGML format and processes the XML to extract the interactions.
kegg_pathways_download(max_expansion = NULL, simplify = FALSE)kegg_pathways_download(max_expansion = NULL, simplify = FALSE)
max_expansion |
Numeric: the maximum number of relations
derived from a single relation record. As one entry might represent
more than one molecular entities, one relation might yield a large
number of relations in the processing. This happens in a combinatorial
way, e.g. if the two entries represent 3 and 4 entities, that results
12 relations. If |
simplify |
Logical: remove KEGG's internal identifiers and the pathway annotations, keep only unique interactions with direction and effect sign. |
A data frame (tibble) of interactions.
## Not run: kegg_pw <- kegg_pathways_download(simplify = TRUE) kegg_pw # # A tibble: 6,765 x 6 # uniprot_source uniprot_target type effect genesymbol_source # <chr> <chr> <chr> <chr> <chr> # 1 Q03113 Q15283 PPrel activ. GNA12 # 2 Q9Y4G8 P62070 PPrel activ. RAPGEF2 # 3 Q13972 P62070 PPrel activ. RASGRF1 # 4 O95267 P62070 PPrel activ. RASGRP1 # 5 P62834 P15056 PPrel activ. RAP1A # # . with 6,760 more rows, and 1 more variable: genesymbol_target <chr> ## End(Not run)## Not run: kegg_pw <- kegg_pathways_download(simplify = TRUE) kegg_pw # # A tibble: 6,765 x 6 # uniprot_source uniprot_target type effect genesymbol_source # <chr> <chr> <chr> <chr> <chr> # 1 Q03113 Q15283 PPrel activ. GNA12 # 2 Q9Y4G8 P62070 PPrel activ. RAPGEF2 # 3 Q13972 P62070 PPrel activ. RASGRF1 # 4 O95267 P62070 PPrel activ. RASGRP1 # 5 P62834 P15056 PPrel activ. RAP1A # # . with 6,760 more rows, and 1 more variable: genesymbol_target <chr> ## End(Not run)
Downloads a KEGG Pathway diagram as a PNG image.
kegg_picture(pathway_id, path = NULL)kegg_picture(pathway_id, path = NULL)
pathway_id |
Character: a KEGG Pathway identifier, e.g. "hsa04710".
For a complete list of IDs see |
path |
Character: save the image to this path. If |
Invisibly returns the path to the downloaded file.
kegg_picture('hsa04710') kegg_picture('hsa04710', path = 'foo/bar') kegg_picture('hsa04710', path = 'foo/bar/circadian.png')kegg_picture('hsa04710') kegg_picture('hsa04710', path = 'foo/bar') kegg_picture('hsa04710', path = 'foo/bar/circadian.png')
Processes KEGG Pathways data extracted from a KGML file. Joins the entries and relations into a single data frame and translates the Gene Symbols to UniProt IDs.
kegg_process(entries, relations, max_expansion = NULL, simplify = FALSE)kegg_process(entries, relations, max_expansion = NULL, simplify = FALSE)
entries |
A data frames with entries extracted from a KGML
file by |
relations |
A data frames with relations extracted from a KGML
file by |
max_expansion |
Numeric: the maximum number of relations
derived from a single relation record. As one entry might represent
more than one molecular entities, one relation might yield a large
number of relations in the processing. This happens in a combinatorial
way, e.g. if the two entries represent 3 and 4 entities, that results
12 relations. If |
simplify |
Logical: remove KEGG's internal identifiers and the pathway annotations, keep only unique interactions with direction and effect sign. |
A data frame (tibble) of interactions. In rare cases when a
pathway doesn't contain any relation, returns NULL.
hsa04350 <- kegg_pathway_download('hsa04350', process = FALSE) tgf_pathway <- kegg_process(hsa04350$entries, hsa04350$relations) tgf_pathway # # A tibble: 50 x 12 # source target type effect arrow relation_id kegg_id_source # <chr> <chr> <chr> <chr> <chr> <chr> <chr> # 1 51 49 PPrel activ. --> hsa04350:1 hsa:7040 hsa:. # 2 57 55 PPrel activ. --> hsa04350:2 hsa:151449 hs. # 3 34 32 PPrel activ. --> hsa04350:3 hsa:3624 hsa:. # 4 20 17 PPrel activ. --> hsa04350:4 hsa:4838 # 5 60 46 PPrel activ. --> hsa04350:5 hsa:4086 hsa:. # # . with 45 more rows, and 5 more variables: genesymbol_source <chr>, # # uniprot_source <chr>, kegg_id_target <chr>, # # genesymbol_target <chr>, uniprot_target <chr>hsa04350 <- kegg_pathway_download('hsa04350', process = FALSE) tgf_pathway <- kegg_process(hsa04350$entries, hsa04350$relations) tgf_pathway # # A tibble: 50 x 12 # source target type effect arrow relation_id kegg_id_source # <chr> <chr> <chr> <chr> <chr> <chr> <chr> # 1 51 49 PPrel activ. --> hsa04350:1 hsa:7040 hsa:. # 2 57 55 PPrel activ. --> hsa04350:2 hsa:151449 hs. # 3 34 32 PPrel activ. --> hsa04350:3 hsa:3624 hsa:. # 4 20 17 PPrel activ. --> hsa04350:4 hsa:4838 # 5 60 46 PPrel activ. --> hsa04350:5 hsa:4086 hsa:. # # . with 45 more rows, and 5 more variables: genesymbol_source <chr>, # # uniprot_source <chr>, kegg_id_target <chr>, # # genesymbol_target <chr>, uniprot_target <chr>
Latin (scientific) names of organisms
latin_name(name)latin_name(name)
name |
Vector with any kind of organism name or identifier, can be also mixed type. |
Character vector with latin (scientific) names, NA if a name in the input could not be found.
latin_name(c(9606, "cat", "dog")) # [1] "Homo sapiens" "Felis catus" "Canis lupus familiaris" latin_name(c(9606, "cat", "doggy")) # [1] "Homo sapiens" "Felis catus" NAlatin_name(c(9606, "cat", "dog")) # [1] "Homo sapiens" "Felis catus" "Canis lupus familiaris" latin_name(c(9606, "cat", "doggy")) # [1] "Homo sapiens" "Felis catus" NA
Load a built in database
load_db(key, param = list())load_db(key, param = list())
key |
Character: the key of the database to load. For a list of
available keys see |
param |
List: override the defaults or pass further parameters to
the database loader function. See the loader functions and their
default parameters in |
This function loads a database which is stored within the package
namespace until its expiry. The loaded database is accessible by
get_db and the loading process is typically initiated by
get_db, not by the users directly.
Returns NULL.
load_db('go_slim') omnipath_show_db()load_db('go_slim') omnipath_show_db()
NCBI Taxonomy IDs of organisms
ncbi_taxid(name)ncbi_taxid(name)
name |
Vector with any kind of organism name or identifier, can be also mixed type. |
Integer vector with NCBI Taxonomy IDs, NA if a name in the input could not be found.
ncbi_taxid(c("Homo sapiens", "cat", "dog")) # [1] 9606 9685 9615 ncbi_taxid(c(9606, "cat", "doggy")) # [1] 9606 9685 NAncbi_taxid(c("Homo sapiens", "cat", "dog")) # [1] 9606 9685 9615 ncbi_taxid(c(9606, "cat", "doggy")) # [1] 9606 9685 NA
Construct a NicheNet ligand-target model
nichenet_build_model(optimization_results, networks, use_weights = TRUE)nichenet_build_model(optimization_results, networks, use_weights = TRUE)
optimization_results |
The outcome of NicheNet parameter optimization
as produced by |
networks |
A list with NicheNet format signaling, ligand-receptor
and gene regulatory networks as produced by
|
use_weights |
Logical: whether to use the optimized weights. |
A named list with two elements: 'weighted_networks' and 'optimized_parameters'.
## Not run: expression <- nichenet_expression_data() networks <- nichenet_networks() optimization_results <- nichenet_optimization(networks, expression) nichenet_model <- nichenet_build_model(optimization_results, networks) ## End(Not run)## Not run: expression <- nichenet_expression_data() networks <- nichenet_networks() optimization_results <- nichenet_optimization(networks, expression) nichenet_model <- nichenet_build_model(optimization_results, networks) ## End(Not run)
NicheNet uses expression data from a collection of published ligand or receptor KO or perturbation experiments to build its model. This function retrieves the original expression data, deposited in Zenodo (https://zenodo.org/record/3260758).
nichenet_expression_data()nichenet_expression_data()
Nested list, each element contains a data frame of processed expression data and key variables about the experiment.
exp_data <- nichenet_expression_data() head(names(exp_data)) # [1] "bmp4_tgfb" "tgfb_bmp4" "nodal_Nodal" "spectrum_Il4" # [5] "spectrum_Tnf" "spectrum_Ifng" purrr::map_chr(head(exp_data), 'from') # bmp4_tgfb tgfb_bmp4 nodal_Nodal spectrum_Il4 spectrum_Tnf # "BMP4" "TGFB1" "NODAL" "IL4" "TNF" # spectrum_Ifng # "IFNG"exp_data <- nichenet_expression_data() head(names(exp_data)) # [1] "bmp4_tgfb" "tgfb_bmp4" "nodal_Nodal" "spectrum_Il4" # [5] "spectrum_Tnf" "spectrum_Ifng" purrr::map_chr(head(exp_data), 'from') # bmp4_tgfb tgfb_bmp4 nodal_Nodal spectrum_Il4 spectrum_Tnf # "BMP4" "TGFB1" "NODAL" "IL4" "TNF" # spectrum_Ifng # "IFNG"
Builds gene regulatory network prior knowledge for NicheNet using multiple resources.
nichenet_gr_network( omnipath = list(), harmonizome = list(), regnetwork = list(), htridb = list(), remap = list(), evex = list(), pathwaycommons = list(), trrust = list(), only_omnipath = FALSE )nichenet_gr_network( omnipath = list(), harmonizome = list(), regnetwork = list(), htridb = list(), remap = list(), evex = list(), pathwaycommons = list(), trrust = list(), only_omnipath = FALSE )
omnipath |
List with paramaters to be passed to
|
harmonizome |
List with paramaters to be passed to
|
regnetwork |
List with paramaters to be passed to
|
htridb |
List with paramaters to be passed to
|
remap |
List with paramaters to be passed to
|
evex |
List with paramaters to be passed to
|
pathwaycommons |
List with paramaters to be passed to
|
trrust |
List with paramaters to be passed to
|
only_omnipath |
Logical: a shortcut to use only OmniPath as network resource. |
A network data frame (tibble) with gene regulatory interactions suitable for use with NicheNet.
# load everything with the default parameters: gr_network <- nichenet_gr_network() # less targets from ReMap, not using RegNetwork: gr_network <- nichenet_gr_network( # I needed to disable ReMap here due to some issues # of one of the Bioconductor build servers # remap = list(top_targets = 200), remap = NULL, regnetwork = NULL, ) # use only OmniPath: gr_network_omnipath <- nichenet_gr_network(only_omnipath = TRUE)# load everything with the default parameters: gr_network <- nichenet_gr_network() # less targets from ReMap, not using RegNetwork: gr_network <- nichenet_gr_network( # I needed to disable ReMap here due to some issues # of one of the Bioconductor build servers # remap = list(top_targets = 200), remap = NULL, regnetwork = NULL, ) # use only OmniPath: gr_network_omnipath <- nichenet_gr_network(only_omnipath = TRUE)
Builds a gene regulatory network using data from the EVEX database and converts it to a format suitable for NicheNet.
nichenet_gr_network_evex( top_confidence = 0.75, indirect = FALSE, regulation_of_expression = FALSE )nichenet_gr_network_evex( top_confidence = 0.75, indirect = FALSE, regulation_of_expression = FALSE )
top_confidence |
Double, between 0 and 1. Threshold based on the quantile of the confidence score. |
indirect |
Logical: whether to include indirect interactions. |
regulation_of_expression |
Logical: whether to include also the "regulation of expression" type interactions. |
Data frame of interactions in NicheNet format.
Data frame with gene regulatory interactions in NicheNet format.
# use only the 10% with the highest confidence: evex_gr_network <- nichenet_gr_network_evex(top_confidence = .9)# use only the 10% with the highest confidence: evex_gr_network <- nichenet_gr_network_evex(top_confidence = .9)
Builds gene regulatory network prior knowledge for NicheNet using Harmonizome
nichenet_gr_network_harmonizome( datasets = c("cheappi", "encodetfppi", "jasparpwm", "transfac", "transfacpwm", "motifmap", "geotf", "geokinase", "geogene"), ... )nichenet_gr_network_harmonizome( datasets = c("cheappi", "encodetfppi", "jasparpwm", "transfac", "transfacpwm", "motifmap", "geotf", "geokinase", "geogene"), ... )
datasets |
The datasets to use. For possible values please refer to default value and the Harmonizome webpage. |
... |
Ignored. |
Data frame with gene regulatory interactions in NicheNet format.
# use only JASPAR and TRANSFAC: hz_gr_network <- nichenet_gr_network_harmonizome( datasets = c('jasparpwm', 'transfac', 'transfacpwm') )# use only JASPAR and TRANSFAC: hz_gr_network <- nichenet_gr_network_harmonizome( datasets = c('jasparpwm', 'transfac', 'transfacpwm') )
Builds a gene regulatory network using data from the HTRIdb database and converts it to a format suitable for NicheNet.
nichenet_gr_network_htridb()nichenet_gr_network_htridb()
Data frame with gene regulatory interactions in NicheNet format.
htridb_download, nichenet_gr_network
htri_gr_network <- nichenet_gr_network_htridb()htri_gr_network <- nichenet_gr_network_htridb()
Retrieves network prior knowledge from OmniPath and provides it in
a format suitable for NicheNet.
This method never downloads the 'ligrecextra' dataset because the
ligand-receptor interactions are supposed to come from
nichenet_lr_network_omnipath.
nichenet_gr_network_omnipath(min_curation_effort = 0, ...)nichenet_gr_network_omnipath(min_curation_effort = 0, ...)
min_curation_effort |
Lower threshold for curation effort |
... |
Passed to |
A network data frame (tibble) with gene regulatory interactions suitable for use with NicheNet.
# use interactions up to confidence level "C" from DoRothEA: op_gr_network <- nichenet_gr_network_omnipath( dorothea_levels = c('A', 'B', 'C') )# use interactions up to confidence level "C" from DoRothEA: op_gr_network <- nichenet_gr_network_omnipath( dorothea_levels = c('A', 'B', 'C') )
Builds gene regulation prior knowledge for NicheNet using PathwayCommons.
nichenet_gr_network_pathwaycommons( interaction_types = "controls-expression-of", ... )nichenet_gr_network_pathwaycommons( interaction_types = "controls-expression-of", ... )
interaction_types |
Character vector with PathwayCommons interaction types. Please refer to the default value and the PathwayCommons webpage. |
... |
Ignored. |
Data frame with gene regulatory interactions in NicheNet format.
pc_gr_network <- nichenet_gr_network_pathwaycommons()pc_gr_network <- nichenet_gr_network_pathwaycommons()
Builds a gene regulatory network using data from the RegNetwork database and converts it to a format suitable for NicheNet.
nichenet_gr_network_regnetwork()nichenet_gr_network_regnetwork()
Data frame with gene regulatory interactions in NicheNet format.
regn_gr_network <- nichenet_gr_network_regnetwork()regn_gr_network <- nichenet_gr_network_regnetwork()
Builds a gene regulatory network using data from the ReMap database and converts it to a format suitable for NicheNet.
nichenet_gr_network_remap( score = 100, top_targets = 500, only_known_tfs = TRUE )nichenet_gr_network_remap( score = 100, top_targets = 500, only_known_tfs = TRUE )
score |
Numeric: a minimum score between 0 and 1000, records with lower scores will be excluded. If NULL no filtering performed. |
top_targets |
Numeric: the number of top scoring targets for each TF. Essentially the maximum number of targets per TF. If NULL the number of targets is not restricted. |
only_known_tfs |
Logical: whether to exclude TFs which are not in TF census. |
Data frame with gene regulatory interactions in NicheNet format.
# use only max. top 100 targets for each TF: remap_gr_network <- nichenet_gr_network_remap(top_targets = 100)# use only max. top 100 targets for each TF: remap_gr_network <- nichenet_gr_network_remap(top_targets = 100)
Builds a gene regulatory network using data from the TRRUST database and converts it to a format suitable for NicheNet.
nichenet_gr_network_trrust()nichenet_gr_network_trrust()
Data frame with gene regulatory interactions in NicheNet format.
trrust_gr_network <- nichenet_gr_network_trrust()trrust_gr_network <- nichenet_gr_network_trrust()
Calls the NicheNet ligand activity analysis
nichenet_ligand_activities( ligand_target_matrix, lr_network, expressed_genes_transmitter, expressed_genes_receiver, genes_of_interest, background_genes = NULL, n_top_ligands = 42, n_top_targets = 250 )nichenet_ligand_activities( ligand_target_matrix, lr_network, expressed_genes_transmitter, expressed_genes_receiver, genes_of_interest, background_genes = NULL, n_top_ligands = 42, n_top_targets = 250 )
ligand_target_matrix |
A matrix with rows and columns corresponding
to ligands and targets, respectively. Produced by
|
lr_network |
A data frame with ligand-receptor interactions, as
produced by |
expressed_genes_transmitter |
Character vector with the gene symbols of the genes expressed in the cells transmitting the signal. |
expressed_genes_receiver |
Character vector with the gene symbols of the genes expressed in the cells receiving the signal. |
genes_of_interest |
Character vector with the gene symbols of the genes of interest. These are the genes in the receiver cell population that are potentially affected by ligands expressed by interacting cells (e.g. genes differentially expressed upon cell-cell interaction). |
background_genes |
Character vector with the gene symbols of the genes to be used as background. |
n_top_ligands |
How many of the top ligands to include in the ligand-target table. |
n_top_targets |
For each ligand, how many of the top targets to include in the ligand-target table. |
A named list with 'ligand_activities' (a tibble giving several ligand activity scores; following columns in the tibble: $test_ligand, $auroc, $aupr and $pearson) and 'ligand_target_links' (a tibble with columns ligand, target and weight (i.e. regulatory potential score)).
## Not run: networks <- nichenet_networks() expression <- nichenet_expression_data() optimization_results <- nichenet_optimization(networks, expression) nichenet_model <- nichenet_build_model(optimization_results, networks) lt_matrix <- nichenet_ligand_target_matrix( nichenet_model$weighted_networks, networks$lr_network, nichenet_model$optimized_parameters ) ligand_activities <- nichenet_ligand_activities( ligand_target_matrix = lt_matrix, lr_network = networks$lr_network, # the rest of the parameters should come # from your transcriptomics data: expressed_genes_transmitter = expressed_genes_transmitter, expressed_genes_receiver = expressed_genes_receiver, genes_of_interest = genes_of_interest ) ## End(Not run)## Not run: networks <- nichenet_networks() expression <- nichenet_expression_data() optimization_results <- nichenet_optimization(networks, expression) nichenet_model <- nichenet_build_model(optimization_results, networks) lt_matrix <- nichenet_ligand_target_matrix( nichenet_model$weighted_networks, networks$lr_network, nichenet_model$optimized_parameters ) ligand_activities <- nichenet_ligand_activities( ligand_target_matrix = lt_matrix, lr_network = networks$lr_network, # the rest of the parameters should come # from your transcriptomics data: expressed_genes_transmitter = expressed_genes_transmitter, expressed_genes_receiver = expressed_genes_receiver, genes_of_interest = genes_of_interest ) ## End(Not run)
A wrapper around nichenetr::get_weighted_ligand_target_links to
compile a data frame with weighted links from the top ligands to their
top targets.
nichenet_ligand_target_links( ligand_activities, ligand_target_matrix, genes_of_interest, n_top_ligands = 42, n_top_targets = 250 )nichenet_ligand_target_links( ligand_activities, ligand_target_matrix, genes_of_interest, n_top_ligands = 42, n_top_targets = 250 )
ligand_activities |
Ligand activity table as produced by
|
ligand_target_matrix |
Ligand-target matrix as produced by
|
genes_of_interest |
Character vector with the gene symbols of the genes of interest. These are the genes in the receiver cell population that are potentially affected by ligands expressed by interacting cells (e.g. genes differentially expressed upon cell-cell interaction). |
n_top_ligands |
How many of the top ligands to include in the ligand-target table. |
n_top_targets |
For each ligand, how many of the top targets to include in the ligand-target table. |
A tibble with columns ligand, target and weight (i.e. regulatory potential score).
## Not run: networks <- nichenet_networks() expression <- nichenet_expression_data() optimization_results <- nichenet_optimization(networks, expression) nichenet_model <- nichenet_build_model(optimization_results, networks) lt_matrix <- nichenet_ligand_target_matrix( nichenet_model$weighted_networks, networks$lr_network, nichenet_model$optimized_parameters ) ligand_activities <- nichenet_ligand_activities( ligand_target_matrix = lt_matrix, lr_network = networks$lr_network, # the rest of the parameters should come # from your transcriptomics data: expressed_genes_transmitter = expressed_genes_transmitter, expressed_genes_receiver = expressed_genes_receiver, genes_of_interest = genes_of_interest ) lt_links <- nichenet_ligand_target_links( ligand_activities = ligand_activities, ligand_target_matrix = lt_matrix, genes_of_interest = genes_of_interest, n_top_ligands = 20, n_top_targets = 100 ) ## End(Not run)## Not run: networks <- nichenet_networks() expression <- nichenet_expression_data() optimization_results <- nichenet_optimization(networks, expression) nichenet_model <- nichenet_build_model(optimization_results, networks) lt_matrix <- nichenet_ligand_target_matrix( nichenet_model$weighted_networks, networks$lr_network, nichenet_model$optimized_parameters ) ligand_activities <- nichenet_ligand_activities( ligand_target_matrix = lt_matrix, lr_network = networks$lr_network, # the rest of the parameters should come # from your transcriptomics data: expressed_genes_transmitter = expressed_genes_transmitter, expressed_genes_receiver = expressed_genes_receiver, genes_of_interest = genes_of_interest ) lt_links <- nichenet_ligand_target_links( ligand_activities = ligand_activities, ligand_target_matrix = lt_matrix, genes_of_interest = genes_of_interest, n_top_ligands = 20, n_top_targets = 100 ) ## End(Not run)
Creates a NicheNet ligand-target matrix
nichenet_ligand_target_matrix( weighted_networks, lr_network, optimized_parameters, use_weights = TRUE, construct_ligand_target_matrix_param = list() )nichenet_ligand_target_matrix( weighted_networks, lr_network, optimized_parameters, use_weights = TRUE, construct_ligand_target_matrix_param = list() )
weighted_networks |
Weighted networks as provided by
|
lr_network |
A data frame with ligand-receptor interactions, as
produced by |
optimized_parameters |
The outcome of NicheNet parameter optimization
as produced by |
use_weights |
Logical: wether the network sources are weighted. In this function it only affects the output file name. |
construct_ligand_target_matrix_param |
Override parameters for
|
A matrix containing ligand-target probability scores.
## Not run: networks <- nichenet_networks() expression <- nichenet_expression_data() optimization_results <- nichenet_optimization(networks, expression) nichenet_model <- nichenet_build_model(optimization_results, networks) lt_matrix <- nichenet_ligand_target_matrix( nichenet_model$weighted_networks, networks$lr_network, nichenet_model$optimized_parameters ) ## End(Not run)## Not run: networks <- nichenet_networks() expression <- nichenet_expression_data() optimization_results <- nichenet_optimization(networks, expression) nichenet_model <- nichenet_build_model(optimization_results, networks) lt_matrix <- nichenet_ligand_target_matrix( nichenet_model$weighted_networks, networks$lr_network, nichenet_model$optimized_parameters ) ## End(Not run)
Builds ligand-receptor network prior knowledge for NicheNet using multiple resources.
nichenet_lr_network( omnipath = list(), guide2pharma = list(), ramilowski = list(), only_omnipath = FALSE, quality_filter_param = list() )nichenet_lr_network( omnipath = list(), guide2pharma = list(), ramilowski = list(), only_omnipath = FALSE, quality_filter_param = list() )
omnipath |
List with paramaters to be passed to
|
guide2pharma |
List with paramaters to be passed to
|
ramilowski |
List with paramaters to be passed to
|
only_omnipath |
Logical: a shortcut to use only OmniPath as network resource. |
quality_filter_param |
Arguments for |
A network data frame (tibble) with ligand-receptor interactions suitable for use with NicheNet.
# load everything with the default parameters: lr_network <- nichenet_lr_network() # don't use Ramilowski: lr_network <- nichenet_lr_network(ramilowski = NULL) # use only OmniPath: lr_network_omnipath <- nichenet_lr_network(only_omnipath = TRUE)# load everything with the default parameters: lr_network <- nichenet_lr_network() # don't use Ramilowski: lr_network <- nichenet_lr_network(ramilowski = NULL) # use only OmniPath: lr_network_omnipath <- nichenet_lr_network(only_omnipath = TRUE)
Downloads ligand-receptor interactions from the Guide to Pharmacology database and converts it to a format suitable for NicheNet.
nichenet_lr_network_guide2pharma()nichenet_lr_network_guide2pharma()
Data frame with ligand-receptor interactions in NicheNet format.
nichenet_lr_network, guide2pharma_download
g2p_lr_network <- nichenet_lr_network_guide2pharma()g2p_lr_network <- nichenet_lr_network_guide2pharma()
Retrieves network prior knowledge from OmniPath and provides it in
a format suitable for NicheNet.
This method never downloads the 'ligrecextra' dataset because the
ligand-receptor interactions are supposed to come from
nichenet_lr_network_omnipath.
nichenet_lr_network_omnipath(quality_filter_param = list(), ...)nichenet_lr_network_omnipath(quality_filter_param = list(), ...)
quality_filter_param |
List with arguments for |
... |
Passed to |
A network data frame (tibble) with ligand-receptor interactions suitable for use with NicheNet.
# use only ligand-receptor interactions (not for example ECM-adhesion): op_lr_network <- nichenet_lr_network_omnipath(ligand_receptor = TRUE) # use only CellPhoneDB and Guide to Pharmacology: op_lr_network <- nichenet_lr_network_omnipath( resources = c('CellPhoneDB', 'Guide2Pharma') ) # only interactions where the receiver is a transporter: op_lr_network <- nichenet_lr_network_omnipath( receiver_param = list(parent = 'transporter') )# use only ligand-receptor interactions (not for example ECM-adhesion): op_lr_network <- nichenet_lr_network_omnipath(ligand_receptor = TRUE) # use only CellPhoneDB and Guide to Pharmacology: op_lr_network <- nichenet_lr_network_omnipath( resources = c('CellPhoneDB', 'Guide2Pharma') ) # only interactions where the receiver is a transporter: op_lr_network <- nichenet_lr_network_omnipath( receiver_param = list(parent = 'transporter') )
Downloads ligand-receptor interactions from Supplementary Table 2 of the paper 'A draft network of ligand–receptor-mediated multicellular signalling in human' (Ramilowski et al. 2015, https://www.nature.com/articles/ncomms8866). It converts the downloaded table to a format suitable for NicheNet.
nichenet_lr_network_ramilowski( evidences = c("literature supported", "putative") )nichenet_lr_network_ramilowski( evidences = c("literature supported", "putative") )
evidences |
Character: evidence types, "literature supported", "putative" or both. |
Data frame with ligand-receptor interactions in NicheNet format.
# use only the literature supported data: rami_lr_network <- nichenet_lr_network_ramilowski( evidences = 'literature supported' )# use only the literature supported data: rami_lr_network <- nichenet_lr_network_ramilowski( evidences = 'literature supported' )
Builds all prior knowledge data required by NicheNet. For this it calls a multitude of methods to download and combine data from various databases according to the settings. The content of the prior knowledge data is highly customizable, see the documentation of the related functions. After the prior knowledge is ready, it performs parameter optimization to build a NicheNet model. This results a weighted ligand- target matrix. Then, considering the expressed genes from user provided data, a gene set of interest and background genes, it executes the NicheNet ligand activity analysis.
nichenet_main( only_omnipath = FALSE, expressed_genes_transmitter = NULL, expressed_genes_receiver = NULL, genes_of_interest = NULL, background_genes = NULL, use_weights = TRUE, n_top_ligands = 42, n_top_targets = 250, signaling_network = list(), lr_network = list(), gr_network = list(), small = FALSE, tiny = FALSE, make_multi_objective_function_param = list(), objective_function_param = list(), mlrmbo_optimization_param = list(), construct_ligand_target_matrix_param = list(), results_dir = NULL, quality_filter_param = list() )nichenet_main( only_omnipath = FALSE, expressed_genes_transmitter = NULL, expressed_genes_receiver = NULL, genes_of_interest = NULL, background_genes = NULL, use_weights = TRUE, n_top_ligands = 42, n_top_targets = 250, signaling_network = list(), lr_network = list(), gr_network = list(), small = FALSE, tiny = FALSE, make_multi_objective_function_param = list(), objective_function_param = list(), mlrmbo_optimization_param = list(), construct_ligand_target_matrix_param = list(), results_dir = NULL, quality_filter_param = list() )
only_omnipath |
Logical: use only OmniPath for network knowledge. This is a simple switch for convenience, further options are available by the other arguments. By default we use all available resources. The networks can be customized on a resource by resource basis, as well as providing custom parameters for individual resources, using the parameters 'signaling_network', 'lr_network' and 'gr_network'. |
expressed_genes_transmitter |
Character vector with the gene symbols of the genes expressed in the cells transmitting the signal. |
expressed_genes_receiver |
Character vector with the gene symbols of the genes expressed in the cells receiving the signal. |
genes_of_interest |
Character vector with the gene symbols of the genes of interest. These are the genes in the receiver cell population that are potentially affected by ligands expressed by interacting cells (e.g. genes differentially expressed upon cell-cell interaction). |
background_genes |
Character vector with the gene symbols of the genes to be used as background. |
use_weights |
Logical: calculate and use optimized weights for resources (i.e. one resource seems to be better than another, hence the former is considered with a higher weight). |
n_top_ligands |
How many of the top ligands to include in the ligand-target table. |
n_top_targets |
How many of the top targets (for each of the top ligands) to consider in the ligand-target table. |
signaling_network |
A list of parameters for building the signaling
network, passed to |
lr_network |
A list of parameters for building the ligand-receptor
network, passed to |
gr_network |
A list of parameters for building the gene regulatory
network, passed to |
small |
Logical: build a small network for testing purposes, using only OmniPath data. It is also a high quality network, it is reasonable to try the analysis with this small network. |
tiny |
Logical: build an even smaller network for testing purposes. As this involves random subsetting, it's not recommended to use this network for analysis. |
make_multi_objective_function_param |
Override parameters for
|
objective_function_param |
Override additional arguments passed to the objective function. |
mlrmbo_optimization_param |
Override arguments for
|
construct_ligand_target_matrix_param |
Override parameters for
|
results_dir |
Character: path to the directory to save intermediate and final outputs from NicheNet methods. |
quality_filter_param |
Arguments for |
About small and tiny networks: Building a NicheNet model
is computationally demanding, taking several hours to run. As this is
related to the enormous size of the networks, to speed up testing we can
use smaller networks, around 1,000 times smaller, with few thousands of
interactions instead of few millions. Random subsetting of the whole
network would result disjunct fragments, instead we load only a few
resources. To run the whole pipeline with tiny networks use
nichenet_test.
A named list with the intermediate and final outputs of the pipeline: 'networks', 'expression', 'optimized_parameters', 'weighted_networks' and 'ligand_target_matrix'.
## Not run: nichenet_results <- nichenet_main( # altering some network resource parameters, the rest # of the resources will be loaded according to the defaults signaling_network = list( cpdb = NULL, # this resource will be excluded inbiomap = NULL, evex = list(min_confidence = 1.0) # override some parameters ), gr_network = list(only_omnipath = TRUE), n_top_ligands = 20, # override the default number of CPU cores to use mlrmbo_optimization_param = list(ncores = 4) ) ## End(Not run)## Not run: nichenet_results <- nichenet_main( # altering some network resource parameters, the rest # of the resources will be loaded according to the defaults signaling_network = list( cpdb = NULL, # this resource will be excluded inbiomap = NULL, evex = list(min_confidence = 1.0) # override some parameters ), gr_network = list(only_omnipath = TRUE), n_top_ligands = 20, # override the default number of CPU cores to use mlrmbo_optimization_param = list(ncores = 4) ) ## End(Not run)
Builds network knowledge required by NicheNet. For this it calls a multitude of methods to download and combine data from various databases according to the settings. The content of the prior knowledge data is highly customizable, see the documentation of the related functions.
nichenet_networks( signaling_network = list(), lr_network = list(), gr_network = list(), only_omnipath = FALSE, small = FALSE, tiny = FALSE, quality_filter_param = list() )nichenet_networks( signaling_network = list(), lr_network = list(), gr_network = list(), only_omnipath = FALSE, small = FALSE, tiny = FALSE, quality_filter_param = list() )
signaling_network |
A list of parameters for building the signaling
network, passed to |
lr_network |
A list of parameters for building the ligand-receptor
network, passed to |
gr_network |
A list of parameters for building the gene regulatory
network, passed to |
only_omnipath |
Logical: a shortcut to use only OmniPath as network resource. |
small |
Logical: build a small network for testing purposes, using only OmniPath data. It is also a high quality network, it is reasonable to try the analysis with this small network. |
tiny |
Logical: build an even smaller network for testing purposes. As this involves random subsetting, it's not recommended to use this network for analysis. |
quality_filter_param |
Arguments for |
A named list with three network data frames (tibbles): the signaling, the ligand-receptor (lr) and the gene regulatory (gr) networks.
## Not run: networks <- nichenet_networks() dplyr::sample_n(networks$gr_network, 10) # # A tibble: 10 x 4 # from to source database # <chr> <chr> <chr> <chr> # 1 MAX ALG3 harmonizome_ENCODE harmonizome # 2 MAX IMPDH1 harmonizome_ENCODE harmonizome # 3 SMAD5 LCP1 Remap_5 Remap # 4 HNF4A TNFRSF19 harmonizome_CHEA harmonizome # 5 SMC3 FAP harmonizome_ENCODE harmonizome # 6 E2F6 HIST1H1B harmonizome_ENCODE harmonizome # 7 TFAP2C MAT2B harmonizome_ENCODE harmonizome # 8 USF1 TBX4 harmonizome_TRANSFAC harmonizome # 9 MIR133B FETUB harmonizome_TRANSFAC harmonizome # 10 SP4 HNRNPH2 harmonizome_ENCODE harmonizome ## End(Not run) # use only OmniPath: omnipath_networks <- nichenet_networks(only_omnipath = TRUE)## Not run: networks <- nichenet_networks() dplyr::sample_n(networks$gr_network, 10) # # A tibble: 10 x 4 # from to source database # <chr> <chr> <chr> <chr> # 1 MAX ALG3 harmonizome_ENCODE harmonizome # 2 MAX IMPDH1 harmonizome_ENCODE harmonizome # 3 SMAD5 LCP1 Remap_5 Remap # 4 HNF4A TNFRSF19 harmonizome_CHEA harmonizome # 5 SMC3 FAP harmonizome_ENCODE harmonizome # 6 E2F6 HIST1H1B harmonizome_ENCODE harmonizome # 7 TFAP2C MAT2B harmonizome_ENCODE harmonizome # 8 USF1 TBX4 harmonizome_TRANSFAC harmonizome # 9 MIR133B FETUB harmonizome_TRANSFAC harmonizome # 10 SP4 HNRNPH2 harmonizome_ENCODE harmonizome ## End(Not run) # use only OmniPath: omnipath_networks <- nichenet_networks(only_omnipath = TRUE)
Optimize NicheNet method parameters, i.e. PageRank parameters and source weights, basedon a collection of experiments where the effect of a ligand on gene expression was measured.
nichenet_optimization( networks, expression, make_multi_objective_function_param = list(), objective_function_param = list(), mlrmbo_optimization_param = list() )nichenet_optimization( networks, expression, make_multi_objective_function_param = list(), objective_function_param = list(), mlrmbo_optimization_param = list() )
networks |
A list with NicheNet format signaling, ligand-receptor
and gene regulatory networks as produced by
|
expression |
A list with expression data from ligand perturbation
experiments, as produced by |
make_multi_objective_function_param |
Override parameters for
|
objective_function_param |
Override additional arguments passed to the objective function. |
mlrmbo_optimization_param |
Override arguments for
|
A result object from the function mlrMBO::mbo. Among other
things, this contains the optimal parameter settings, the output
corresponding to every input etc.
## Not run: networks <- nichenet_networks() expression <- nichenet_expression_data() optimization_results <- nichenet_optimization(networks, expression) ## End(Not run)## Not run: networks <- nichenet_networks() expression <- nichenet_expression_data() optimization_results <- nichenet_optimization(networks, expression) ## End(Not run)
Removes from the expression data the perturbation experiments involving ligands without connections.
nichenet_remove_orphan_ligands(expression, lr_network)nichenet_remove_orphan_ligands(expression, lr_network)
expression |
Expression data as returned by
|
lr_network |
A NicheNet format ligand-recptor network data frame as
produced by |
The same list as 'expression' with certain elements removed.
lr_network <- nichenet_lr_network() expression <- nichenet_expression_data() expression <- nichenet_remove_orphan_ligands(expression, lr_network)lr_network <- nichenet_lr_network() expression <- nichenet_expression_data() expression <- nichenet_remove_orphan_ligands(expression, lr_network)
Path to the directory to save intermediate and final outputs from NicheNet methods.
nichenet_results_dir()nichenet_results_dir()
Character: path to the NicheNet results directory.
nichenet_results_dir() # [1] "nichenet_results"nichenet_results_dir() # [1] "nichenet_results"
Builds signaling network prior knowledge for NicheNet using multiple resources.
nichenet_signaling_network( omnipath = list(), pathwaycommons = list(), harmonizome = list(), vinayagam = list(), cpdb = list(), evex = list(), inbiomap = list(), only_omnipath = FALSE )nichenet_signaling_network( omnipath = list(), pathwaycommons = list(), harmonizome = list(), vinayagam = list(), cpdb = list(), evex = list(), inbiomap = list(), only_omnipath = FALSE )
omnipath |
List with paramaters to be passed to
|
pathwaycommons |
List with paramaters to be passed to
|
harmonizome |
List with paramaters to be passed to
|
vinayagam |
List with paramaters to be passed to
|
cpdb |
List with paramaters to be passed to
|
evex |
List with paramaters to be passed to
|
inbiomap |
List with paramaters to be passed to
|
only_omnipath |
Logical: a shortcut to use only OmniPath as network resource. |
A network data frame (tibble) with signaling interactions suitable for use with NicheNet.
# load everything with the default parameters: # we don't load inBio Map due to the - hopefully # temporary - issues of their server sig_network <- nichenet_signaling_network(inbiomap = NULL, cpdb = NULL) # override parameters for some resources: sig_network <- nichenet_signaling_network( omnipath = list(resources = c('SIGNOR', 'SignaLink3', 'SPIKE')), pathwaycommons = NULL, harmonizome = list(datasets = c('phosphositeplus', 'depod')), # we can not include this in everyday tests as it takes too long: # cpdb = list(complex_max_size = 1, min_score = .98), cpdb = NULL, evex = list(min_confidence = 1.5), inbiomap = NULL ) # use only OmniPath: sig_network_omnipath <- nichenet_signaling_network(only_omnipath = TRUE)# load everything with the default parameters: # we don't load inBio Map due to the - hopefully # temporary - issues of their server sig_network <- nichenet_signaling_network(inbiomap = NULL, cpdb = NULL) # override parameters for some resources: sig_network <- nichenet_signaling_network( omnipath = list(resources = c('SIGNOR', 'SignaLink3', 'SPIKE')), pathwaycommons = NULL, harmonizome = list(datasets = c('phosphositeplus', 'depod')), # we can not include this in everyday tests as it takes too long: # cpdb = list(complex_max_size = 1, min_score = .98), cpdb = NULL, evex = list(min_confidence = 1.5), inbiomap = NULL ) # use only OmniPath: sig_network_omnipath <- nichenet_signaling_network(only_omnipath = TRUE)
Builds signaling network prior knowledge using ConsensusPathDB (CPDB) data. Note, the interactions from CPDB are not directed and many of them comes from complex expansion. Find out more at http://cpdb.molgen.mpg.de/.
nichenet_signaling_network_cpdb(...)nichenet_signaling_network_cpdb(...)
... |
Passed to |
A network data frame (tibble) with signaling interactions suitable for use with NicheNet.
# use some parameters stricter than default: cpdb_signaling_network <- nichenet_signaling_network_cpdb( complex_max_size = 2, min_score = .99 )# use some parameters stricter than default: cpdb_signaling_network <- nichenet_signaling_network_cpdb( complex_max_size = 2, min_score = .99 )
Builds signaling network prior knowledge for NicheNet from the EVEX database.
nichenet_signaling_network_evex(top_confidence = 0.75, indirect = FALSE, ...)nichenet_signaling_network_evex(top_confidence = 0.75, indirect = FALSE, ...)
top_confidence |
Double, between 0 and 1. Threshold based on the quantile of the confidence score. |
indirect |
Logical: whether to include indirect interactions. |
... |
Ignored. |
A network data frame (tibble) with signaling interactions suitable for use with NicheNet.
ev_signaling_network <- nichenet_signaling_network_evex( top_confidence = .9 )ev_signaling_network <- nichenet_signaling_network_evex( top_confidence = .9 )
Builds signaling network prior knowledge for NicheNet using Harmonizome
nichenet_signaling_network_harmonizome( datasets = c("phosphositeplus", "kea", "depod"), ... )nichenet_signaling_network_harmonizome( datasets = c("phosphositeplus", "kea", "depod"), ... )
datasets |
The datasets to use. For possible values please refer to default value and the Harmonizome webpage. |
... |
Ignored. |
A network data frame (tibble) with signaling interactions suitable for use with NicheNet.
# use only KEA and PhosphoSite: hz_signaling_network <- nichenet_signaling_network_harmonizome( datasets = c('kea', 'phosphositeplus') )# use only KEA and PhosphoSite: hz_signaling_network <- nichenet_signaling_network_harmonizome( datasets = c('kea', 'phosphositeplus') )
Builds signaling network prior knowledge for NicheNet from the InWeb InBioMap database.
nichenet_signaling_network_inbiomap(...)nichenet_signaling_network_inbiomap(...)
... |
Ignored. |
A network data frame (tibble) with signaling interactions suitable for use with NicheNet.
nichenet_signaling_network, inbiomap_download
## Not run: ib_signaling_network <- nichenet_signaling_network_inbiomap() ## End(Not run)## Not run: ib_signaling_network <- nichenet_signaling_network_inbiomap() ## End(Not run)
Retrieves network prior knowledge from OmniPath and provides it in
a format suitable for NicheNet.
This method never downloads the 'ligrecextra' dataset because the
ligand-receptor interactions are supposed to come from
nichenet_lr_network_omnipath.
nichenet_signaling_network_omnipath(min_curation_effort = 0, ...)nichenet_signaling_network_omnipath(min_curation_effort = 0, ...)
min_curation_effort |
Lower threshold for curation effort |
... |
Passed to |
A network data frame (tibble) with signaling interactions suitable for use with NicheNet.
# use interactions with at least 2 evidences (reference or database) op_signaling_network <- nichenet_signaling_network_omnipath( min_curation_effort = 2 )# use interactions with at least 2 evidences (reference or database) op_signaling_network <- nichenet_signaling_network_omnipath( min_curation_effort = 2 )
Builds signaling network prior knowledge for NicheNet using PathwayCommons.
nichenet_signaling_network_pathwaycommons( interaction_types = c("catalysis-precedes", "controls-phosphorylation-of", "controls-state-change-of", "controls-transport-of", "in-complex-with", "interacts-with"), ... )nichenet_signaling_network_pathwaycommons( interaction_types = c("catalysis-precedes", "controls-phosphorylation-of", "controls-state-change-of", "controls-transport-of", "in-complex-with", "interacts-with"), ... )
interaction_types |
Character vector with PathwayCommons interaction types. Please refer to the default value and the PathwayCommons webpage. |
... |
Ignored. |
A network data frame (tibble) with signaling interactions suitable for use with NicheNet.
# use only the "controls-transport-of" interactions: pc_signaling_network <- nichenet_signaling_network_pathwaycommons( interaction_types = 'controls-transport-of' )# use only the "controls-transport-of" interactions: pc_signaling_network <- nichenet_signaling_network_pathwaycommons( interaction_types = 'controls-transport-of' )
Builds signaling network prior knowledge for NicheNet using Vinayagam 2011 Supplementary Table S6. Find out more at https://doi.org/10.1126/scisignal.2001699.
nichenet_signaling_network_vinayagam(...)nichenet_signaling_network_vinayagam(...)
... |
Ignored. |
A network data frame (tibble) with signaling interactions suitable for use with NicheNet.
vi_signaling_network <- nichenet_signaling_network_vinayagam()vi_signaling_network <- nichenet_signaling_network_vinayagam()
Loads a tiny network and runs the NicheNet pipeline with low number of
iterations in the optimization process. This way the pipeline runs in
a reasonable time in order to test the code. Due to the random subsampling
disconnected networks might be produced sometimes. If you see an error
like "Error in if (sd(prediction_vector) == 0) ... missing value
where TRUE/FALSE needed", the random subsampled input is not appropriate.
In this case just interrupt and call again. This test ensures the
computational integrity of the pipeline. If it fails during the
optimization process, try to start it over several times, even
restarting R. The unpredictability is related to mlrMBO and
nichenetr not being prepared to handle certain conditions, and
it's also difficult to find out which conditions lead to which errors.
At least 3 different errors appear time to time, depending on the input.
It also seems like restarting R sometimes helps, suggesting that the
entire system might be somehow stateful. You can ignore the
Parallelization was not stopped warnings on repeated runs.
nichenet_test(...)nichenet_test(...)
... |
Passed to |
A named list with the intermediate and final outputs of the pipeline: 'networks', 'expression', 'optimized_parameters', 'weighted_networks' and 'ligand_target_matrix'.
## Not run: nnt <- nichenet_test() ## End(Not run)## Not run: nnt <- nichenet_test() ## End(Not run)
NicheNet requires the availability of some lazy loaded external data
which are not available if the package is not loaded and attached. Also,
the BBmisc::convertToShortString used for error reporting in
mlrMBO::evalTargetFun.OptState is patched here to print longer
error messages. Maybe it's a better solution to attach nichenetr
before running the NicheNet pipeline. Alternatively you can try to call
this function in the beginning. Why we don't call this automatically is
just because we don't want to load datasets from another package without
the user knowing about it.
nichenet_workarounds()nichenet_workarounds()
Returns NULL.
## Not run: nichenet_workarounds() ## End(Not run)## Not run: nichenet_workarounds() ## End(Not run)
Reads the contents of an OBO file and processes it into data frames or a list based data structure.
obo_parser( path, relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates", "negatively_regulates"), shorten_namespace = TRUE, tables = TRUE )obo_parser( path, relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates", "negatively_regulates"), shorten_namespace = TRUE, tables = TRUE )
path |
Path to the OBO file. |
relations |
Character vector: process only these relations. |
shorten_namespace |
Logical: shorten the namespace to a single letter code (as usual for Gene Ontology, e.g. cellular_component = "C"). |
tables |
Logical: return data frames (tibbles) instead of nested lists. |
A list with the following elements: 1) "names" a list with
terms as names and names as values; 2) "namespaces" a list with
terms as names and namespaces as values; 3) "relations" a list with
relations between terms: terms are keys, values are lists with
relations as names and character vectors of related terms as
values; 4) "subsets" a list with terms as keys and character
vectors of subset names as values (or NULL if the term
does not belong to any subset); 5) "obsolete" character vector
with all the terms labeled as obsolete. If the tables
parameter is TRUE, "names", "namespaces", "relations"
and "subsets" will be data frames (tibbles).
goslim_url <- "http://current.geneontology.org/ontology/subsets/goslim_generic.obo" path <- tempfile() httr::GET(goslim_url, httr::write_disk(path, overwrite = TRUE)) obo <- obo_parser(path, tables = FALSE) unlink(path) names(obo) # [1] "names" "namespaces" "relations" "subsets" "obsolete" head(obo$relations, n = 2) # $`GO:0000001` # $`GO:0000001`$is_a # [1] "GO:0048308" "GO:0048311" # # $`GO:0000002` # $`GO:0000002`$is_a # [1] "GO:0007005"goslim_url <- "http://current.geneontology.org/ontology/subsets/goslim_generic.obo" path <- tempfile() httr::GET(goslim_url, httr::write_disk(path, overwrite = TRUE)) obo <- obo_parser(path, tables = FALSE) unlink(path) names(obo) # [1] "names" "namespaces" "relations" "subsets" "obsolete" head(obo$relations, n = 2) # $`GO:0000001` # $`GO:0000001`$is_a # [1] "GO:0048308" "GO:0048311" # # $`GO:0000002` # $`GO:0000002`$is_a # [1] "GO:0007005"
Note: OMA species codes are whenever possible identical to UniProt codes.
oma_code(name)oma_code(name)
name |
Vector with any kind of organism name or identifier, can be also mixed type. |
A character vector with the Orthologous Matrix (OMA) codes of the organisms.
oma_code(c(10090, "cjacchus", "Vicugna pacos")) # [1] "MOUSE" "CALJA" "VICPA"oma_code(c(10090, "cjacchus", "Vicugna pacos")) # [1] "MOUSE" "CALJA" "VICPA"
Organism identifiers from the Orthologous Matrix
oma_organisms()oma_organisms()
A data frame with organism identifiers.
oma_organisms()oma_organisms()
From the web API of Orthologous Matrix (OMA). Items which could not be translated to 'id_type' (but present in the data with their internal OMA IDs) are removed.
oma_pairwise( organism_a = "human", organism_b = "mouse", id_type = "uniprot", mappings = c("1:1", "1:m", "n:1", "n:m"), only_ids = TRUE )oma_pairwise( organism_a = "human", organism_b = "mouse", id_type = "uniprot", mappings = c("1:1", "1:m", "n:1", "n:m"), only_ids = TRUE )
organism_a |
Name or identifier of an organism. |
organism_b |
Name or identifier of another organism. |
id_type |
The gene or protein identifier to use in the table. For a
list of supported ID types see 'omnipathr.env$id_types$oma'. In addition,
"genesymbol" is supported, in this case
|
mappings |
Character vector: control ambiguous mappings:
|
only_ids |
Logical: include only the two identifier columns, not the mapping type and the orthology group columns. |
A data frame with orthologous gene pairs.
oma_pairwise("human", "mouse", "uniprot") # # A tibble: 21,753 × 4 # id_organism_a id_organism_b mapping oma_group # <chr> <chr> <chr> <dbl> # 1 Q15326 Q8R5C8 1:1 1129380 # 2 Q9Y2E4 B2RQ71 1:1 681224 # 3 Q92615 Q6A0A2 1:1 1135087 # 4 Q9BZE4 Q99ME9 1:1 1176239 # 5 Q9BXS1 Q8BFZ6 1:m NA # # … with 21,743 more rowsoma_pairwise("human", "mouse", "uniprot") # # A tibble: 21,753 × 4 # id_organism_a id_organism_b mapping oma_group # <chr> <chr> <chr> <dbl> # 1 Q15326 Q8R5C8 1:1 1129380 # 2 Q9Y2E4 B2RQ71 1:1 681224 # 3 Q92615 Q6A0A2 1:1 1135087 # 4 Q9BZE4 Q99ME9 1:1 1176239 # 5 Q9BXS1 Q8BFZ6 1:m NA # # … with 21,743 more rows
The Orthologous Matrix (OMA), a resource of orthologous relationships
between genes, doesn't provide gene symbols, the identifier preferred in
many bioinformatics pipelines. Hence this function wraps
oma_pairwise by translating the identifiers used in OMA to
gene symbols. Items that can not be translated to 'id_type' (but present
in the data with their internal OMA IDs) will be removed. Then,
in this function we translate the identifiers to gene symbols.
oma_pairwise_genesymbols( organism_a = "human", organism_b = "mouse", oma_id_type = "uniprot_entry", mappings = c("1:1", "1:m", "n:1", "n:m"), only_ids = TRUE )oma_pairwise_genesymbols( organism_a = "human", organism_b = "mouse", oma_id_type = "uniprot_entry", mappings = c("1:1", "1:m", "n:1", "n:m"), only_ids = TRUE )
organism_a |
Name or identifier of an organism. |
organism_b |
Name or identifier of another organism. |
oma_id_type |
Character: the gene or protein identifier to be queried from OMA. These IDs will be translated to 'id_type'. |
mappings |
Character vector: control ambiguous mappings:
|
only_ids |
Logical: include only the two identifier columns, not the mapping type and the orthology group columns. |
A data frame with orthologous gene pairs.
oma_pairwise_genesymbols("human", "mouse")oma_pairwise_genesymbols("human", "mouse")
The Orthologous Matrix (OMA), a resource of orthologous relationships
between genes, doesn't provide gene symbols, the identifier preferred in
many bioinformatics pipelines. Hence this function wraps
oma_pairwise by translating the identifiers used in OMA to
gene symbols. Items that can not be translated to 'id_type' (but present
in the data with their internal OMA IDs) will be removed. Then,
in this function we translate the identifiers to the desired ID type.
oma_pairwise_translated( organism_a = "human", organism_b = "mouse", id_type = "uniprot", oma_id_type = "uniprot_entry", mappings = c("1:1", "1:m", "n:1", "n:m"), only_ids = TRUE )oma_pairwise_translated( organism_a = "human", organism_b = "mouse", id_type = "uniprot", oma_id_type = "uniprot_entry", mappings = c("1:1", "1:m", "n:1", "n:m"), only_ids = TRUE )
organism_a |
Name or identifier of an organism. |
organism_b |
Name or identifier of another organism. |
id_type |
The gene or protein identifier to use in the table. For a list of supported ID types see 'omnipathr.env$id_types$oma'. These are the identifiers that will be translated to gene symbols. |
oma_id_type |
Character: the gene or protein identifier to be queried from OMA. These IDs will be translated to 'id_type'. |
mappings |
Character vector: control ambiguous mappings:
|
only_ids |
Logical: include only the two identifier columns, not the mapping type and the orthology group columns. |
A data frame with orthologous gene pairs.
oma_pairwise_translated("human", "mouse")oma_pairwise_translated("human", "mouse")
Removes the old versions, the failed downloads and the files in the cache
directory which are missing from the database. For more flexible
operations use omnipath_cache_remove and
omnipath_cache_clean.
omnipath_cache_autoclean()omnipath_cache_autoclean()
Invisibl returns the cache database (list of cache records).
## Not run: omnipath_cache_autoclean() ## End(Not run)## Not run: omnipath_cache_autoclean() ## End(Not run)
Removes the items from the cache directory which are unknown by the cache database
omnipath_cache_clean()omnipath_cache_clean()
Returns 'NULL'.
omnipath_cache_clean()omnipath_cache_clean()
Removes the cache database entries without existing files
omnipath_cache_clean_db(...)omnipath_cache_clean_db(...)
... |
Ignored. |
Returns 'NULL'.
omnipath_cache_clean_db()omnipath_cache_clean_db()
Sets the download status to ready for a cache item
omnipath_cache_download_ready(version, key = NULL)omnipath_cache_download_ready(version, key = NULL)
version |
Version of the cache item. If does not exist a new version item will be created |
key |
Key of the cache item |
Character: invisibly returns the version number of the cache version item.
bioc_url <- 'https://bioconductor.org/' # request a new version item (or retrieve the latest) new_version <- omnipath_cache_latest_or_new(url = bioc_url) # check if the version item is not a finished download new_version$status # [1] "unknown" # download the file httr::GET(bioc_url, httr::write_disk(new_version$path, overwrite = TRUE)) # report to the cache database that the download is ready omnipath_cache_download_ready(new_version) # now the status is ready: version <- omnipath_cache_latest_or_new(url = bioc_url) version$status # "ready" version$dl_finished # [1] "2021-03-09 16:48:38 CET" omnipath_cache_remove(url = bioc_url) # cleaning upbioc_url <- 'https://bioconductor.org/' # request a new version item (or retrieve the latest) new_version <- omnipath_cache_latest_or_new(url = bioc_url) # check if the version item is not a finished download new_version$status # [1] "unknown" # download the file httr::GET(bioc_url, httr::write_disk(new_version$path, overwrite = TRUE)) # report to the cache database that the download is ready omnipath_cache_download_ready(new_version) # now the status is ready: version <- omnipath_cache_latest_or_new(url = bioc_url) version$status # "ready" version$dl_finished # [1] "2021-03-09 16:48:38 CET" omnipath_cache_remove(url = bioc_url) # cleaning up
Filters the versions based on multiple conditions: their age and status
omnipath_cache_filter_versions( record, latest = FALSE, max_age = NULL, min_age = NULL, status = CACHE_STATUS$READY )omnipath_cache_filter_versions( record, latest = FALSE, max_age = NULL, min_age = NULL, status = CACHE_STATUS$READY )
record |
A cache record |
latest |
Return the most recent version |
max_age |
The maximum age in days (e.g. 5: 5 days old or more recent) |
min_age |
The minimum age in days (e.g. 5: 5 days old or older) |
status |
Character vector with status codes. By default only the versions with 'ready' (completed download) status are selected |
Character vector with version IDs, NA if no version satisfies the conditions.
# creating an example cache record bioc_url <- 'https://bioconductor.org/' version <- omnipath_cache_latest_or_new(url = bioc_url) httr::GET(bioc_url, httr::write_disk(version$path, overwrite = TRUE)) omnipath_cache_download_ready(version) record <- dplyr::first(omnipath_cache_search('biocond')) # only the versions with status "ready" version_numbers <- omnipath_cache_filter_versions(record, status = 'ready') omnipath_cache_remove(url = bioc_url) # cleaning up# creating an example cache record bioc_url <- 'https://bioconductor.org/' version <- omnipath_cache_latest_or_new(url = bioc_url) httr::GET(bioc_url, httr::write_disk(version$path, overwrite = TRUE)) omnipath_cache_download_ready(version) record <- dplyr::first(omnipath_cache_search('biocond')) # only the versions with status "ready" version_numbers <- omnipath_cache_filter_versions(record, status = 'ready') omnipath_cache_remove(url = bioc_url) # cleaning up
Retrieves one item from the cache directory
omnipath_cache_get( key = NULL, url = NULL, post = NULL, payload = NULL, create = TRUE, ... )omnipath_cache_get( key = NULL, url = NULL, post = NULL, payload = NULL, create = TRUE, ... )
key |
The key of the cache record |
url |
URL pointing to the resource |
post |
HTTP POST parameters as a list |
payload |
HTTP data payload |
create |
Create a new entry if doesn't exist yet |
... |
Passed to |
Cache record: an existing record if the entry already exists, otherwise a newly created and inserted record
# create an example cache record bioc_url <- 'https://bioconductor.org/' version <- omnipath_cache_latest_or_new(url = bioc_url) omnipath_cache_remove(url = bioc_url) # cleaning up # retrieve the cache record record <- omnipath_cache_get(url = bioc_url) record$key # [1] "41346a00fb20d2a9df03aa70cf4d50bf88ab154a" record$url # [1] "https://bioconductor.org/"# create an example cache record bioc_url <- 'https://bioconductor.org/' version <- omnipath_cache_latest_or_new(url = bioc_url) omnipath_cache_remove(url = bioc_url) # cleaning up # retrieve the cache record record <- omnipath_cache_get(url = bioc_url) record$key # [1] "41346a00fb20d2a9df03aa70cf4d50bf88ab154a" record$url # [1] "https://bioconductor.org/"
Generates a hash which identifies an element in the cache database
omnipath_cache_key(url, post = NULL, payload = NULL)omnipath_cache_key(url, post = NULL, payload = NULL)
url |
Character vector with URLs |
post |
List with the HTTP POST parameters or a list of lists if the url vector is longer than 1. NULL for queries without POST parameters. |
payload |
HTTP data payload. List with multiple items if the url vector is longer than 1. NULL for queries without data. |
Character vector of cache record keys.
bioc_url <- 'https://bioconductor.org/' omnipath_cache_key(bioc_url) # [1] "41346a00fb20d2a9df03aa70cf4d50bf88ab154a"bioc_url <- 'https://bioconductor.org/' omnipath_cache_key(bioc_url) # [1] "41346a00fb20d2a9df03aa70cf4d50bf88ab154a"
Looks up a record in the cache and returns its latest valid version. If the record doesn't exist or no valid version available, creates a new one.
omnipath_cache_latest_or_new( key = NULL, url = NULL, post = NULL, payload = NULL, create = TRUE, ... )omnipath_cache_latest_or_new( key = NULL, url = NULL, post = NULL, payload = NULL, create = TRUE, ... )
key |
The key of the cache record |
url |
URL pointing to the resource |
post |
HTTP POST parameters as a list |
payload |
HTTP data payload |
create |
Logical: whether to create and return a new version. If FALSE only the latest existing valid version is returned, if available. |
... |
Passed to |
A cache version item.
## Not run: # retrieve the latest version of the first cache record # found by the search keyword "bioplex" latest_bioplex <- omnipath_cache_latest_or_new( names(omnipath_cache_search('bioplex'))[1] ) latest_bioplex$dl_finished # [1] "2021-03-09 14:28:50 CET" latest_bioplex$path # [1] "/home/denes/.cache/OmnipathR/378e0def2ac97985f629-1.rds" ## End(Not run) # create an example cache record bioc_url <- 'https://bioconductor.org/' version <- omnipath_cache_latest_or_new(url = bioc_url) omnipath_cache_remove(url = bioc_url) # cleaning up## Not run: # retrieve the latest version of the first cache record # found by the search keyword "bioplex" latest_bioplex <- omnipath_cache_latest_or_new( names(omnipath_cache_search('bioplex'))[1] ) latest_bioplex$dl_finished # [1] "2021-03-09 14:28:50 CET" latest_bioplex$path # [1] "/home/denes/.cache/OmnipathR/378e0def2ac97985f629-1.rds" ## End(Not run) # create an example cache record bioc_url <- 'https://bioconductor.org/' version <- omnipath_cache_latest_or_new(url = bioc_url) omnipath_cache_remove(url = bioc_url) # cleaning up
Finds the most recent version in a cache record
omnipath_cache_latest_version(record)omnipath_cache_latest_version(record)
record |
A cache record |
Character: the version ID with the most recent download finished time
Loads the object from RDS format.
omnipath_cache_load( key = NULL, version = NULL, url = NULL, post = NULL, payload = NULL )omnipath_cache_load( key = NULL, version = NULL, url = NULL, post = NULL, payload = NULL )
key |
Key of the cache item |
version |
Version of the cache item. If does not exist or NULL, the latest version will be retrieved |
url |
URL of the downloaded resource |
post |
HTTP POST parameters as a list |
payload |
HTTP data payload |
Object loaded from the cache RDS file.
url <- paste0( 'https://omnipathdb.org/intercell?resources=Adhesome,Almen2009,', 'Baccin2019,CSPA,CellChatDB&license=academic' ) result <- read.delim(url, sep = '\t') omnipath_cache_save(result, url = url) # works only if you have already this item in the cache intercell_data <- omnipath_cache_load(url = url) class(intercell_data) # [1] "data.frame" nrow(intercell_data) # [1] 16622 attr(intercell_data, 'origin') # [1] "cache" # basic example of saving and loading to and from the cache: bioc_url <- 'https://bioconductor.org/' bioc_html <- readChar(url(bioc_url), nchars = 99999) omnipath_cache_save(bioc_html, url = bioc_url) bioc_html <- omnipath_cache_load(url = bioc_url)url <- paste0( 'https://omnipathdb.org/intercell?resources=Adhesome,Almen2009,', 'Baccin2019,CSPA,CellChatDB&license=academic' ) result <- read.delim(url, sep = '\t') omnipath_cache_save(result, url = url) # works only if you have already this item in the cache intercell_data <- omnipath_cache_load(url = url) class(intercell_data) # [1] "data.frame" nrow(intercell_data) # [1] 16622 attr(intercell_data, 'origin') # [1] "cache" # basic example of saving and loading to and from the cache: bioc_url <- 'https://bioconductor.org/' bioc_html <- readChar(url(bioc_url), nchars = 99999) omnipath_cache_save(bioc_html, url = bioc_url) bioc_html <- omnipath_cache_load(url = bioc_url)
Either the key or the URL (with POST and payload) must be provided.
omnipath_cache_move_in( path, key = NULL, version = NULL, url = NULL, post = NULL, payload = NULL, keep_original = FALSE )omnipath_cache_move_in( path, key = NULL, version = NULL, url = NULL, post = NULL, payload = NULL, keep_original = FALSE )
path |
Path to the source file |
key |
Key of the cache item |
version |
Version of the cache item. If does not exist a new version item will be created |
url |
URL of the downloaded resource |
post |
HTTP POST parameters as a list |
payload |
HTTP data payload |
keep_original |
Whether to keep or remove the original file |
Character: invisibly returns the version number of the cache version item.
path <- tempfile() saveRDS(rnorm(100), file = path) omnipath_cache_move_in(path, url = 'the_download_address') # basic example of moving a file to the cache: bioc_url <- 'https://bioconductor.org/' html_file <- tempfile(fileext = '.html') httr::GET(bioc_url, httr::write_disk(html_file, overwrite = TRUE)) omnipath_cache_move_in(path = html_file, url = bioc_url) omnipath_cache_remove(url = bioc_url) # cleaning uppath <- tempfile() saveRDS(rnorm(100), file = path) omnipath_cache_move_in(path, url = 'the_download_address') # basic example of moving a file to the cache: bioc_url <- 'https://bioconductor.org/' html_file <- tempfile(fileext = '.html') httr::GET(bioc_url, httr::write_disk(html_file, overwrite = TRUE)) omnipath_cache_move_in(path = html_file, url = bioc_url) omnipath_cache_remove(url = bioc_url) # cleaning up
According to the parameters, it can remove contents older than a certain age, or contents having a more recent version, one specific item, or wipe the entire cache.
omnipath_cache_remove(key = NULL, url = NULL, post = NULL, payload = NULL, max_age = NULL, min_age = NULL, status = NULL, only_latest = FALSE, wipe = FALSE, autoclean = TRUE)omnipath_cache_remove(key = NULL, url = NULL, post = NULL, payload = NULL, max_age = NULL, min_age = NULL, status = NULL, only_latest = FALSE, wipe = FALSE, autoclean = TRUE)
key |
The key of the cache record |
url |
URL pointing to the resource |
post |
HTTP POST parameters as a list |
payload |
HTTP data payload |
max_age |
Age of cache items in days. Remove everything that is older than this age |
min_age |
Age of cache items in days. Remove everything more recent than this age |
status |
Remove items having any of the states listed here |
only_latest |
Keep only the latest version |
wipe |
Logical: if TRUE, removes all files from the cache and the
cache database. Same as calling |
autoclean |
Remove the entries about failed downloads, the files in the cache directory which are missing from the cache database, and the entries without existing files in the cache directory |
Invisibly returns the cache database (list of cache records).
## Not run: # remove all cache data from the BioPlex database cache_records <- omnipath_cache_search( 'bioplex', ignore.case = TRUE ) omnipath_cache_remove(names(cache_records)) # remove a record by its URL regnetwork_url <- 'http://www.regnetworkweb.org/download/human.zip' omnipath_cache_remove(url = regnetwork_url) # remove all records older than 30 days omnipath_cache_remove(max_age = 30) # for each record, remove all versions except the latest omnipath_cache_remove(only_latest = TRUE) ## End(Not run) bioc_url <- 'https://bioconductor.org/' version <- omnipath_cache_latest_or_new(url = bioc_url) httr::GET(bioc_url, httr::write_disk(version$path, overwrite = TRUE)) omnipath_cache_download_ready(version) key <- omnipath_cache_key(bioc_url) omnipath_cache_remove(key = key)## Not run: # remove all cache data from the BioPlex database cache_records <- omnipath_cache_search( 'bioplex', ignore.case = TRUE ) omnipath_cache_remove(names(cache_records)) # remove a record by its URL regnetwork_url <- 'http://www.regnetworkweb.org/download/human.zip' omnipath_cache_remove(url = regnetwork_url) # remove all records older than 30 days omnipath_cache_remove(max_age = 30) # for each record, remove all versions except the latest omnipath_cache_remove(only_latest = TRUE) ## End(Not run) bioc_url <- 'https://bioconductor.org/' version <- omnipath_cache_latest_or_new(url = bioc_url) httr::GET(bioc_url, httr::write_disk(version$path, overwrite = TRUE)) omnipath_cache_download_ready(version) key <- omnipath_cache_key(bioc_url) omnipath_cache_remove(key = key)
Exports the object in RDS format, creates new cache record if necessary.
omnipath_cache_save( data, key = NULL, version = NULL, url = NULL, post = NULL, payload = NULL )omnipath_cache_save( data, key = NULL, version = NULL, url = NULL, post = NULL, payload = NULL )
data |
An object |
key |
Key of the cache item |
version |
Version of the cache item. If does not exist a new version item will be created |
url |
URL of the downloaded resource |
post |
HTTP POST parameters as a list |
payload |
HTTP data payload |
Returns invisibly the data itself.
Invisibly returns the 'data'.
mydata <- data.frame(a = c(1, 2, 3), b = c('a', 'b', 'c')) omnipath_cache_save(mydata, url = 'some_dummy_address') from_cache <- omnipath_cache_load(url = 'some_dummy_address') from_cache # a b # 1 1 a # 2 2 b # 3 3 c attr(from_cache, 'origin') # [1] "cache" # basic example of saving and loading to and from the cache: bioc_url <- 'https://bioconductor.org/' bioc_html <- readChar(url(bioc_url), nchars = 99999) omnipath_cache_save(bioc_html, url = bioc_url) bioc_html <- omnipath_cache_load(url = bioc_url)mydata <- data.frame(a = c(1, 2, 3), b = c('a', 'b', 'c')) omnipath_cache_save(mydata, url = 'some_dummy_address') from_cache <- omnipath_cache_load(url = 'some_dummy_address') from_cache # a b # 1 1 a # 2 2 b # 3 3 c attr(from_cache, 'origin') # [1] "cache" # basic example of saving and loading to and from the cache: bioc_url <- 'https://bioconductor.org/' bioc_html <- readChar(url(bioc_url), nchars = 99999) omnipath_cache_save(bioc_html, url = bioc_url) bioc_html <- omnipath_cache_load(url = bioc_url)
Searches the cache records by matching the URL against a string or regexp.
omnipath_cache_search(pattern, ...)omnipath_cache_search(pattern, ...)
pattern |
String or regular expression. |
... |
Passed to |
List of cache records matching the pattern.
# find all cache records from the BioPlex database bioplex_cache_records <- omnipath_cache_search( 'bioplex', ignore.case = TRUE )# find all cache records from the BioPlex database bioplex_cache_records <- omnipath_cache_search( 'bioplex', ignore.case = TRUE )
Sets the file extension for a cache record
omnipath_cache_set_ext(key, ext)omnipath_cache_set_ext(key, ext)
key |
Character: key for a cache item, alternatively a version entry. |
ext |
Character: the file extension, e.g. "zip". |
Returns 'NULL'.
bioc_url <- 'https://bioconductor.org/' version <- omnipath_cache_latest_or_new(url = bioc_url) version$path # [1] "/home/denes/.cache/OmnipathR/41346a00fb20d2a9df03-1" httr::GET(bioc_url, httr::write_disk(version$path, overwrite = TRUE)) key <- omnipath_cache_key(url = bioc_url) omnipath_cache_set_ext(key = key, ext = 'html') version <- omnipath_cache_latest_or_new(url = bioc_url) version$path # [1] "/home/denes/.cache/OmnipathR/41346a00fb20d2a9df03-1.html" record <- omnipath_cache_get(url = bioc_url) record$ext # [1] "html" omnipath_cache_remove(url = bioc_url) # cleaning upbioc_url <- 'https://bioconductor.org/' version <- omnipath_cache_latest_or_new(url = bioc_url) version$path # [1] "/home/denes/.cache/OmnipathR/41346a00fb20d2a9df03-1" httr::GET(bioc_url, httr::write_disk(version$path, overwrite = TRUE)) key <- omnipath_cache_key(url = bioc_url) omnipath_cache_set_ext(key = key, ext = 'html') version <- omnipath_cache_latest_or_new(url = bioc_url) version$path # [1] "/home/denes/.cache/OmnipathR/41346a00fb20d2a9df03-1.html" record <- omnipath_cache_get(url = bioc_url) record$ext # [1] "html" omnipath_cache_remove(url = bioc_url) # cleaning up
Updates the status of an existing cache record
omnipath_cache_update_status(key, version, status, dl_finished = NULL)omnipath_cache_update_status(key, version, status, dl_finished = NULL)
key |
Key of the cache item |
version |
Version of the cache item. If does not exist a new version item will be created |
status |
The updated status value |
dl_finished |
Timestamp for the time when download was finished, if 'NULL' the value remains unchanged |
Character: invisibly returns the version number of the cache version item.
bioc_url <- 'https://bioconductor.org/' latest_version <- omnipath_cache_latest_or_new(url = bioc_url) key <- omnipath_cache_key(bioc_url) omnipath_cache_update_status( key = key, version = latest_version$number, status = 'ready', dl_finished = Sys.time() ) omnipath_cache_remove(url = bioc_url) # cleaning upbioc_url <- 'https://bioconductor.org/' latest_version <- omnipath_cache_latest_or_new(url = bioc_url) key <- omnipath_cache_key(bioc_url) omnipath_cache_update_status( key = key, version = latest_version$number, status = 'ready', dl_finished = Sys.time() ) omnipath_cache_remove(url = bioc_url) # cleaning up
After this operation the cache directory will be completely empty, except an empty cache database file.
omnipath_cache_wipe(...)omnipath_cache_wipe(...)
... |
Ignored. |
Returns 'NULL'.
## Not run: omnipath_cache_wipe() # the cache is completely empty: print(omnipathr.env$cache) # list() list.files(omnipath_get_cachedir()) # [1] "cache.json" ## End(Not run)## Not run: omnipath_cache_wipe() # the cache is completely empty: print(omnipathr.env$cache) # list() list.files(omnipath_get_cachedir()) # [1] "cache.json" ## End(Not run)
Current config file path of OmnipathR
Current config file path for a certain package
omnipath_config_path(user = FALSE) config_path(user = FALSE, pkg = "OmnipathR")omnipath_config_path(user = FALSE) config_path(user = FALSE, pkg = "OmnipathR")
user |
Logical: prioritize the user level config even if a config in the current working directory is available. |
pkg |
Character: name of the package. |
Character: path to the config file.
omnipath_config_path()omnipath_config_path()
OmniPath PPI for the COSMOS PKN
omnipath_for_cosmos( organism = 9606L, resources = NULL, datasets = NULL, interaction_types = NULL, id_types = c("uniprot", "genesymbol"), ... )omnipath_for_cosmos( organism = 9606L, resources = NULL, datasets = NULL, interaction_types = NULL, id_types = c("uniprot", "genesymbol"), ... )
organism |
Character or integer: name or NCBI Taxonomy ID of the organism. |
resources |
Character: names of one or more resources. Correct spelling is important. |
datasets |
Character: one or more network datasets in OmniPath. |
interaction_types |
Character: one or more interaction type |
id_types |
Character: translate the protein identifiers to these ID types. Each ID type results two extra columns in the output, for the "source" and "target" sides of the interaction, respectively. The default ID type for proteins is Esembl Gene ID, and by default UniProt IDs and Gene Symbols are included. The UniProt IDs returned by the web service are left intact, while the Gene Symbols are queried from Ensembl. These Gene Symbols are different from the ones returned from the web service, and match the Ensembl Gene Symbols used by other components of the COSMOS PKN. |
... |
Further parameters to |
Data frame with the columns source, target and sign.
op_cosmos <- omnipath_for_cosmos() op_cosmosop_cosmos <- omnipath_for_cosmos() op_cosmos
Load the package configuration from a config file
Load the coniguration of a certain package
omnipath_load_config(path = NULL, title = "default", user = FALSE, ...) load_config( path = NULL, title = "default", user = FALSE, pkg = "OmnipathR", ... )omnipath_load_config(path = NULL, title = "default", user = FALSE, ...) load_config( path = NULL, title = "default", user = FALSE, pkg = "OmnipathR", ... )
path |
Path to the config file. |
title |
Load the config under this title. One config file might contain multple configurations, each identified by a title. If the title is not available the first section of the config file will be used. |
user |
Force to use the user level config even if a config file exists in the current directory. By default, the local config files have prioroty over the user level config. |
... |
Passed to |
pkg |
Character: name of the package |
Invisibly returns the config as a list.
## Not run: # load the config from a custom config file: omnipath_load_config(path = 'my_custom_omnipath_config.yml') ## End(Not run)## Not run: # load the config from a custom config file: omnipath_load_config(path = 'my_custom_omnipath_config.yml') ## End(Not run)
Browse the current OmnipathR log file
Browse the latest log from a package
omnipath_log() read_log(pkg = "OmnipathR")omnipath_log() read_log(pkg = "OmnipathR")
pkg |
Character: name of a package. |
Returns 'NULL'.
## Not run: omnipath_log() # then you can browse the log file, and exit with `q` ## End(Not run)## Not run: omnipath_log() # then you can browse the log file, and exit with `q` ## End(Not run)
Path to the current OmnipathR log file
Path to the current logfile of a package
omnipath_logfile() logfile(pkg = "OmnipathR")omnipath_logfile() logfile(pkg = "OmnipathR")
pkg |
Character: name of a package. |
Character: path to the current logfile, or NULL if no
logfile is available.
omnipath_logfile() # [1] "/home/denes/omnipathr/omnipathr-log/omnipathr-20210309-1642.log"omnipath_logfile() # [1] "/home/denes/omnipathr/omnipathr-log/omnipathr-20210309-1642.log"
Any package or script can easily send log messages and establish a logging facility with the fantastic 'logger' package. This function serves the only purpose if you want to inject messages into the logger of OmnipathR. Otherwise we recommend to use the 'logger' package directly.
omnipath_msg(level, ...)omnipath_msg(level, ...)
level |
Character, numeric or class loglevel. A log level, if character one of the followings: "fatal", "error", "warn", "success", "info", "trace". |
... |
Arguments for string formatting, passed |
Returns 'NULL'.
omnipath_msg( level = 'success', 'Talking to you in the name of OmnipathR, my favourite number is %d', round(runif(1, 1, 10)) )omnipath_msg( level = 'success', 'Talking to you in the name of OmnipathR, my favourite number is %d', round(runif(1, 1, 10)) )
This is the most generic method for accessing data from the OmniPath web service. All other functions retrieving data from OmniPath call this function with various parameters. In general, every query can retrieve data in tabular or JSON format, the tabular (data frame) being the default.
omnipath_query( query_type, organism = 9606L, resources = NULL, datasets = NULL, types = NULL, genesymbols = "yes", fields = NULL, default_fields = TRUE, silent = FALSE, logicals = NULL, download_args = list(), format = "data.frame", references_by_resource = TRUE, add_counts = TRUE, license = NULL, password = NULL, exclude = NULL, json_param = list(), strict_evidences = FALSE, genesymbol_resource = "UniProt", cache = NULL, ... )omnipath_query( query_type, organism = 9606L, resources = NULL, datasets = NULL, types = NULL, genesymbols = "yes", fields = NULL, default_fields = TRUE, silent = FALSE, logicals = NULL, download_args = list(), format = "data.frame", references_by_resource = TRUE, add_counts = TRUE, license = NULL, password = NULL, exclude = NULL, json_param = list(), strict_evidences = FALSE, genesymbol_resource = "UniProt", cache = NULL, ... )
query_type |
Character: "interactions", "enzsub", "complexes", "annotations", or "intercell". |
organism |
Character or integer: name or NCBI Taxonomy ID of the organism. OmniPath is built of human data, and the web service provides orthology translated interactions and enzyme-substrate relationships for mouse and rat. For other organisms and query types, orthology translation will be called automatically on the downloaded human data before returning the result. |
resources |
Character vector: name of one or more resources. Restrict the data to these resources. For a complete list of available resources, call the '<query_type>_resources' functions for the query type of interst. |
datasets |
Character vector: name of one or more datasets. In the interactions query type a number of datasets are available. The default is caled "omnipath", and corresponds to the curated causal signaling network published in the 2016 OmniPath paper. |
types |
Character vector: one or more interaction types, such as "transcriptional" or "post_translational". For a full list of interaction types see 'query_info("interaction")$types'. |
genesymbols |
Character or logical: TRUE or FALS or "yes" or "no". Include the 'genesymbols' column in the results. OmniPath uses UniProt IDs as the primary identifiers, gene symbols are optional. |
fields |
Character vector: additional fields to include in the result. For a list of available fields, call 'query_info("interactions")'. |
default_fields |
Logical: if TRUE, the default fields will be included. |
silent |
Logical: if TRUE, no messages will be printed. By default a summary message is printed upon successful download. |
logicals |
Character vector: fields to be cast to logical. |
download_args |
List: parameters to pass to the download function, which is 'readr::read_tsv' by default, and 'jsonlite::safe_load'. |
format |
Character: if "json", JSON will be retrieved and processed into a nested list; any other value will return data frame. |
references_by_resource |
Logical: if TRUE,, in the 'references' column the PubMed IDs will be prefixed with the names of the resources they are coming from. If FALSE, the 'references' column will be a list of unique PubMed IDs. |
add_counts |
Logical: if TRUE, the number of references and number of resources for each record will be added to the result. |
license |
Character: license restrictions. By default, data from resources allowing "academic" use is returned by OmniPath. If you use the data for work in a company, you can provide "commercial" or "for-profit", which will restrict the data to those records which are supported by resources that allow for-profit use. |
password |
Character: password for the OmniPath web service. You can provide a special password here which enables the use of 'license = "ignore"' option, completely bypassing the license filter. |
exclude |
Character vector: resource or dataset names to be excluded. The data will be filtered after download to remove records of the excluded datasets and resources. |
json_param |
List: parameters to pass to the 'jsonlite::fromJSON' when processing JSON columns embedded in the downloaded data. Such columns are "extra_attrs" and "evidences". These are optional columns which provide a lot of extra details about interactions. |
strict_evidences |
Logical: reconstruct the "sources" and "references" columns of interaction data frames based on the "evidences" column, strictly filtering them to the queried datasets and resources. Without this, the "sources" and "references" fields for each record might contain information for datasets and resources other than the queried ones, because the downloaded records are a result of a simple filtering of an already integrated data frame. |
genesymbol_resource |
Character: "uniprot" (default) or "ensembl". The OmniPath web service uses the primary gene symbols as provided by UniProt. By passing "ensembl" here, the UniProt gene symbols will be replaced by the ones used in Ensembl. This translation results in a loss of a few records, and multiplication of another few records due to ambiguous translation. |
cache |
Logical: use caching, load data from and save to the. The cache
directory by default belongs to the user, located in the user's default
cache directory, and named "OmnipathR". Find out about it by
|
... |
Additional parameters for the OmniPath web service. These
parameters will be processed, validated and included in the query
string. Many parameters are already explicitly set by the arguments
above. A number of query type specific parameters are also available,
learn more about these by the |
Data frame (tibble) or list: the data returned by the OmniPath web service (or loaded from cache), after processing. Nested list if the "format" parameter is "json", otherwise a tibble.
interaction_data <- omnipath_query("interaction", datasets = "omnipath") interaction_datainteraction_data <- omnipath_query("interaction", datasets = "omnipath") interaction_data
Save the current package configuration
Save the configuration of a certain package
omnipath_save_config(path = NULL, title = "default", local = FALSE) save_config(path = NULL, title = "default", local = FALSE, pkg = "OmnipathR")omnipath_save_config(path = NULL, title = "default", local = FALSE) save_config(path = NULL, title = "default", local = FALSE, pkg = "OmnipathR")
path |
Path to the config file. Directories and the file will be created if don't exist. |
title |
Save the config under this title. One config file might contain multiple configurations, each identified by a title. |
local |
Save into a config file in the current directory instead of a user level config file. When loading, the config in the current directory has priority over the user level config. |
pkg |
Character: name of the package |
Returns 'NULL'.
## Not run: # after this, all downloads will default to commercial licenses # i.e. the resources that allow only academic use will be excluded: options(omnipathr.license = 'commercial') omnipath_save_config() ## End(Not run)## Not run: # after this, all downloads will default to commercial licenses # i.e. the resources that allow only academic use will be excluded: options(omnipathr.license = 'commercial') omnipath_save_config() ## End(Not run)
Change the cache directory
omnipath_set_cachedir(path = NULL)omnipath_set_cachedir(path = NULL)
path |
Character: path to the new cache directory. If don't exist,
the directories will be created. If the path is an existing cache
directory, the package's cache database for the current session will
be loaded from the database in the directory. If |
Returns NULL.
tmp_cache <- tempdir() omnipath_set_cachedir(tmp_cache) # restore the default cache directory: omnipath_set_cachedir()tmp_cache <- tempdir() omnipath_set_cachedir(tmp_cache) # restore the default cache directory: omnipath_set_cachedir()
Use this method to change during a session which messages you want to be printed on the console. Before loading the package, you can set it also by the config file, with the omnipathr.console_loglevel key.
omnipath_set_console_loglevel(level)omnipath_set_console_loglevel(level)
level |
Character or class 'loglevel'. The desired log level. |
Returns 'NULL'.
omnipath_set_console_loglevel('warn') # or: omnipath_set_console_loglevel(logger::WARN)omnipath_set_console_loglevel('warn') # or: omnipath_set_console_loglevel(logger::WARN)
Use this method to change during a session which messages you want to be written into the logfile. Before loading the package, you can set it also by the config file, with the "omnipathr.loglevel" key.
omnipath_set_logfile_loglevel(level)omnipath_set_logfile_loglevel(level)
level |
Character or class 'loglevel'. The desired log level. |
Returns 'NULL'.
omnipath_set_logfile_loglevel('info') # or: omnipath_set_logfile_loglevel(logger::INFO)omnipath_set_logfile_loglevel('info') # or: omnipath_set_logfile_loglevel(logger::INFO)
Sets the log level for the package logger
Sets the log level for any package
omnipath_set_loglevel(level, target = "logfile") set_loglevel(level, target = "logfile", pkg = "OmnipathR")omnipath_set_loglevel(level, target = "logfile") set_loglevel(level, target = "logfile", pkg = "OmnipathR")
level |
Character or class 'loglevel'. The desired log level. |
target |
Character, either 'logfile' or 'console' |
pkg |
Character: name of the package. |
Returns 'NULL'.
omnipath_set_loglevel(logger::FATAL, target = 'console')omnipath_set_loglevel(logger::FATAL, target = 'console')
Databases are resources which might be costly to load but can be used many times by functions which usually automatically load and retrieve them from the database manager. Each database has a lifetime and will be unloaded automatically upon expiry.
omnipath_show_db()omnipath_show_db()
A data frame with the built in database definitions.
database_definitions <- omnipath_show_db() database_definitions # # A tibble: 14 x 10 # name last_used lifetime package loader loader_p. # <chr> <dttm> <dbl> <chr> <chr> <list> # 1 Gene Onto. 2021-04-04 20:19:15 300 Omnipat. go_ontol. <named l. # 2 Gene Onto. NA 300 Omnipat. go_ontol. <named l. # 3 Gene Onto. NA 300 Omnipat. go_ontol. <named l. # 4 Gene Onto. NA 300 Omnipat. go_ontol. <named l. # 5 Gene Onto. NA 300 Omnipat. go_ontol. <named l. # ... (truncated) # # . with 4 more variables: latest_param <list>, loaded <lgl>, db <list>, # # key <chr>database_definitions <- omnipath_show_db() database_definitions # # A tibble: 14 x 10 # name last_used lifetime package loader loader_p. # <chr> <dttm> <dbl> <chr> <chr> <list> # 1 Gene Onto. 2021-04-04 20:19:15 300 Omnipat. go_ontol. <named l. # 2 Gene Onto. NA 300 Omnipat. go_ontol. <named l. # 3 Gene Onto. NA 300 Omnipat. go_ontol. <named l. # 4 Gene Onto. NA 300 Omnipat. go_ontol. <named l. # 5 Gene Onto. NA 300 Omnipat. go_ontol. <named l. # ... (truncated) # # . with 4 more variables: latest_param <list>, loaded <lgl>, db <list>, # # key <chr>
A lock file in the cache directory avoids simulatneous write and read. It's supposed to be removed after each read and write operation. This might not happen if the process crashes during such an operation. In this case you can manually call this function.
omnipath_unlock_cache_db()omnipath_unlock_cache_db()
Logical: returns TRUE if the cache was locked and now is unlocked; FALSE if it was not locked.
omnipath_unlock_cache_db()omnipath_unlock_cache_db()
The functions listed here all download pairwise, causal molecular interactions from the https://omnipathdb.org/interactions endpoint of the OmniPath web service. They are different only in the type of interactions and the kind of resources and data they have been compiled from. A complete list of these functions is available below, these cover the interaction datasets and types currently available in OmniPath:
Interactions from the https://omnipathdb.org/interactions endpoint of the OmniPath web service. By default, it downloads only the "omnipath" dataset, which corresponds to the curated causal interactions described in Turei et al. 2016.
Imports interactions from the 'omnipath' dataset of OmniPath, a dataset that inherits most of its design and contents from the original OmniPath core from the 2016 publication. This dataset consists of about 40k interactions.
Imports the dataset from: https://omnipathdb.org/interactions?datasets=pathwayextra, which contains activity flow interactions without literature reference. The activity flow interactions supported by literature references are part of the 'omnipath' dataset.
Imports the dataset from: https://omnipathdb.org/interactions?datasets=kinaseextra, which contains enzyme-substrate interactions without literature reference. The enzyme-substrate interactions supported by literature references are part of the 'omnipath' dataset.
Imports the dataset from: https://omnipathdb.org/interactions?datasets=ligrecextra, which contains ligand-receptor interactions without literature reference. The ligand-receptor interactions supported by literature references are part of the 'omnipath' dataset.
Imports interactions from all post-translational datasets of OmniPath. The datasets are "omnipath", "kinaseextra", "pathwayextra" and "ligrecextra".
Imports the dataset from: https://omnipathdb.org/interactions?datasets=dorothea which contains transcription factor (TF)-target interactions from DoRothEA https://github.com/saezlab/DoRothEA DoRothEA is a comprehensive resource of transcriptional regulation, consisting of 16 original resources, in silico TFBS prediction, gene expression signatures and ChIP-Seq binding site analysis.
Imports the dataset from: https://omnipathdb.org/interactions?datasets=tf_target, which contains transcription factor-target protein coding gene interactions. Note: this is not the only TF-target dataset in OmniPath, 'dorothea' is the other one and the 'tf_mirna' dataset provides TF-miRNA gene interactions.
Imports the dataset from: https://omnipathdb.org/interactions?datasets=tf_target,dorothea, which contains transcription factor-target protein coding gene interactions.
CollecTRI is a comprehensive resource of transcriptional regulation, published in 2023, consisting of 14 resources and original literature curation.
Imports the dataset from: https://omnipathdb.org/interactions?datasets=mirnatarget, which contains miRNA-mRNA interactions.
Imports the dataset from: https://omnipathdb.org/interactions?datasets=tf_mirna, which contains transcription factor-miRNA gene interactions
Imports the dataset from: https://omnipathdb.org/interactions?datasets=lncrna_mrna, which contains lncRNA-mRNA interactions
Imports the dataset from: https://omnipathdb.org/interactions?datasets=small_molecule, which contains small molecule-protein interactions. Small molecules can be metabolites, intrinsic ligands or drug compounds.
omnipath_interactions(...) omnipath(...) pathwayextra(...) kinaseextra(...) ligrecextra(...) post_translational(...) dorothea(dorothea_levels = c("A", "B"), ...) tf_target(...) transcriptional(dorothea_levels = c("A", "B"), ...) collectri(...) mirna_target(...) tf_mirna(...) lncrna_mrna(...) small_molecule(...) all_interactions( dorothea_levels = c("A", "B"), types = NULL, fields = NULL, exclude = NULL, ... )omnipath_interactions(...) omnipath(...) pathwayextra(...) kinaseextra(...) ligrecextra(...) post_translational(...) dorothea(dorothea_levels = c("A", "B"), ...) tf_target(...) transcriptional(dorothea_levels = c("A", "B"), ...) collectri(...) mirna_target(...) tf_mirna(...) lncrna_mrna(...) small_molecule(...) all_interactions( dorothea_levels = c("A", "B"), types = NULL, fields = NULL, exclude = NULL, ... )
... |
Arguments passed on to
|
dorothea_levels |
The confidence levels of the dorothea interactions (TF-target) which range from A to D. Set to A and B by default. |
types |
Character: interaction types, such as "transcriptional", "post_transcriptional", "post_translational", etc. |
fields |
Character: additional fields (columns) to be included in the
result. For a list of available fields, see |
exclude |
Character: names of datasets or resource to be excluded from
the result. By deafult, the records supported by only these resources or
datasets will be removed from the output. If |
Post-translational (protein-protein, PPI) interactions
omnipath: the OmniPath data as defined in the 2016 paper,
an arbitrary optimum between coverage and quality. This dataset
contains almost entirely causal (stimulatory or inhibitory; i.e.
activity flow , according to the SBGN standard), physical
interactions between pairs of proteins, curated by experts
from the literature.
pathwayextra: activity flow interactions without literature
references.
kinaseextra: enzyme-substrate interactions without
literature references.
ligrecextra: ligand-receptor interactions without
literature references.
post_translational: all post-translational
(protein-protein, PPI) interactions; this is the combination of the
omnipath, pathwayextra, kinaseextra and ligrecextra
datasets.
TF-target (gene regulatory, GRN) interactions
collectri: transcription factor (TF)-target
interactions from CollecTRI.
dorothea: transcription factor (TF)-target
interactions from DoRothEA
tf_target: transcription factor
(TF)-target interactions from other resources
transcriptional: all transcription factor
(TF)-target interactions; this is the combination of the
collectri, dorothea and tf_target datasets.
Post-transcriptional (miRNA-target) and other RNA related interactions
In these datasets we intend to collect the literature curated resources, hence we don't include some of the most well known large databases if those are based on predictions or high-throughput assays.
mirna_target: miRNA-mRNA interactions
tf_mirna: TF-miRNA interactions
lncrna_mrna: lncRNA-mRNA interactions
Other interaction access functions
small_molecule: interactions between small molecules and
proteins. Currently this is a small, experimental dataset that
includes drug-target, ligand-receptor, enzyme-metabolite and other
interactions. In the future this will be largely expanded and
divided into multiple datasets.
all_interactions: all the interaction datasets combined.
A dataframe of molecular interactions.
A dataframe of literature curated, post-translational signaling interactions.
A dataframe containing activity flow interactions between proteins without literature reference
A dataframe containing enzyme-substrate interactions without literature reference
A dataframe containing ligand-receptor interactions including the ones without literature references
A dataframe containing post-translational interactions
A data frame of TF-target interactions from DoRothEA.
A dataframe containing TF-target interactions
A dataframe containing TF-target interactions.
A dataframe of TF-target interactions.
A dataframe containing miRNA-mRNA interactions
A dataframe containing TF-miRNA interactions
A dataframe containing lncRNA-mRNA interactions
A dataframe of small molecule-protein interactions
A dataframe containing all the datasets in the interactions query
op <- omnipath(resources = c("CA1", "SIGNOR", "SignaLink3")) op interactions = omnipath_interactions( resources = "SignaLink3", organism = 9606 ) pathways <- omnipath() pathways interactions <- pathwayextra( resources = c("BioGRID", "IntAct"), organism = 9606 ) kinase_substrate <- kinaseextra( resources = c('PhosphoPoint', 'PhosphoSite'), organism = 9606 ) ligand_receptor <- ligrecextra( resources = c('HPRD', 'Guide2Pharma'), organism = 9606 ) interactions <- post_translational(resources = "BioGRID") dorothea_grn <- dorothea( resources = c('DoRothEA', 'ARACNe-GTEx_DoRothEA'), organism = 9606, dorothea_levels = c('A', 'B', 'C') ) dorothea_grn interactions <- tf_target(resources = c("DoRothEA", "SIGNOR")) grn <- transcriptional(resources = c("PAZAR", "ORegAnno", "DoRothEA")) grn collectri_grn <- collectri() collectri_grn interactions <- mirna_target( resources = c("miRTarBase", "miRecords")) interactions <- tf_mirna(resources = "TransmiR") interactions <- lncrna_mrna(resources = c("ncRDeathDB")) # What are the targets of aspirin? interactions <- small_molecule(sources = "ASPIRIN") # The prostaglandin synthases: interactions interactions <- all_interactions( resources = c("HPRD", "BioGRID"), organism = 9606 )op <- omnipath(resources = c("CA1", "SIGNOR", "SignaLink3")) op interactions = omnipath_interactions( resources = "SignaLink3", organism = 9606 ) pathways <- omnipath() pathways interactions <- pathwayextra( resources = c("BioGRID", "IntAct"), organism = 9606 ) kinase_substrate <- kinaseextra( resources = c('PhosphoPoint', 'PhosphoSite'), organism = 9606 ) ligand_receptor <- ligrecextra( resources = c('HPRD', 'Guide2Pharma'), organism = 9606 ) interactions <- post_translational(resources = "BioGRID") dorothea_grn <- dorothea( resources = c('DoRothEA', 'ARACNe-GTEx_DoRothEA'), organism = 9606, dorothea_levels = c('A', 'B', 'C') ) dorothea_grn interactions <- tf_target(resources = c("DoRothEA", "SIGNOR")) grn <- transcriptional(resources = c("PAZAR", "ORegAnno", "DoRothEA")) grn collectri_grn <- collectri() collectri_grn interactions <- mirna_target( resources = c("miRTarBase", "miRecords")) interactions <- tf_mirna(resources = "TransmiR") interactions <- lncrna_mrna(resources = c("ncRDeathDB")) # What are the targets of aspirin? interactions <- small_molecule(sources = "ASPIRIN") # The prostaglandin synthases: interactions interactions <- all_interactions( resources = c("HPRD", "BioGRID"), organism = 9606 )
OmnipathR is an R package built to provide easy access to the data stored in the OmniPath web service:
And a number of other resources, such as BioPlex, ConsensusPathDB, EVEX, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011.
The OmniPath web service implements a very simple REST style API. This package make requests by the HTTP protocol to retreive the data. Hence, fast Internet access is required for a propser use of OmnipathR.
The package also provides some utility functions to filter, analyse and visualize the data. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation nichenetr (available in CRAN).
Alberto Valdeolivas <alvaldeolivas@gmail> and Denes Turei <[email protected]> and Attila Gabor <[email protected]>
Useful links:
## Not run: # Download post-translational modifications: enzsub <- enzyme_substrate(resources = c("PhosphoSite", "SIGNOR")) # Download protein-protein interactions interactions <- omnipath(resources = "SignaLink3") # Convert to igraph objects: enzsub_g <- enzsub_graph(enzsub = enzsub) OPI_g <- interaction_graph(interactions = interactions) # Print some interactions: print_interactions(head(enzsub)) # interactions with references: print_interactions(tail(enzsub), writeRefs = TRUE) # find interactions between kinase and substrate: print_interactions(dplyr::filter(ptms,enzyme_genesymbol=="MAP2K1", substrate_genesymbol=="MAPK3")) # find shortest paths on the directed network between proteins print_path_es(shortest_paths(OPI_g, from = "TYRO3", to = "STAT3", output = 'epath')$epath[[1]], OPI_g) # find all shortest paths between proteins print_path_vs( all_shortest_paths( enzsub_g, from = "SRC", to = "STAT1" )$res, enzsub_g ) ## End(Not run)## Not run: # Download post-translational modifications: enzsub <- enzyme_substrate(resources = c("PhosphoSite", "SIGNOR")) # Download protein-protein interactions interactions <- omnipath(resources = "SignaLink3") # Convert to igraph objects: enzsub_g <- enzsub_graph(enzsub = enzsub) OPI_g <- interaction_graph(interactions = interactions) # Print some interactions: print_interactions(head(enzsub)) # interactions with references: print_interactions(tail(enzsub), writeRefs = TRUE) # find interactions between kinase and substrate: print_interactions(dplyr::filter(ptms,enzyme_genesymbol=="MAP2K1", substrate_genesymbol=="MAPK3")) # find shortest paths on the directed network between proteins print_path_es(shortest_paths(OPI_g, from = "TYRO3", to = "STAT3", output = 'epath')$epath[[1]], OPI_g) # find all shortest paths between proteins print_path_vs( all_shortest_paths( enzsub_g, from = "SRC", to = "STAT1" )$res, enzsub_g ) ## End(Not run)
Recreate interaction data frame based on certain datasets and resources
only_from( data, datasets = NULL, resources = NULL, exclude = NULL, .keep = FALSE )only_from( data, datasets = NULL, resources = NULL, exclude = NULL, .keep = FALSE )
data |
An interaction data frame from the OmniPath web service with evidences column. |
datasets |
Character: a vector of dataset labels. Only evidences from these datasets will be used. |
resources |
Character: a vector of resource labels. Only evidences from these resources will be used. |
exclude |
Character vector of resource names to be excluded. |
.keep |
Logical: keep the "evidences" column. |
The OmniPath interactions database fully integrates all attributes from all resources for each interaction. This comes with the advantage that interaction data frames are ready for use in most of the applications; however, it makes it impossible to know which of the resources and references support the direction or effect sign of the interaction. This information can be recovered from the "evidences" column. The "evidences" column preserves all the details about interaction provenances. In cases when you want to use a faithful copy of a certain resource or dataset, this function will help you do so. Still, in most of the applications the best is to use the interaction data as it is returned by the web service.
Note: This function is automatically applied if the
'strict_evidences' argument is passed to any function querying interactions
(e.g. omnipath-interactions).
A copy of the interaction data frame restricted to the given datasets and resources.
## Not run: ci <- collectri(evidences = TRUE) ci <- only_from(ci, datasets = 'collectri') ## End(Not run)## Not run: ci <- collectri(evidences = TRUE) ci <- only_from(ci, datasets = 'collectri') ## End(Not run)
Converts a mixture of ontology IDs and names to only IDs. If an element of the input is missing from the chosen ontology it will be dropped. This can happen if the ontology is a subset (slim) version, but also if the input is not a valid ID or name.
ontology_ensure_id(terms, db_key = "go_basic")ontology_ensure_id(terms, db_key = "go_basic")
terms |
Character: ontology IDs or term names. |
db_key |
Character: key to identify the ontology database. For the
available keys see |
Character vector of ontology IDs.
ontology_ensure_id(c('mitochondrion inheritance', 'GO:0001754')) # [1] "GO:0000001" "GO:0001754"ontology_ensure_id(c('mitochondrion inheritance', 'GO:0001754')) # [1] "GO:0000001" "GO:0001754"
Converts a mixture of ontology IDs and names to only names. If an element of the input is missing from the chosen ontology it will be dropped. This can happen if the ontology is a subset (slim) version, but also if the input is not a valid ID or name.
ontology_ensure_name(terms, db_key = "go_basic")ontology_ensure_name(terms, db_key = "go_basic")
terms |
Character: ontology IDs or term names. |
db_key |
Character: key to identify the ontology database. For the
available keys see |
Character vector of ontology term names.
ontology_ensure_name(c('reproduction', 'GO:0001754', 'foo bar')) # [1] "eye photoreceptor cell differentiation" "reproduction"ontology_ensure_name(c('reproduction', 'GO:0001754', 'foo bar')) # [1] "eye photoreceptor cell differentiation" "reproduction"
Makes sure that the output contains only valid IDs or term names. The input can be a mixture of IDs and names. The order of the input won't be preserved in the output.
ontology_name_id(terms, ids = TRUE, db_key = "go_basic")ontology_name_id(terms, ids = TRUE, db_key = "go_basic")
terms |
Character: ontology IDs or term names. |
ids |
Logical: the output should contain IDs or term names. |
db_key |
Character: key to identify the ontology database. For the
available keys see |
Character vector of ontology IDs or term names.
ontology_name_id(c('mitochondrion inheritance', 'reproduction')) # [1] "GO:0000001" "GO:0000003" ontology_name_id(c('GO:0000001', 'reproduction'), ids = FALSE) # [1] "mitochondrion inheritance" "reproduction"ontology_name_id(c('mitochondrion inheritance', 'reproduction')) # [1] "GO:0000001" "GO:0000003" ontology_name_id(c('GO:0000001', 'reproduction'), ids = FALSE) # [1] "mitochondrion inheritance" "reproduction"
Make sure the resource supports the organism and it has the ID
organism_for(organism, resource, error = TRUE)organism_for(organism, resource, error = TRUE)
organism |
Character or integer: name or NCBI Taxonomy ID of the organism. |
resource |
Charater: name of the resource. |
error |
Logical: raise an error if the organism is not supported in the resource. Otherwise it only emits a warning. |
Character: the ID of the organism as it is used by the resource. NA if the organism can not be translated to the required identifier type.
organism_for(10116, 'chalmers-gem') # [1] "Rat" organism_for(6239, 'chalmers-gem') # [1] "Worm" # organism_for('foobar', 'chalmers-gem') # Error in organism_for("foobar", "chalmers-gem") : # Organism `foobar` (common_name: `NA`; common_name: `NA`) # is not supported by resource `chalmers-gem`. Supported organisms: # Human, Mouse, Rat, Zebrafish, Drosophila melanogaster (Fruit fly), # Caenorhabditis elegans (PRJNA13758).organism_for(10116, 'chalmers-gem') # [1] "Rat" organism_for(6239, 'chalmers-gem') # [1] "Worm" # organism_for('foobar', 'chalmers-gem') # Error in organism_for("foobar", "chalmers-gem") : # Organism `foobar` (common_name: `NA`; common_name: `NA`) # is not supported by resource `chalmers-gem`. Supported organisms: # Human, Mouse, Rat, Zebrafish, Drosophila melanogaster (Fruit fly), # Caenorhabditis elegans (PRJNA13758).
Translate a column of identifiers by orthologous gene pairs
orthology_translate_column( data, column, id_type = NULL, target_organism = "mouse", source_organism = "human", resource = "oma", replace = FALSE, one_to_many = NULL, keep_untranslated = FALSE, translate_complexes = FALSE, uniprot_by_id_type = "entrez" )orthology_translate_column( data, column, id_type = NULL, target_organism = "mouse", source_organism = "human", resource = "oma", replace = FALSE, one_to_many = NULL, keep_untranslated = FALSE, translate_complexes = FALSE, uniprot_by_id_type = "entrez" )
data |
A data frame with the column to be translated. |
column |
Name of a character column with identifiers of the source organism of type 'id_type'. |
id_type |
Type of identifiers in 'column'. Available ID types include
"uniprot", "entrez", "ensg", "refseq" and "swissprot" for OMA, and
"uniprot", "entrez", "genesymbol", "refseq" and "gi" for NCBI
HomoloGene. If you want to translate an ID type not directly available
in your preferred resource, use first |
target_organism |
Name or NCBI Taxonomy ID of the target organism. |
source_organism |
Name or NCBI Taxonomy ID of the source organism. |
resource |
Character: source of the orthology mapping. Currently Orthologous Matrix (OMA) and NCBI HomoloGene are available, refer to them by "oma" and "homologene", respectively. |
replace |
Logical or character: replace the column with the translated identifiers, or create a new column. If it is character, it will be used as the name of the new column. |
one_to_many |
Integer: maximum number of orthologous pairs for one gene of the source organism. Genes mapping to higher number of orthologues will be dropped. |
keep_untranslated |
Logical: keep records without orthologous pairs. If 'replace' is TRUE, this option is ignored, and untranslated records will be dropped. Genes with more than 'one_to_many' orthologues will always be dropped. |
translate_complexes |
Logical: translate the complexes by translating their components. |
uniprot_by_id_type |
Character: translate NCBI HomoloGene to UniProt by this ID type. One of "genesymbol", "entrez", "refseq" or "gi". |
The data frame with identifiers translated to other organism.
PathwayCommons (http://www.pathwaycommons.org/) provides molecular interactions from a number of databases, in either BioPAX or SIF (simple interaction format). This function retrieves all interactions in SIF format. The data is limited to the interacting pair and the type of the interaction.
pathwaycommons_download()pathwaycommons_download()
A data frame (tibble) with interactions.
pc_interactions <- pathwaycommons_download() pc_interactions # # A tibble: 1,884,849 x 3 # from type to # <chr> <chr> <chr> # 1 A1BG controls-expression-of A2M # 2 A1BG interacts-with ABCC6 # 3 A1BG interacts-with ACE2 # 4 A1BG interacts-with ADAM10 # 5 A1BG interacts-with ADAM17 # # . with 1,884,839 more rowspc_interactions <- pathwaycommons_download() pc_interactions # # A tibble: 1,884,849 x 3 # from type to # <chr> <chr> <chr> # 1 A1BG controls-expression-of A2M # 2 A1BG interacts-with ABCC6 # 3 A1BG interacts-with ACE2 # 4 A1BG interacts-with ADAM10 # 5 A1BG interacts-with ADAM17 # # . with 1,884,839 more rows
Use this method to reconstitute the annotation tables into the format of
the original resources. With the 'wide=TRUE' option
annotations applies this function to the
downloaded data.
pivot_annotations(annotations)pivot_annotations(annotations)
annotations |
A data frame of annotations downloaded from the
OmniPath web service by |
A wide format data frame (tibble) if the provided data contains annotations from one resource, otherwise a list of wide format tibbles.
# single resource: the result is a data frame disgenet <- annotations(resources = "DisGeNet") disgenet <- pivot_annotations(disgenet) disgenet # # A tibble: 126,588 × 11 # uniprot genesymbol entity_type disease type score dsi dpi # <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> # 1 P04217 A1BG protein Schizophren. dise. 0.3 0.7 0.538 # 2 P04217 A1BG protein Hepatomegaly phen. 0.3 0.7 0.538 # 3 P01023 A2M protein Fibrosis, L. dise. 0.3 0.529 0.769 # 4 P01023 A2M protein Acute kidne. dise. 0.3 0.529 0.769 # 5 P01023 A2M protein Mental Depr. dise. 0.3 0.529 0.769 # # . with 126,583 more rows, and 3 more variables: nof_pmids <dbl>, # # nof_snps <dbl>, source <chr> # multiple resources: the result is a list annot_long <- annotations( resources = c("DisGeNet", "SignaLink_function", "DGIdb", "kinase.com") ) annot_wide <- pivot_annotations(annot_long) names(annot_wide) # [1] "DGIdb" "DisGeNet" "kinase.com" # [4] "SignaLink_function" annot_wide$kinase.com # # A tibble: 825 x 6 # uniprot genesymbol entity_type group family subfamily # <chr> <chr> <chr> <chr> <chr> <chr> # 1 P31749 AKT1 protein AGC Akt NA # 2 P31751 AKT2 protein AGC Akt NA # 3 Q9Y243 AKT3 protein AGC Akt NA # 4 O14578 CIT protein AGC DMPK CRIK # 5 Q09013 DMPK protein AGC DMPK GEK # # . with 815 more rows# single resource: the result is a data frame disgenet <- annotations(resources = "DisGeNet") disgenet <- pivot_annotations(disgenet) disgenet # # A tibble: 126,588 × 11 # uniprot genesymbol entity_type disease type score dsi dpi # <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> # 1 P04217 A1BG protein Schizophren. dise. 0.3 0.7 0.538 # 2 P04217 A1BG protein Hepatomegaly phen. 0.3 0.7 0.538 # 3 P01023 A2M protein Fibrosis, L. dise. 0.3 0.529 0.769 # 4 P01023 A2M protein Acute kidne. dise. 0.3 0.529 0.769 # 5 P01023 A2M protein Mental Depr. dise. 0.3 0.529 0.769 # # . with 126,583 more rows, and 3 more variables: nof_pmids <dbl>, # # nof_snps <dbl>, source <chr> # multiple resources: the result is a list annot_long <- annotations( resources = c("DisGeNet", "SignaLink_function", "DGIdb", "kinase.com") ) annot_wide <- pivot_annotations(annot_long) names(annot_wide) # [1] "DGIdb" "DisGeNet" "kinase.com" # [4] "SignaLink_function" annot_wide$kinase.com # # A tibble: 825 x 6 # uniprot genesymbol entity_type group family subfamily # <chr> <chr> <chr> <chr> <chr> <chr> # 1 P31749 AKT1 protein AGC Akt NA # 2 P31751 AKT2 protein AGC Akt NA # 3 Q9Y243 AKT3 protein AGC Akt NA # 4 O14578 CIT protein AGC DMPK CRIK # 5 Q09013 DMPK protein AGC DMPK GEK # # . with 815 more rows
Retrieves predicted protein-protein interactions from the PrePPI database (http://honig.c2b2.columbia.edu/preppi). The interactions in this table are supposed to be correct with a > 0.5 probability.
preppi_download(...)preppi_download(...)
... |
Minimum values for the scores. The available scores are:
str, protpep, str_max, red, ort, phy, coexp, go, total, exp and final.
Furthermore, an operator can be passed, either |
PrePPI is a combination of many prediction methods, each resulting a score. For an explanation of the scores see https://honiglab.c2b2.columbia.edu/hfpd/help/Manual.html. The minimum, median and maximum values of the scores:
| Score | Minimum | Median | Maximum | | ------- | ------- | -------- | ------------------ | | str | 0 | 5.5 | 6,495 | | protpep | 0 | 3.53 | 38,138 | | str_max | 0 | 17.9 | 38,138 | | red | 0 | 1.25 | 24.4 | | ort | 0 | 0 | 5,000 | | phy | 0 | 2.42 | 2.42 | | coexp | 0 | 2.77 | 45.3 | | go | 0 | 5.86 | 181 | | total | 0 | 1,292 | 106,197,000,000 | | exp | 1 | 958 | 4,626 | | final | 600 | 1,778 | 4.91e14 |
A data frame (tibble) of interactions with scores, databases and literature references.
preppi <- preppi_download() preppi # # A tibble: 1,545,710 x 15 # prot1 prot2 str_score protpep_score str_max_score red_score ort_score # <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 Q131. P146. 18.6 6.45 18.6 4.25 0.615 # 2 P064. Q96N. 1.83 14.3 14.3 4.25 0 # 3 Q7Z6. Q8NC. 4.57 0 4.57 0 0 # 4 P370. P154. 485. 0 485. 1.77 0.615 # 5 O004. Q9NR. 34.0 0 34.0 0.512 0 # # . with 1,545,700 more rows, and 8 more variables: phy_score <dbl>, # # coexp_score <dbl>, go_score <dbl>, total_score <dbl>, dbs <chr>, # # pubs <chr>, exp_score <dbl>, final_score <dbl>preppi <- preppi_download() preppi # # A tibble: 1,545,710 x 15 # prot1 prot2 str_score protpep_score str_max_score red_score ort_score # <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 Q131. P146. 18.6 6.45 18.6 4.25 0.615 # 2 P064. Q96N. 1.83 14.3 14.3 4.25 0 # 3 Q7Z6. Q8NC. 4.57 0 4.57 0 0 # 4 P370. P154. 485. 0 485. 1.77 0.615 # 5 O004. Q9NR. 34.0 0 34.0 0.512 0 # # . with 1,545,700 more rows, and 8 more variables: phy_score <dbl>, # # coexp_score <dbl>, go_score <dbl>, total_score <dbl>, dbs <chr>, # # pubs <chr>, exp_score <dbl>, final_score <dbl>
Filter PrePPI interactions by scores
preppi_filter(data, ..., .op = "&")preppi_filter(data, ..., .op = "&")
data |
A data frame of PrePPI interactions as provided by
|
... |
Minimum values for the scores. The available scores are:
str, protpep, str_max, red, ort, phy, coexp, go, total, exp and final.
See more about the scores at |
.op |
The operator to combine the scores with: either |
The input data frame (tibble) filtered by the score thresholds.
preppi <- preppi_download() preppi_filtered <- preppi_filter(preppi, red = 10, str = 4.5, ort = 1) nrow(preppi_filtered) # [1] 8443preppi <- preppi_download() preppi_filtered <- preppi_filter(preppi, red = 10, str = 4.5, ort = 1) nrow(preppi_filtered) # [1] 8443
The motifs can be copy-pasted into a BMA canvas.
print_bma_motif_es(edge_seq, G, granularity = 2)print_bma_motif_es(edge_seq, G, granularity = 2)
edge_seq |
An igraph edge sequence. |
G |
An igraph graph object. |
granularity |
Numeric: granularity value. |
Returns 'NULL'.
interactions <- omnipath(resources = "ARN") graph <- interaction_graph(interactions) print_bma_motif_es(igraph::E(graph)[1], graph) # {"Model": { # "Name": "Omnipath motif", # "Variables":[{ # "Name":"ULK1", # "Id":1, # "RangeFrom":0, # "RangeTo":2, # "Formula":"" # }, # { # "Name":"ATG13", # ... # }], # ... (truncated) # }}interactions <- omnipath(resources = "ARN") graph <- interaction_graph(interactions) print_bma_motif_es(igraph::E(graph)[1], graph) # {"Model": { # "Name": "Omnipath motif", # "Variables":[{ # "Name":"ULK1", # "Id":1, # "RangeFrom":0, # "RangeTo":2, # "Formula":"" # }, # { # "Name":"ATG13", # ... # }], # ... (truncated) # }}
The motifs can be copy-pasted into a BMA canvas.
print_bma_motif_vs(node_seq, G)print_bma_motif_vs(node_seq, G)
node_seq |
An igraph node sequence. |
G |
An igraph graph object. |
Returns 'NULL'.
interactions <- omnipath(resources = "ARN") graph <- interaction_graph(interactions) print_bma_motif_vs( igraph::all_shortest_paths( graph, from = 'ULK1', to = 'ATG13' )$res, graph )interactions <- omnipath(resources = "ARN") graph <- interaction_graph(interactions) print_bma_motif_vs( igraph::all_shortest_paths( graph, from = 'ULK1', to = 'ATG13' )$res, graph )
Prints the interactions or enzyme-substrate relationships in a nice format.
print_interactions(interactions, refs = FALSE)print_interactions(interactions, refs = FALSE)
interactions |
Data frame with the interactions generated by any of the
functions in |
refs |
Logical: include PubMed IDs where available. |
Returns 'NULL'.
enzsub <- enzyme_substrate() print_interactions(head(enzsub)) print_interactions(tail(enzsub), refs = TRUE) print_interactions( dplyr::filter( enzsub, enzyme_genesymbol == 'MAP2K1', substrate_genesymbol == 'MAPK3' ) ) signor <- omnipath(resources = "SIGNOR") print_interactions(head(signor)) # source interaction target n_resources # 6 MAPK14 (Q16539) ==( + )==> MAPKAPK2 (P49137) 23 # 4 TRPM7 (Q96QT4) ==( + )==> ANXA1 (P04083) 10 # 1 PRKG1 (Q13976) ==( - )==> TRPC3 (Q13507) 8 # 2 PTPN1 (P18031) ==( - )==> TRPV6 (Q9H1D0) 6 # 5 PRKACA (P17612) ==( - )==> MCOLN1 (Q9GZU1) 6 # 3 RACK1 (P63244) ==( - )==> TRPM6 (Q9BX84) 2enzsub <- enzyme_substrate() print_interactions(head(enzsub)) print_interactions(tail(enzsub), refs = TRUE) print_interactions( dplyr::filter( enzsub, enzyme_genesymbol == 'MAP2K1', substrate_genesymbol == 'MAPK3' ) ) signor <- omnipath(resources = "SIGNOR") print_interactions(head(signor)) # source interaction target n_resources # 6 MAPK14 (Q16539) ==( + )==> MAPKAPK2 (P49137) 23 # 4 TRPM7 (Q96QT4) ==( + )==> ANXA1 (P04083) 10 # 1 PRKG1 (Q13976) ==( - )==> TRPC3 (Q13507) 8 # 2 PTPN1 (P18031) ==( - )==> TRPV6 (Q9H1D0) 6 # 5 PRKACA (P17612) ==( - )==> MCOLN1 (Q9GZU1) 6 # 3 RACK1 (P63244) ==( - )==> TRPM6 (Q9BX84) 2
Pretty prints the interactions in a path.
print_path_es(edges, G)print_path_es(edges, G)
edges |
An igraph edge sequence object. |
G |
igraph object (from ptms or any interaction dataset) |
Returns 'NULL'.
interactions <- omnipath(resources = "SignaLink3") OPI_g <- interaction_graph(interactions = interactions) print_path_es( suppressWarnings(igraph::shortest_paths( OPI_g, from = 'TYRO3', to = 'STAT3', output = 'epath' ))$epath[[1]], OPI_g )interactions <- omnipath(resources = "SignaLink3") OPI_g <- interaction_graph(interactions = interactions) print_path_es( suppressWarnings(igraph::shortest_paths( OPI_g, from = 'TYRO3', to = 'STAT3', output = 'epath' ))$epath[[1]], OPI_g )
Prints the interactions in the path in a nice format.
print_path_vs(nodes, G)print_path_vs(nodes, G)
nodes |
An igraph node sequence object. |
G |
An igraph graph object (from ptms or interactions) |
Returns 'NULL'.
interactions <- omnipath(resources = "SignaLink3") OPI_g <- interaction_graph(interactions = interactions) print_path_vs( igraph::all_shortest_paths( OPI_g, from = 'TYRO3', to = 'STAT3' )$vpath, OPI_g ) enzsub <- enzyme_substrate(resources=c("PhosphoSite", "SIGNOR")) enzsub_g <- enzsub_graph(enzsub) print_path_vs( igraph::all_shortest_paths( enzsub_g, from = 'SRC', to = 'STAT1' )$res, enzsub_g )interactions <- omnipath(resources = "SignaLink3") OPI_g <- interaction_graph(interactions = interactions) print_path_vs( igraph::all_shortest_paths( OPI_g, from = 'TYRO3', to = 'STAT3' )$vpath, OPI_g ) enzsub <- enzyme_substrate(resources=c("PhosphoSite", "SIGNOR")) enzsub_g <- enzsub_graph(enzsub) print_path_vs( igraph::all_shortest_paths( enzsub_g, from = 'SRC', to = 'STAT1' )$res, enzsub_g )
Open one or more PubMed articles
pubmed_open(pmids, browser = NULL, sep = ";", max_pages = 25L)pubmed_open(pmids, browser = NULL, sep = ";", max_pages = 25L)
pmids |
Character or numberic vector of one or more PubMed IDs. |
browser |
Character: name of the web browser executable. If 'NULL', the default web browser will be used. |
sep |
Character: split the PubMed IDs by this separator. |
max_pages |
Numeric: largest number of pages to open. This is to prevent opening hundreds or thousands of pages at once. |
Returns 'NULL'.
interactions <- omnipath() pubmed_open(interactions$references[1])interactions <- omnipath() pubmed_open(interactions$references[1])
All parameter names and their possible values for a query type. Note: parameters with 'NULL' values have too many possible values to list them.
query_info(query_type)query_info(query_type)
query_type |
Character: interactions, annotations, complexes, enz_sub or intercell. |
A named list with the parameter names and their possible values.
ia_param <- query_info('interactions') ia_param$datasets[1:5] # [1] "dorothea" "kinaseextra" "ligrecextra" "lncrna_mrna" "mirnatarget"ia_param <- query_info('interactions') ia_param$datasets[1:5] # [1] "dorothea" "kinaseextra" "ligrecextra" "lncrna_mrna" "mirnatarget"
Curated ligand-receptor pairs from Supplementary Table 2 of the article "A draft network of ligand-receptor mediated multicellular signaling in human" (https://www.nature.com/articles/ncomms8866).
ramilowski_download()ramilowski_download()
A data frame (tibble) with interactions.
rami_interactions <- ramilowski_download() rami_interactions # # A tibble: 2,557 x 16 # Pair.Name Ligand.Approved. Ligand.Name Receptor.Approv. # <chr> <chr> <chr> <chr> # 1 A2M_LRP1 A2M alpha-2-ma. LRP1 # 2 AANAT_MT. AANAT aralkylami. MTNR1A # 3 AANAT_MT. AANAT aralkylami. MTNR1B # 4 ACE_AGTR2 ACE angiotensi. AGTR2 # 5 ACE_BDKR. ACE angiotensi. BDKRB2 # # . with 2,547 more rows, and 12 more variables: Receptor.Name <chr>, # # DLRP <chr>, HPMR <chr>, IUPHAR <chr>, HPRD <chr>, # # STRING.binding <chr>, STRING.experiment <chr>, HPMR.Ligand <chr>, # # HPMR.Receptor <chr>, PMID.Manual <chr>, Pair.Source <chr>, # # Pair.Evidence <chr>rami_interactions <- ramilowski_download() rami_interactions # # A tibble: 2,557 x 16 # Pair.Name Ligand.Approved. Ligand.Name Receptor.Approv. # <chr> <chr> <chr> <chr> # 1 A2M_LRP1 A2M alpha-2-ma. LRP1 # 2 AANAT_MT. AANAT aralkylami. MTNR1A # 3 AANAT_MT. AANAT aralkylami. MTNR1B # 4 ACE_AGTR2 ACE angiotensi. AGTR2 # 5 ACE_BDKR. ACE angiotensi. BDKRB2 # # . with 2,547 more rows, and 12 more variables: Receptor.Name <chr>, # # DLRP <chr>, HPMR <chr>, IUPHAR <chr>, HPRD <chr>, # # STRING.binding <chr>, STRING.experiment <chr>, HPMR.Ligand <chr>, # # HPMR.Receptor <chr>, PMID.Manual <chr>, Pair.Source <chr>, # # Pair.Evidence <chr>
Pairwise ID translation table from RaMP database
ramp_id_mapping_table(from, to, version = "2.5.4")ramp_id_mapping_table(from, to, version = "2.5.4")
from |
Character or Symbol. Name of an identifier type. |
to |
Character or Symbol. Name of an identifier type. |
version |
Character. The version of RaMP to download. |
Dataframe of pairs of identifiers.
ramp_id_mapping_table('hmdb', 'kegg')ramp_id_mapping_table('hmdb', 'kegg')
RaMP identifier type label
ramp_id_type(label)ramp_id_type(label)
label |
Character: an ID type label, as shown in the table returned
by |
Character: the RaMP specific ID type label, or the input unchanged if it could not be translated (still might be a valid identifier name). These labels should be valid value names, as used in RaMP SQL database.
ramp_id_type("rhea") # [1] "rhea-comp"ramp_id_type("rhea") # [1] "rhea-comp"
Download and open RaMP database SQLite
ramp_sqlite(version = "2.5.4")ramp_sqlite(version = "2.5.4")
version |
Character. The version of RaMP to download. |
SQLite connection.
sqlite_con <- ramp_sqlite()sqlite_con <- ramp_sqlite()
Return table from RaMP database
ramp_table(name, version = "2.5.4")ramp_table(name, version = "2.5.4")
name |
Character. The name of the RaMP table to fetch. |
version |
Character. The version of RaMP to download. |
Character vector of table names in the RaMP SQLite database.
ramp_table('source')ramp_table('source')
List tables in RaMP database
ramp_tables(version = "2.5.4")ramp_tables(version = "2.5.4")
version |
Character. The version of RaMP to download. |
Character vector of table names in the RaMP SQLite database.
ramp_tables()ramp_tables()
Transcription factor effects from RegNetwork
regnetwork_directions(organism = "human")regnetwork_directions(organism = "human")
organism |
Character: either human or mouse. |
A data frame (tibble) of TF-target interactions with effect signs.
regn_dir <- regnetwork_directions() regn_dir # # A tibble: 3,954 x 5 # source_genesymb. source_entrez target_genesymb. target_entrez # <chr> <chr> <chr> <chr> # 1 AHR 196 CDKN1B 1027 # 2 APLNR 187 PIK3C3 5289 # 3 APLNR 187 PIK3R4 30849 # 4 AR 367 KLK3 354 # 5 ARNT 405 ALDOA 226 # # . with 3,944 more rows, and 1 more variable: effect <dbl>regn_dir <- regnetwork_directions() regn_dir # # A tibble: 3,954 x 5 # source_genesymb. source_entrez target_genesymb. target_entrez # <chr> <chr> <chr> <chr> # 1 AHR 196 CDKN1B 1027 # 2 APLNR 187 PIK3C3 5289 # 3 APLNR 187 PIK3R4 30849 # 4 AR 367 KLK3 354 # 5 ARNT 405 ALDOA 226 # # . with 3,944 more rows, and 1 more variable: effect <dbl>
Downloads transcriptional and post-transcriptional regulatory interactions
from the RegNetwork database (http://www.regnetworkweb.org/). The
information about effect signs (stimulation or inhibition), provided by
regnetwork_directions are included in the result.
regnetwork_download(organism = "human")regnetwork_download(organism = "human")
organism |
Character: either human or mouse. |
Data frame with interactions.
regn_interactions <- regnetwork_download() regn_interactions # # A tibble: 372,778 x 7 # source_genesymb. source_entrez target_genesymb. target_entrez # <chr> <chr> <chr> <chr> # 1 USF1 7391 S100A6 6277 # 2 USF1 7391 DUSP1 1843 # 3 USF1 7391 C4A 720 # 4 USF1 7391 ABCA1 19 # 5 TP53 7157 TP73 7161 # # . with 372,768 more rows, and 3 more variables: effect <dbl>, # # source_type <chr>, target_type <chr>regn_interactions <- regnetwork_download() regn_interactions # # A tibble: 372,778 x 7 # source_genesymb. source_entrez target_genesymb. target_entrez # <chr> <chr> <chr> <chr> # 1 USF1 7391 S100A6 6277 # 2 USF1 7391 DUSP1 1843 # 3 USF1 7391 C4A 720 # 4 USF1 7391 ABCA1 19 # 5 TP53 7157 TP73 7161 # # . with 372,768 more rows, and 3 more variables: effect <dbl>, # # source_type <chr>, target_type <chr>
Converting the nested list to a table is a more costly operation, it takes
a few seconds. Best to do it only once, or pass tables = TRUE to
obo_parser, and convert the data frame to list, if you
also need it in list format.
relations_list_to_table(relations, direction = NULL)relations_list_to_table(relations, direction = NULL)
relations |
A nested list of ontology relations (the "relations"
element of the list returned by |
direction |
Override the direction (i.e. child -> parents or parent -> children). The nested lists produced by functions in the current package add an attribute "direction" thus no need to pass this value. If the attribute and the argument are both missing, the column will be named simply "side2" and it won't be clear whether the relations point from "term" to "side2" or the other way around. The direction should be a character vector of length 2 with the values "parents" and "children". |
The relations converted to a data frame (tibble).
goslim_url <- "http://current.geneontology.org/ontology/subsets/goslim_generic.obo" path <- tempfile() httr::GET(goslim_url, httr::write_disk(path, overwrite = TRUE)) obo <- obo_parser(path, tables = FALSE) unlink(path) rel_tbl <- relations_list_to_table(obo$relations)goslim_url <- "http://current.geneontology.org/ontology/subsets/goslim_generic.obo" path <- tempfile() httr::GET(goslim_url, httr::write_disk(path, overwrite = TRUE)) obo <- obo_parser(path, tables = FALSE) unlink(path) rel_tbl <- relations_list_to_table(obo$relations)
Graph from a table of ontology relations
relations_table_to_graph(relations)relations_table_to_graph(relations)
relations |
A data frame of ontology relations (the "relations"
element of the list returned by |
By default the relations point from child to parents, the edges in the
graph will be of the same direction. Use swap_relations
on the data frame to reverse the direction.
The relations converted to an igraph graph object.
## Not run: go <- get_db('go_basic') go_graph <- relations_table_to_graph(go$relations) ## End(Not run)## Not run: go <- get_db('go_basic') go_graph <- relations_table_to_graph(go$relations) ## End(Not run)
Nested list from a table of ontology relations
relations_table_to_list(relations)relations_table_to_list(relations)
relations |
A data frame of ontology relations (the "relations"
element of the list returned by |
The relations converted to a nested list.
goslim_url <- "http://current.geneontology.org/ontology/subsets/goslim_generic.obo" path <- tempfile() httr::GET(goslim_url, httr::write_disk(path, overwrite = TRUE)) obo <- obo_parser(path, tables = TRUE) unlink(path) rel_list <- relations_table_to_list(obo$relations)goslim_url <- "http://current.geneontology.org/ontology/subsets/goslim_generic.obo" path <- tempfile() httr::GET(goslim_url, httr::write_disk(path, overwrite = TRUE)) obo <- obo_parser(path, tables = TRUE) unlink(path) rel_list <- relations_table_to_list(obo$relations)
ReMap (http://remap.univ-amu.fr/) is a database of ChIP-Seq experiments. It provides raw and merged peaks and CRMs (cis regulatory motifs) with their associations to regulators (TFs). TF-target relationships can be derived as it is written in Garcia-Alonso et al. 2019: "For ChIP-seq, we downloaded the binding peaks from ReMap and scored the interactions between each TF and each gene according to the distance between the TFBSs and the genes’ transcription start sites. We evaluated different filtering strategies that consisted of selecting only the top-scoring 100, 200, 500, and 1000 target genes for each TF." (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6673718/#s1title). This function returns the top TF-target relationships as used in DoRothEA: https://github.com/saezlab/dorothea/blob/master/inst/scripts/02_chip_seq.R).
remap_dorothea_download()remap_dorothea_download()
Data frame with TF-target relationships.
remap_interactions <- remap_dorothea_download() remap_interactions # # A tibble: 136,988 x 2 # tf target # <chr> <chr> # 1 ADNP ABCC1 # 2 ADNP ABCC6 # 3 ADNP ABHD5 # 4 ADNP ABT1 # 5 ADNP AC002066.1 # # . with 136,978 more rowsremap_interactions <- remap_dorothea_download() remap_interactions # # A tibble: 136,988 x 2 # tf target # <chr> <chr> # 1 ADNP ABCC1 # 2 ADNP ABCC6 # 3 ADNP ABHD5 # 4 ADNP ABT1 # 5 ADNP AC002066.1 # # . with 136,978 more rows
Downloads the ReMap TF-target interactions as processed by Garcia-Alonso et al. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6673718/#s1title) and filters them based on a score threshold, the top targets and whether the TF is included in the TF census (Vaquerizas et al. 2009). The code for filtering is adapted from DoRothEA, written by Christian Holland.
remap_filtered(score = 100, top_targets = 500, only_known_tfs = TRUE)remap_filtered(score = 100, top_targets = 500, only_known_tfs = TRUE)
score |
Numeric: a minimum score between 0 and 1000, records with lower scores will be excluded. If NULL no filtering performed. |
top_targets |
Numeric: the number of top scoring targets for each TF. Essentially the maximum number of targets per TF. If NULL the number of targets is not restricted. |
only_known_tfs |
Logical: whether to exclude TFs which are not in TF census. |
Data frame with TF-target relationships.
## Not run: remap_interactions <- remap_filtered() nrow(remap_interactions) # [1] 145680 remap_interactions <- remap_filtered(top_targets = 100) remap_interactions # # A tibble: 30,330 x 2 # source_genesymbol target_genesymbol # <chr> <chr> # 1 ADNP ABCC1 # 2 ADNP ABT1 # 3 ADNP AC006076.1 # 4 ADNP AC007792.1 # 5 ADNP AC011288.2 # # . with 30,320 more rows ## End(Not run)## Not run: remap_interactions <- remap_filtered() nrow(remap_interactions) # [1] 145680 remap_interactions <- remap_filtered(top_targets = 100) remap_interactions # # A tibble: 30,330 x 2 # source_genesymbol target_genesymbol # <chr> <chr> # 1 ADNP ABCC1 # 2 ADNP ABT1 # 3 ADNP AC006076.1 # 4 ADNP AC007792.1 # 5 ADNP AC011288.2 # # . with 30,320 more rows ## End(Not run)
ReMap (http://remap.univ-amu.fr/) is a database of ChIP-Seq experiments. It provides raw and merged peaks and CRMs (cis regulatory motifs) with their associations to regulators (TFs). TF-target relationships can be derived as it is written in Garcia-Alonso et al. 2019: "For ChIP-seq, we downloaded the binding peaks from ReMap and scored the interactions between each TF and each gene according to the distance between the TFBSs and the genes’ transcription start sites. We evaluated different filtering strategies that consisted of selecting only the top-scoring 100, 200, 500, and 1000 target genes for each TF." (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6673718/#s1title). This function retrieves the full processed TF-target list from the data deposited in https://zenodo.org/record/3713238.
remap_tf_target_download()remap_tf_target_download()
Data frame with TF-target relationships.
## Not run: remap_interactions <- remap_tf_target_download() remap_interactions # # A tibble: 9,546,470 x 4 # source_genesymbol target_genesymbol target_ensembl score # <chr> <chr> <chr> <dbl> # 1 ADNP PTPRS ENSG00000105426.16 1000 # 2 AFF4 PRKCH ENSG00000027075.14 1000 # 3 AHR CTNND2 ENSG00000169862.18 1000 # 4 AR PDE4D ENSG00000113448.18 1000 # 5 ARID1A PLEC ENSG00000178209.14 1000 # # . with 9,546,460 more rows ## End(Not run)## Not run: remap_interactions <- remap_tf_target_download() remap_interactions # # A tibble: 9,546,470 x 4 # source_genesymbol target_genesymbol target_ensembl score # <chr> <chr> <chr> <dbl> # 1 ADNP PTPRS ENSG00000105426.16 1000 # 2 AFF4 PRKCH ENSG00000027075.14 1000 # 3 AHR CTNND2 ENSG00000169862.18 1000 # 4 AR PDE4D ENSG00000113448.18 1000 # 5 ARID1A PLEC ENSG00000178209.14 1000 # # . with 9,546,460 more rows ## End(Not run)
Restore the built-in default values of all config parameters of a package
Restore the built-in default values of all config parameters of OmnipathR
reset_config(save = NULL, reset_all = FALSE, pkg = "OmnipathR") omnipath_reset_config(...)reset_config(save = NULL, reset_all = FALSE, pkg = "OmnipathR") omnipath_reset_config(...)
save |
If a path, the restored config will be also saved
to this file. If TRUE, the config will be saved to the current default
config path (see |
reset_all |
Reset to their defaults also the options already set in the R options. |
pkg |
Character: name of a package |
... |
Ignored. |
The config as a list.
omnipath_load_config, omnipath_save_config
## Not run: # restore the defaults and write them to the default config file: omnipath_reset_config() omnipath_save_config() ## End(Not run)## Not run: # restore the defaults and write them to the default config file: omnipath_reset_config() omnipath_save_config() ## End(Not run)
The 'resources' query type provides resource metadata in JSON format. Here we retrieve this JSON and return it as a nested list structure.
resource_info()resource_info()
A nested list structure with resource metadata.
resource_info()resource_info()
Collects the names of the resources available in OmniPath for a certain query type and optionally for a dataset within that.
resources(query_type, datasets = NULL, generic_categories = NULL)resources(query_type, datasets = NULL, generic_categories = NULL)
query_type |
one of the query types 'interactions', 'enz_sub', 'complexes', 'annotations' or 'intercell' |
datasets |
currently within the 'interactions' query type only, multiple datasets are available: 'omnipath', 'kinaseextra', 'pathwayextra', 'ligrecextra', 'dorothea', 'tf_target', 'tf_mirna', 'mirnatarget' and 'lncrna_mrna'. |
generic_categories |
for the 'intercell' query type, restrict the search for some generic categories e.g. 'ligand' or 'receptor'. |
a character vector with resource names
resources(query_type = "interactions")resources(query_type = "interactions")
Unfortunately the column title is different across the various query types in the OmniPath web service, so we need to guess.
resources_colname(data)resources_colname(data)
data |
A data frame downloaded by any |
Character: the name of the column, if any of the column names matches.
co <- complexes() resources_colname(co) # [1] "sources"co <- complexes() resources_colname(co) # [1] "sources"
Collect resource names from a data frame
resources_in(data)resources_in(data)
data |
A data frame from an OmniPath query. |
Character: resource names occuring in the data frame.
pathways <- omnipath_interactions() resources_in(pathways)pathways <- omnipath_interactions() resources_in(pathways)
This function takes an OmniPath interaction data frame as input and returns a sigmaJS object for the subgraph formed by the neighbors of a node of interest.
show_network(interactions, node = NULL)show_network(interactions, node = NULL)
interactions |
An OmniPath interaction data frame. |
node |
The node of interest. |
A sigmaJS object, check http://sigmajs.john-coene.com/index.html for further details and customization options.
## Not run: # get interactions from omnipath interactions <- omnipath() # create and plot the network containing ATM neighbors viz_sigmajs_neighborhood(interactions_df = interactions, int_node = "ATM") ## End(Not run)## Not run: # get interactions from omnipath interactions <- omnipath() # create and plot the network containing ATM neighbors viz_sigmajs_neighborhood(interactions_df = interactions, int_node = "ATM") ## End(Not run)
Enzyme-substrate data does not contain sign (activation/inhibition), we generate this information based on the interaction network.
signed_ptms( enzsub = enzyme_substrate(), interactions = omnipath_interactions() )signed_ptms( enzsub = enzyme_substrate(), interactions = omnipath_interactions() )
enzsub |
Enzyme-substrate data frame generated by
|
interactions |
interaction data frame generated by an OmniPath
interactions query: |
Data frame of enzyme-substrate relationships with is_inhibition and is_stimulation columns.
enzsub <- enzyme_substrate(resources = c("PhosphoSite", "SIGNOR")) interactions <- omnipath_interactions() enzsub <- signed_ptms(enzsub, interactions)enzsub <- enzyme_substrate(resources = c("PhosphoSite", "SIGNOR")) interactions <- omnipath_interactions() enzsub <- signed_ptms(enzsub, interactions)
The intercellular communication network data frames, created by
intercell_network, are combinations of a network data
frame with two copies of the intercell annotation data frames, all of them
already having quite some columns. Here we keep only the names of the
interacting pair, their intercellular communication roles, and the minimal
information of the origin of both the interaction and the annotations.
Optionally further columns can be selected.
simplify_intercell_network(network, ...)simplify_intercell_network(network, ...)
network |
An intercell network data frame, as provided by
|
... |
Optional, further columns to select. |
An intercell network data frame with some columns removed.
icn <- intercell_network() icn_s <- simplify_intercell_network(icn)icn <- intercell_network() icn_s <- simplify_intercell_network(icn)
A few resources and datasets are available also as plain TSV files and can be accessed without TLS. The purpose of these tables is to make the most often used OmniPath data available on computers with configuration issues. These tables are not the recommended way to access OmniPath data, and a warning is issued each time they are accessed.
static_table( query, resource, organism = 9606L, strict_evidences = TRUE, wide = TRUE, dorothea_levels = c("A", "B", "C") )static_table( query, resource, organism = 9606L, strict_evidences = TRUE, wide = TRUE, dorothea_levels = c("A", "B", "C") )
query |
Character: a query type such as "annotations" or "interactions". |
resource |
Character: name of the resource or dataset, such as "CollecTRI" or "PROGENy". |
organism |
Integer: NCBI Taxonomy of the organism: 9606 for human, 10090 for mouse and 10116 for rat. |
strict_evidences |
Logical: restrict the evidences to the queried datasets and resources. If set to FALSE, the directions and effect signs and references might be based on other datasets and resources. |
wide |
Convert the annotation table to wide format, which
corresponds more or less to the original resource. If the data comes
from more than one resource a list of wide tables will be returned.
See examples at |
dorothea_levels |
Vector detailing the confidence levels of the
interactions to be downloaded. In dorothea, every TF-target interaction
has a confidence score ranging from A to E, being A the most reliable
interactions.
By default here we take A, B and C level interactions
( |
A data frame (tibble) with the requested resource.
static_table("annotations", "PROGENy")static_table("annotations", "PROGENy")
A few resources and datasets are available also as plain TSV files and can be accessed without TLS. The purpose of these tables is to make the most often used OmniPath data available on computers with configuration issues. These tables are not the recommended way to access OmniPath data, and a warning is issued each time they are accessed.
static_tables()static_tables()
A data frame listing the available tables.
static_tables()static_tables()
Retrieve the STITCH actions dataset
stitch_actions(organism = "human", prefixes = FALSE)stitch_actions(organism = "human", prefixes = FALSE)
organism |
Character or integer: name or NCBI Taxonomy ID of an organism. STITCH supports many organisms, please refer to their web site at https://stitch.embl.de/. |
prefixes |
Logical: include the prefixes in front of identifiers. |
Data frame of STITCH actions.
sta <- stitch_actions(organism = 'mouse')sta <- stitch_actions(organism = 'mouse')
Retrieve the STITCH links dataset
stitch_links(organism = "human", prefixes = FALSE)stitch_links(organism = "human", prefixes = FALSE)
organism |
Character or integer: name or NCBI Taxonomy ID of an organism. STITCH supports many organisms, please refer to their web site at https://stitch.embl.de/. |
prefixes |
Logical: include the prefixes in front of identifiers. |
Data frame: organism specific STITCH links dataset.
stl <- stitch_links()stl <- stitch_links()
Chemical-protein interactions from STITCH
stitch_network( organism = "human", min_score = 700L, protein_ids = c("uniprot", "genesymbol"), metabolite_ids = c("hmdb", "kegg"), cosmos = FALSE )stitch_network( organism = "human", min_score = 700L, protein_ids = c("uniprot", "genesymbol"), metabolite_ids = c("hmdb", "kegg"), cosmos = FALSE )
organism |
Character or integer: name or NCBI Taxonomy ID of an organism. STITCH supports many organisms, please refer to their web site at https://stitch.embl.de/. |
min_score |
Confidence cutoff used for STITCH connections (700 by default). |
protein_ids |
Character: translate the protein identifiers to these ID types. Each ID type results two extra columns in the output, for the "a" and "b" sides of the interaction, respectively. The default ID type for proteins is Esembl Protein ID, and by default UniProt IDs and Gene Symbols are included. |
metabolite_ids |
Character: translate the protein identifiers to these ID types. Each ID type results two extra columns in the output, for the "a" and "b" sides of the interaction, respectively. The default ID type for metabolites is PubChem CID, and HMDB IDs and KEGG IDs are included. |
cosmos |
Logical: use COSMOS format? |
A data frame of STITCH chemical-protein and protein-chemical interactions with their effect signs, and optionally with identifiers translated.
stn <- stitch_network(protein_ids = 'genesymbol', metabolite_ids = 'hmdb')stn <- stitch_network(protein_ids = 'genesymbol', metabolite_ids = 'hmdb')
STITCH adds the NCBI Taxonomy ID as a prefix to Ensembl protein identifiers, e.g. "9606.ENSP00000170630", and "CID" followed by "s" or "m" (stereospecific or merged, respectively) in front of PubChem Compound Identifiers. It also pads the CID with zeros. This function removes these prefixes, leaving only the identifiers.
stitch_remove_prefixes(d, ..., remove = TRUE)stitch_remove_prefixes(d, ..., remove = TRUE)
d |
Data frame, typically the output of |
... |
Names of columns to remove prefixes from. NSE is supported. |
remove |
Logical: remove the prefixes? If FALSE, this function does nothing. |
Data frame with prefixes removed in the specified columns.
stitch_remove_prefixes( data.frame(a = c('9606.ENSP00000170630', 'CIDs00012345')), a )stitch_remove_prefixes( data.frame(a = c('9606.ENSP00000170630', 'CIDs00012345')), a )
Extract a custom subnetwork from a large network
subnetwork( network, nodes = NULL, order = 1L, mode = "all", mindist = 0L, return_df = TRUE )subnetwork( network, nodes = NULL, order = 1L, mode = "all", mindist = 0L, return_df = TRUE )
network |
Either an OmniPath interaction data frame, or an igraph graph object. |
nodes |
Character or integer vector: names, identifiers or indices of the nodes to build the subnetwork around. |
order |
Integer: order of neighbourhood around nodes; i.e., number of steps starting from the provided nodes. |
mode |
Character: "all", "out" or "in". Follow directed edges from the provided nodes in any, outbound or inbound direction, respectively. |
mindist |
Integer: The minimum distance to include the vertex in the result. |
return_df |
Logical: return an interaction data frame instead of an igraph object. |
A network data frame or an igraph object, depending on the “return_df“ parameter.
Reverse the direction of ontology relations
swap_relations(relations)swap_relations(relations)
relations |
The 'relations' component of the data returned by
|
Same type as the input, but the relations swapped: if in the input these pointed from each child to the parents, in the output they point from each parent to their children, and vice versa.
goslim_url <- "http://current.geneontology.org/ontology/subsets/goslim_generic.obo" path <- tempfile() httr::GET(goslim_url, httr::write_disk(path, overwrite = TRUE)) obo <- obo_parser(path) unlink(path) rel_swapped <- swap_relations(obo$relations)goslim_url <- "http://current.geneontology.org/ontology/subsets/goslim_generic.obo" path <- tempfile() httr::GET(goslim_url, httr::write_disk(path, overwrite = TRUE)) obo <- obo_parser(path) unlink(path) rel_swapped <- swap_relations(obo$relations)
Retain only SwissProt IDs
swissprots_only(uniprots, organism = 9606)swissprots_only(uniprots, organism = 9606)
uniprots |
Character vector of UniProt IDs. |
organism |
Character or integer: name or identifier of the organism. |
Character vector with only SwissProt IDs.
swissprots_only(c("Q05BL1", "A0A654IBU3", "P00533")) # [1] "P00533"swissprots_only(c("Q05BL1", "A0A654IBU3", "P00533")) # [1] "P00533"
Vaquerizas et al. published in 2009 a list of transcription factors. This function retrieves Supplementary Table 2 from the article (http://www.nature.com/nrg/journal/v10/n4/index.html).
tfcensus_download()tfcensus_download()
A data frame (tibble) listing transcription factors.
tfcensus <- tfcensus_download() tfcensus # # A tibble: 1,987 x 7 # Class `Ensembl ID` `IPI ID` `Interpro DBD` `Interpro DNA-b. # <chr> <chr> <chr> <chr> <chr> # 1 a ENSG0000000. IPI0021. NA IPR001289 # 2 a ENSG0000000. IPI0004. IPR000047;IPR. NA # 3 a ENSG0000000. IPI0001. IPR001356;IPR. NA # 4 a ENSG0000000. IPI0029. IPR000910;IPR. NA # 5 a ENSG0000000. IPI0001. IPR007087;IPR. IPR006794 # # . with 1,977 more rows, and 2 more variables: `HGNC symbol` <chr>, # # `Tissue-specificity` <chr>tfcensus <- tfcensus_download() tfcensus # # A tibble: 1,987 x 7 # Class `Ensembl ID` `IPI ID` `Interpro DBD` `Interpro DNA-b. # <chr> <chr> <chr> <chr> <chr> # 1 a ENSG0000000. IPI0021. NA IPR001289 # 2 a ENSG0000000. IPI0004. IPR000047;IPR. NA # 3 a ENSG0000000. IPI0001. IPR001356;IPR. NA # 4 a ENSG0000000. IPI0029. IPR000910;IPR. NA # 5 a ENSG0000000. IPI0001. IPR007087;IPR. IPR006794 # # . with 1,977 more rows, and 2 more variables: `HGNC symbol` <chr>, # # `Tissue-specificity` <chr>
Translates a vector of identifiers, resulting a new vector, or a column of identifiers in a data frame by creating another column with the target identifiers.
translate_ids( d, ..., uploadlists = FALSE, ensembl = FALSE, hmdb = FALSE, ramp = FALSE, chalmers = FALSE, entity_type = NULL, keep_untranslated = TRUE, return_df = FALSE, organism = 9606, reviewed = TRUE, complexes = NULL, complexes_one_to_many = NULL )translate_ids( d, ..., uploadlists = FALSE, ensembl = FALSE, hmdb = FALSE, ramp = FALSE, chalmers = FALSE, entity_type = NULL, keep_untranslated = TRUE, return_df = FALSE, organism = 9606, reviewed = TRUE, complexes = NULL, complexes_one_to_many = NULL )
d |
Character vector or data frame. |
... |
At least two arguments, with or without names. The first of these arguments describes the source identifier, the rest of them describe the target identifier(s). The values of all these arguments must be valid identifier types as shown in Details. The names of the arguments are column names. In case of the first (source) ID the column must exist. For the rest of the IDs new columns will be created with the desired names. For ID types provided as arguments without names, the name of the ID type will be used for column name. |
uploadlists |
Force using the |
ensembl |
Logical: use data from Ensembl BioMart instead of UniProt. |
hmdb |
Logical: use HMDB ID translation data. |
ramp |
Logical: use RaMP ID translation data. |
chalmers |
Logical: use ID translation data from Chalmers Sysbio GEM. |
entity_type |
Character: "gene" and "smol" are short symbols for proteins, genes and small molecules respectively. Several other synonyms are also accepted. |
keep_untranslated |
In case the output is a data frame, keep the records where the source identifier could not be translated. At these records the target identifier will be NA. |
return_df |
Return a data frame even if the input is a vector. |
organism |
Character or integer, name or NCBI Taxonomy ID of the
organism (by default 9606 for human). Matters only if
|
reviewed |
Translate only reviewed ( |
complexes |
Logical: translate complexes by their members. Only
complexes where all members can be translated will be included in the
result. If |
complexes_one_to_many |
Logical: allow combinatorial expansion or
use only the first target identifier for each member of each complex.
If |
This function, depending on the uploadlists parameter, uses either
the uploadlists service of UniProt or plain UniProt queries to obtain
identifier translation tables. The possible values for from and to
are the identifier type abbreviations used in the UniProt API, please
refer to the table here: https://www.uniprot.org/help/api_idmapping.
In addition, simple synonyms are available which realize a uniform API
for the uploadlists and UniProt query based backends. These are the
followings:
| OmnipathR | Uploadlists | UniProt query | Ensembl BioMart |
| uniprot | ACC | id | uniprotswissprot |
| uniprot_entry | ID | entry name | |
| trembl | reviewed = FALSE | reviewed = FALSE | uniprotsptrembl |
| genesymbol | GENENAME | genes(PREFERRED) | external_gene_name |
| genesymbol_syn | genes(ALTERNATIVE) | external_synonym | |
| hgnc | HGNC_ID | database(HGNC) | hgnc_symbol |
| entrez | P_ENTREZGENEID | database(GeneID) | |
| ensembl | ENSEMBL_ID | ensembl_gene_id | |
| ensg | ENSEMBL_ID | ensembl_gene_id | |
| enst | ENSEMBL_TRS_ID | database(Ensembl) | ensembl_transcript_id |
| ensp | ENSEMBL_PRO_ID | ensembl_peptide_id | |
| ensgg | ENSEMBLGENOME_ID | ||
| ensgt | ENSEMBLGENOME_TRS_ID | ||
| ensgp | ENSEMBLGENOME_PRO_ID | ||
| protein_name | protein names | ||
| pir | PIR | database(PIR) | |
| ccds | database(CCDS) | ||
| refseqp | P_REFSEQ_AC | database(refseq) | |
| ipro | interpro | ||
| ipro_desc | interpro_description | ||
| ipro_sdesc | interpro_short_description | ||
| wikigene | wikigene_name | ||
| rnacentral | rnacentral | ||
| gene_desc | description | ||
| wormbase | database(WormBase) | ||
| flybase | database(FlyBase) | ||
| xenbase | database(Xenbase) | ||
| zfin | database(ZFIN) | ||
| pbd | PBD_ID | database(PDB) | pbd |
For a complete list of ID types and their synonyms, including metabolite and
chemical ID types which are not shown here, see id_types.
The mapping between identifiers can be ambiguous. In this case one row in the original data frame yields multiple rows or elements in the returned data frame or vector(s).
Data frame: if the input is a data frame or the input is a
vector and return_df is TRUE.
Vector: if the input is a vector, there is only one target
ID type and return_df is FALSE.
List of vectors: if the input is a vector, there are more than
one target ID types and return_df is FALSE. The names
of the list will be ID types (as they were column names, see
the description of the ... argument), and the list will also
include the source IDs.
d <- data.frame(uniprot_id = c('P00533', 'Q9ULV1', 'P43897', 'Q9Y2P5')) d <- translate_ids(d, uniprot_id = uniprot, genesymbol) d # uniprot_id genesymbol # 1 P00533 EGFR # 2 Q9ULV1 FZD4 # 3 P43897 TSFM # 4 Q9Y2P5 SLC27A5d <- data.frame(uniprot_id = c('P00533', 'Q9ULV1', 'P43897', 'Q9Y2P5')) d <- translate_ids(d, uniprot_id = uniprot, genesymbol) d # uniprot_id genesymbol # 1 P00533 EGFR # 2 Q9ULV1 FZD4 # 3 P43897 TSFM # 4 Q9Y2P5 SLC27A5
Especially when translating network interactions, where two ID columns exist
(source and target), it is convenient to call the same ID translation on
multiple columns. The translate_ids function is already able
to translate to multiple ID types in one call, but is able to work only from
one source column. Here too, multiple target IDs are supported. The source
columns can be listed explicitely, or they might share a common stem, in
this case the first element of ... will be used as stem, and the
column names will be created by adding the suffixes. The
suffixes are also used to name the target columns. If no
suffixes are provided, the name of the source columns will be added
to the name of the target columns. ID types can be defined the same way as
for translate_ids. The only limitation is that, if the source
columns are provided as stem+suffixes, they must be the same ID type.
translate_ids_multi( d, ..., suffixes = NULL, suffix_sep = "_", uploadlists = FALSE, ensembl = FALSE, hmdb = FALSE, chalmers = FALSE, entity_type = NULL, keep_untranslated = TRUE, organism = 9606, reviewed = TRUE )translate_ids_multi( d, ..., suffixes = NULL, suffix_sep = "_", uploadlists = FALSE, ensembl = FALSE, hmdb = FALSE, chalmers = FALSE, entity_type = NULL, keep_untranslated = TRUE, organism = 9606, reviewed = TRUE )
d |
A data frame. |
... |
At least two arguments, with or without names. These arguments
describe identifier columns, either the ones we translate from (source),
or the ones we translate to (target). Columns existing in the data frame
will be used as source columns. All the rest will be considered target
columns. Alternatively, the source columns can be defined as a stem and
a vector of suffixes, plus a separator between the stem and suffix. In
this case, the source columns will be the ones that exist in the data
frame with the suffixes added. The values of all these
arguments must be valid identifier types as shown at
|
suffixes |
Column name suffixes in case the names should be composed of stem and suffix. |
suffix_sep |
Character: separator between the stem and suffixes. |
uploadlists |
Force using the 'uploadlists' service from UniProt.
By default the plain query interface is used (implemented in
|
ensembl |
Logical: use data from Ensembl BioMart instead of UniProt. |
hmdb |
Logical: use HMDB ID translation data. |
chalmers |
Logical: use ID translation data from Chalmers Sysbio GEM. |
entity_type |
Character: "gene" and "smol" are short symbols for proteins, genes and small molecules respectively. Several other synonyms are also accepted. |
keep_untranslated |
In case the output is a data frame, keep the records where the source identifier could not be translated. At these records the target identifier will be NA. |
organism |
Character or integer, name or NCBI Taxonomy ID of the
organism (by default 9606 for human). Matters only if
|
reviewed |
Translate only reviewed ( |
A data frame with all source columns translated to all target identifiers. The number of new columns is the product of source and target columns. The target columns are distinguished by the suffexes added to their names.
ia <- omnipath() translate_ids_multi(ia, source = uniprot, target, ensp, ensembl = TRUE)ia <- omnipath() translate_ids_multi(ia, source = uniprot, target, ensp, ensembl = TRUE)
Retain only TrEMBL IDs
trembls_only(uniprots, organism = 9606)trembls_only(uniprots, organism = 9606)
uniprots |
Character vector of UniProt IDs. |
organism |
Character or integer: name or identifier of the organism. |
Character vector with only TrEMBL IDs.
trembls_only(c("Q05BL1", "A0A654IBU3", "P00533")) # [1] "Q05BL1" "A0A654IBU3"trembls_only(c("Q05BL1", "A0A654IBU3", "P00533")) # [1] "Q05BL1" "A0A654IBU3"
TRRUST v2 (https://www.grnpedia.org/trrust/) is a database of literature mined TF-target interactions for human and mouse.
trrust_download(organism = "human")trrust_download(organism = "human")
organism |
Character: either "human" or "mouse". |
A data frame of TF-target interactions.
trrust_interactions <- trrust_download() trrust_interactions # # A tibble: 11,698 x 4 # source_genesymbol target_genesymbol effect reference # <chr> <chr> <dbl> <chr> # 1 AATF BAX -1 22909821 # 2 AATF CDKN1A 0 17157788 # 3 AATF KLK3 0 23146908 # 4 AATF MYC 1 20549547 # 5 AATF TP53 0 17157788 # 6 ABL1 BAX 1 11753601 # 7 ABL1 BCL2 -1 11753601 # # . with 11,688 more rowstrrust_interactions <- trrust_download() trrust_interactions # # A tibble: 11,698 x 4 # source_genesymbol target_genesymbol effect reference # <chr> <chr> <dbl> <chr> # 1 AATF BAX -1 22909821 # 2 AATF CDKN1A 0 17157788 # 3 AATF KLK3 0 23146908 # 4 AATF MYC 1 20549547 # 5 AATF TP53 0 17157788 # 6 ABL1 BAX 1 11753601 # 7 ABL1 BCL2 -1 11753601 # # . with 11,688 more rows
Creates an ID translation table from UniProt data
uniprot_full_id_mapping_table( to, from = "accession", reviewed = TRUE, organism = 9606 )uniprot_full_id_mapping_table( to, from = "accession", reviewed = TRUE, organism = 9606 )
to |
Character or symbol: target ID type. See Details for possible values. |
from |
Character or symbol: source ID type. See Details for possible values. |
reviewed |
Translate only reviewed ( |
organism |
Integer, NCBI Taxonomy ID of the organism (by default 9606 for human). |
For both source and target ID type, this function accepts column codes
used by UniProt and some simple shortcuts defined here. For the UniProt
codes please refer to
https://www.uniprot.org/help/uniprotkb
The shortcuts are entrez, genesymbol, genesymbol_syn (synonym gene
symbols), hgnc, embl, refseqp (RefSeq protein), enst (Ensembl transcript),
uniprot_entry (UniProtKB AC, e.g. EGFR_HUMAN), protein_name (full name of
the protein), uniprot (UniProtKB ID, e.g. P00533). For a complete table
please refer to translate_ids.
A data frame (tibble) with columns 'From' and 'To', UniProt IDs and the corresponding foreign IDs, respectively.
uniprot_entrez <- uniprot_full_id_mapping_table(to = 'entrez') uniprot_entrez # # A tibble: 20,723 x 2 # From To # <chr> <chr> # 1 Q96R72 NA # 2 Q9UKL2 23538 # 3 Q9H205 144125 # 4 Q8NGN2 219873 # 5 Q8NGC1 390439 # # . with 20,713 more rowsuniprot_entrez <- uniprot_full_id_mapping_table(to = 'entrez') uniprot_entrez # # A tibble: 20,723 x 2 # From To # <chr> <chr> # 1 Q96R72 NA # 2 Q9UKL2 23538 # 3 Q9H205 144125 # 4 Q8NGN2 219873 # 5 Q8NGC1 390439 # # . with 20,713 more rows
TrEMBL to SwissProt by gene names
uniprot_genesymbol_cleanup(uniprots, organism = 9606, only_trembls = TRUE)uniprot_genesymbol_cleanup(uniprots, organism = 9606, only_trembls = TRUE)
uniprots |
Character vector possibly containing TrEMBL IDs. |
organism |
Character or integer: organism name or identifier. |
only_trembls |
Attempt to convert only known TrEMBL IDs of the organism. This is the recommended practice. |
Sometimes one gene or protein is represented by multiple identifiers in UniProt. These are typically slightly different isoforms, some of them having TrEMBL IDs, some of the SwissProt. For the purposes of most systems biology application, the most important is to identify the protein or gene in a way that we can recognize it in other datasets. Unfortunately UniProt or Ensembl do not seem to offer solution for this issue. Hence, if we find that a TrEMBL ID has a gene name which is also associated with a SwissProt ID, we replace this TrEMBL ID by that SwissProt. There might be a minor difference in their sequence, but most of the omics analyses do not even consider isoforms. And it is quite possible that later UniProt will convert the TrEMBL record to an isoform within the SwissProt record. Typically this translation is not so important (but still beneficial) for human, but for other organisms it is critical especially when translating from foreign identifiers.
This function accepts a mixed input of UniProt IDs and provides a distinct translation table that you can use to translate your data.
Data frame with two columns: "input" and "output". The first one contains all identifiers from the input vector 'uniprots'. The second one has the corresponding identifiers which are either SwissProt IDs with gene names identical to the TrEMBL IDs in the input, or if no such records are available, the output has the input items unchanged.
## Not run: uniprot_genesymbol_cleanup('Q6PB82', organism = 10090) # # A tibble: 1 × 2 # input output # <chr> <chr> # 1 Q6PB82 O70405 ## End(Not run)## Not run: uniprot_genesymbol_cleanup('Q6PB82', organism = 10090) # # A tibble: 1 × 2 # input output # <chr> <chr> # 1 Q6PB82 O70405 ## End(Not run)
Retrieves an identifier translation table from the UniProt ID Mapping service (https://www.uniprot.org/help/id_mapping).
uniprot_id_mapping_table(identifiers, from, to, chunk_size = NULL)uniprot_id_mapping_table(identifiers, from, to, chunk_size = NULL)
identifiers |
Character vector of identifiers |
from |
Character or symbol: type of the identifiers provided. See Details for possible values. |
to |
Character or symbol: identifier type to be retrieved from UniProt. See Details for possible values. |
chunk_size |
Integer: query the identifiers in chunks of this size. If you are experiencing download failures, try lower values. |
This function uses the uploadlists service of UniProt to obtain identifier
translation tables. The possible values for 'from' and 'to' are the
identifier type abbreviations used in the UniProt API, please refer to
the table here: uniprot_idmapping_id_types or
the table of synonyms supported by the current package:
translate_ids.
Note: if the number of identifiers is larger than the chunk size the log
message about the cache origin is not guaranteed to be correct (most
of the times it is still correct).
A data frame (tibble) with columns 'From' and 'To', the identifiers provided and the corresponding target IDs, respectively.
uniprot_genesymbol <- uniprot_id_mapping_table( c('P00533', 'P23771'), uniprot, genesymbol ) uniprot_genesymbol # # A tibble: 2 x 2 # From To # <chr> <chr> # 1 P00533 EGFR # 2 P23771 GATA3uniprot_genesymbol <- uniprot_id_mapping_table( c('P00533', 'P23771'), uniprot, genesymbol ) uniprot_genesymbol # # A tibble: 2 x 2 # From To # <chr> <chr> # 1 P00533 EGFR # 2 P23771 GATA3
UniProt identifier type label
uniprot_id_type(label)uniprot_id_type(label)
label |
Character: an ID type label, as shown in the table at
|
Character: the UniProt specific ID type label, or the input unchanged if it could not be translated (still might be a valid identifier name). This is the label that one can use in UniProt REST queries.
ensembl_id_type("entrez") # [1] "database(GeneID)"ensembl_id_type("entrez") # [1] "database(GeneID)"
ID types available in the UniProt ID Mapping service
uniprot_idmapping_id_types()uniprot_idmapping_id_types()
A data frame listing the ID types.
uniprot_idmapping_id_types()uniprot_idmapping_id_types()
In the intercellular network data frames produced by
intercell_network, by default each pair of annotations for
an interaction is represented in a separate row. This function drops the
annotations and keeps only the distinct interacting pairs.
unique_intercell_network(network, ...)unique_intercell_network(network, ...)
network |
An intercellular network data frame as produced by
|
... |
Additional columns to keep. Note: if these have multiple values for an interacting pair, only the first row will be preserved. |
A data frame with interacting pairs and interaction attributes.
icn <- intercell_network() icn_unique <- unique_intercell_network(icn)icn <- intercell_network() icn_unique <- unique_intercell_network(icn)
Separate evidences by direction and effect sign
unnest_evidences(data, longer = FALSE, .keep = FALSE)unnest_evidences(data, longer = FALSE, .keep = FALSE)
data |
An interaction data frame with "evidences" column. |
longer |
Logical: If TRUE, the "evidences" column is split into rows. |
.keep |
Logical: keep the "evidences" column. When unnesting to longer data frame, the "evidences" column will contain the unnested evidences, while the original column will be retained under the "all_evidences" name (if '.keep = TRUE'). |
The data frame with new columns or new rows by direction and sign.
## Not run: op <- omnipath_interactions(fields = "evidences") op <- unnest_evidences(op) colnames(op) ## End(Not run)## Not run: op <- omnipath_interactions(fields = "evidences") op <- unnest_evidences(op) colnames(op) ## End(Not run)
UniProt Uploadlists identifier type label
uploadlists_id_type(label, side = "from")uploadlists_id_type(label, side = "from")
label |
Character: an ID type label, as shown in the table at
|
side |
Character: either "from" or "to": direction of the mapping. |
Character: the UniProt Uploadlists specific ID type label, or the input unchanged if it could not be translated (still might be a valid identifier name). This is the label that one can use in UniProt Uploadlists (ID Mapping) queries.
ensembl_id_type("entrez") # [1] "GeneID"ensembl_id_type("entrez") # [1] "GeneID"
Retrieves the Supplementary Table S6 from Vinayagam et al. 2011. Find out more at https://doi.org/10.1126/scisignal.2001699.
vinayagam_download()vinayagam_download()
A data frame (tibble) with interactions.
vinayagam_interactions <- vinayagam_download() vinayagam_interactions # # A tibble: 34,814 x 5 # `Input-node Gen. `Input-node Gen. `Output-node Ge. `Output-node Ge. # <chr> <dbl> <chr> <dbl> # 1 C1orf103 55791 MNAT1 4331 # 2 MAST2 23139 DYNLL1 8655 # 3 RAB22A 57403 APPL2 55198 # 4 TRAP1 10131 EXT2 2132 # 5 STAT2 6773 COPS4 51138 # # . with 34,804 more rows, and 1 more variable: # # `Edge direction score` <dbl>vinayagam_interactions <- vinayagam_download() vinayagam_interactions # # A tibble: 34,814 x 5 # `Input-node Gen. `Input-node Gen. `Output-node Ge. `Output-node Ge. # <chr> <dbl> <chr> <dbl> # 1 C1orf103 55791 MNAT1 4331 # 2 MAST2 23139 DYNLL1 8655 # 3 RAB22A 57403 APPL2 55198 # 4 TRAP1 10131 EXT2 2132 # 5 STAT2 6773 COPS4 51138 # # . with 34,804 more rows, and 1 more variable: # # `Edge direction score` <dbl>
Starting from the selected nodes, recursively walks the ontology tree until it reaches either the root or leaf nodes. Collects all visited nodes.
walk_ontology_tree( terms, ancestors = TRUE, db_key = "go_basic", ids = TRUE, method = "gra", relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates", "negatively_regulates") )walk_ontology_tree( terms, ancestors = TRUE, db_key = "go_basic", ids = TRUE, method = "gra", relations = c("is_a", "part_of", "occurs_in", "regulates", "positively_regulates", "negatively_regulates") )
terms |
Character vector of ontology term IDs or names. A mixture of IDs and names can be provided. |
ancestors |
Logical: if |
db_key |
Character: key to identify the ontology database. For the
available keys see |
ids |
Logical: whether to return IDs or term names. |
method |
Character: either "gra" or "lst". The implementation to use for traversing the ontology tree. The graph based implementation is faster than the list based, the latter will be removed in the future. |
relations |
Character vector of ontology relation types. Only these relations will be used. |
Note: this function relies on the database manager, the first call might
take long because of the database load process. Subsequent calls within
a short period should be faster. See get_ontology_db.
Character vector of ontology IDs. If the input terms are all
leaves or roots NULL is returned. The starting nodes won't
be included in the result unless they fall onto the traversal path
from other nodes.
walk_ontology_tree(c('GO:0006241', 'GO:0044211')) # [1] "GO:0006139" "GO:0006220" "GO:0006221" "GO:0006241" "GO:0006725" # [6] "GO:0006753" "GO:0006793" "GO:0006796" "GO:0006807" "GO:0008150" # ... (truncated) walk_ontology_tree(c('GO:0006241', 'GO:0044211'), ancestors = FALSE) # [1] "GO:0044210" "GO:0044211" walk_ontology_tree( c('GO:0006241', 'GO:0044211'), ancestors = FALSE, ids = FALSE ) # [1] "'de novo' CTP biosynthetic process" "CTP salvage"walk_ontology_tree(c('GO:0006241', 'GO:0044211')) # [1] "GO:0006139" "GO:0006220" "GO:0006221" "GO:0006241" "GO:0006725" # [6] "GO:0006753" "GO:0006793" "GO:0006796" "GO:0006807" "GO:0008150" # ... (truncated) walk_ontology_tree(c('GO:0006241', 'GO:0044211'), ancestors = FALSE) # [1] "GO:0044210" "GO:0044211" walk_ontology_tree( c('GO:0006241', 'GO:0044211'), ancestors = FALSE, ids = FALSE ) # [1] "'de novo' CTP biosynthetic process" "CTP salvage"
Interaction records having certain extra attributes
with_extra_attrs(data, ...)with_extra_attrs(data, ...)
data |
An interaction data frame. |
... |
The name(s) of the extra attributes; NSE is supported. |
The data frame filtered to the records having the extra attribute.
i <- omnipath(fields = "extra_attrs") with_extra_attrs(i, Macrophage_type)i <- omnipath(fields = "extra_attrs") with_extra_attrs(i, Macrophage_type)
Interactions having references
with_references(data, resources = NULL)with_references(data, resources = NULL)
data |
An interaction data frame. |
resources |
Character: consider only these resources. If 'NULL', records with any reference will be accepted. |
A subset of the input interaction data frame.
cc <- import_post_translational_interactions(resources = 'CellChatDB') with_references(cc, 'CellChatDB')cc <- import_post_translational_interactions(resources = 'CellChatDB') with_references(cc, 'CellChatDB')
Zenodo is a repository of large scientific datasets. Many projects and publications make their datasets available at Zenodo. This function downloads an archive from Zenodo and extracts the requested file.
zenodo_download( path, reader = NULL, reader_param = list(), url_key = NULL, zenodo_record = NULL, zenodo_fname = NULL, url_param = list(), url_key_param = list(), ... )zenodo_download( path, reader = NULL, reader_param = list(), url_key = NULL, zenodo_record = NULL, zenodo_fname = NULL, url_param = list(), url_key_param = list(), ... )
path |
Character: path to the file within the archive. |
reader |
Optional, a function to read the connection. |
reader_param |
List: arguments for the reader function. |
url_key |
Character: name of the option containing the URL |
zenodo_record |
The Zenodo record ID, either integer or character. |
zenodo_fname |
The file name within the record. |
url_param |
List: variables to insert into the URL string (which is returned from the options). |
url_key_param |
List: variables to insert into the 'url_key'. |
... |
Passed to |
A connection
# an example from the OmnipathR::remap_tf_target_download function: remap_dorothea <- zenodo_download( zenodo_record = 3713238, zenodo_fname = 'tf_target_sources.zip', path = ( 'tf_target_sources/chip_seq/remap/gene_tf_pairs_genesymbol.txt' ), reader = read_tsv, reader_param = list( col_names = c( 'source_genesymbol', 'target_genesymbol', 'target_ensembl', 'score' ), col_types = cols(), progress = FALSE ), resource = 'ReMap' )# an example from the OmnipathR::remap_tf_target_download function: remap_dorothea <- zenodo_download( zenodo_record = 3713238, zenodo_fname = 'tf_target_sources.zip', path = ( 'tf_target_sources/chip_seq/remap/gene_tf_pairs_genesymbol.txt' ), reader = read_tsv, reader_param = list( col_names = c( 'source_genesymbol', 'target_genesymbol', 'target_ensembl', 'score' ), col_types = cols(), progress = FALSE ), resource = 'ReMap' )