| Title: | Cell Communication Analysis for Single Cell RNA Sequencing |
|---|---|
| Description: | dominoSignal is a package developed to analyze cell signaling through ligand-receptor-transcription factor networks in scRNAseq data. It takes as input information transcriptomic data, requiring counts, z-scored counts, and cluster labels, as well as information on transcription factor activity (such as from SCENIC) and a database of ligand and receptor pairings (such as from CellPhoneDB). This package creates an object storing ligand-receptor-transcription factor linkages by cluster and provides several methods for exploring, summarizing, and visualizing the analysis. |
| Authors: | Christopher Cherry [aut] (ORCID: <https://orcid.org/0000-0002-5481-0055>), Jacob T Mitchell [aut] (ORCID: <https://orcid.org/0000-0002-5370-9692>), Sushma Nagaraj [aut] (ORCID: <https://orcid.org/0000-0001-5166-1309>), Kavita Krishnan [aut, cre] (ORCID: <https://orcid.org/0000-0003-1345-0249>), Dmitrijs Lvovs [aut], Elana Fertig [ctb] (ORCID: <https://orcid.org/0000-0003-3204-342X>), Jennifer Elisseeff [ctb] (ORCID: <https://orcid.org/0000-0002-5066-1996>) |
| Maintainer: | Kavita Krishnan <[email protected]> |
| License: | GPL-3 | file LICENSE |
| Version: | 1.7.1 |
| Built: | 2026-10-15 20:43:31 UTC |
| Source: | https://github.com/bioc/dominoSignal |
This function adds a column to the internal rl 'map' used to map all receptor and receptor complexes to all ligand and ligand complexes.
add_rl_column(map, map_ref, conv, new_name)add_rl_column(map, map_ref, conv, new_name)
map |
RL signaling data frame. |
map_ref |
Name of column to match new data to |
conv |
Data frame matching current data in map to new data. |
new_name |
Name of new column to be created in RL map |
An updated RL signaling data frame
data(CellPhoneDB) lr_name <- data.frame("abbrev" = c("L", "R"), "full" = c("Ligand", "Receptor")) rl_map_expanded <- add_rl_column( map = CellPhoneDB$rl_map_tiny, map_ref = "type_A", conv = lr_name, new_name = "type_A_full" )data(CellPhoneDB) lr_name <- data.frame("abbrev" = c("L", "R"), "full" = c("Ligand", "Receptor")) rl_map_expanded <- add_rl_column( map = CellPhoneDB$rl_map_tiny, map_ref = "type_A", conv = lr_name, new_name = "type_A_full" )
domino() objectThis function calculates a signaling network. It requires a domino() object
preprocessed from create_domino() and returns a domino() object prepared for
plotting with the various plotting functions in this package.
build_domino( dom, max_tf_per_clust = 5, max_tf_pval = 0.01, max_rec_per_tf = 5, rec_tf_cor_threshold = 0.15, min_rec_percentage = 0.1 )build_domino( dom, max_tf_per_clust = 5, max_tf_pval = 0.01, max_rec_per_tf = 5, rec_tf_cor_threshold = 0.15, min_rec_percentage = 0.1 )
dom |
A |
max_tf_per_clust |
Maximum number of transcription factors called active in a cluster. |
max_tf_pval |
Maximum p-value from differential feature score test to call a transcription factor active in a cluster, serving as a significance threshold (used if object was created with TF selection method "clusters"). For selection with "variable" or "all", this argument is ignored, and TFs are ranked by max correlation with active receptors by cluster. |
max_rec_per_tf |
Maximum number of receptors to link to each transcription factor. |
rec_tf_cor_threshold |
Minimum Spearman correlation used to consider a receptor linked with a transcription factor. Increasing this will decrease the number of receptors linked to each transcription factor. |
min_rec_percentage |
Minimum proportion of cells in cluster expressing a receptor (0 to 1) for the receptor to be linked to transcription factors in that cluster. For receptor complexes, every component gene must exceed this threshold. |
A domino() object with a signaling network built
create_domino() to create a domino() object
data(DominoObjects) dom <- DominoObjects$dom_tiny # a relaxed example pbmc_dom_built_tiny <- build_domino( dom = dom, max_tf_pval = .05, max_tf_per_clust = Inf, max_rec_per_tf = Inf, rec_tf_cor_threshold = .1, min_rec_percentage = 0.01 )data(DominoObjects) dom <- DominoObjects$dom_tiny # a relaxed example pbmc_dom_built_tiny <- build_domino( dom = dom, max_tf_pval = .05, max_tf_per_clust = Inf, max_rec_per_tf = Inf, rec_tf_cor_threshold = .1, min_rec_percentage = 0.01 )
A list of four subsets of CellPhoneDB v4.0.0 data and resulting domino rl_map.
data("CellPhoneDB")data("CellPhoneDB")
A list of:
A subset of the CellPhoneDB gene_input.csv file.
A subset of the CellPhoneDB protein_input.csv file.
A subset of the CellPhoneDB complex_input.csv file.
A subset of the CellPhoneDB interaction_input.csv file.
The subset formatted as a receptor-ligand map by create_rl_map_cellphonedb().
https://github.com/ventolab/cellphonedb-data/archive/refs/tags/v4.0.0.tar.gz; subset with data-raw/tiny_data.R.
Creates a chord plot of expression of ligands that can activate a specified receptor where chord widths correspond to mean ligand expression by the cluster.
circos_ligand_receptor( dom, receptor, ligand_expression_threshold = 0.01, cell_idents = NULL, cell_colors = NULL )circos_ligand_receptor( dom, receptor, ligand_expression_threshold = 0.01, cell_idents = NULL, cell_colors = NULL )
dom |
A |
receptor |
Name of a receptor active in at least one cell type in the |
ligand_expression_threshold |
Minimum mean expression value of a ligand by a cell type for a chord to be rendered between the cell type and the receptor |
cell_idents |
Vector of clusters from cluster assignments in the |
cell_colors |
Named vector of color names or hex codes where names correspond to the plotted cell types and values to the colors. |
Renders a circos plot to the active graphics device
Other plotting:
cor_scatter(),
plot_differential_linkages()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny # basic usage circos_ligand_receptor(dom, receptor = "CXCR3") # specify colors cols <- c("red", "orange", "green") names(cols) <- dom_clusters(dom) circos_ligand_receptor(dom, receptor = "CXCR3", cell_colors = cols)data(DominoObjects) dom <- DominoObjects$built_dom_tiny # basic usage circos_ligand_receptor(dom, receptor = "CXCR3") # specify colors cols <- c("red", "orange", "green") names(cols) <- dom_clusters(dom) circos_ligand_receptor(dom, receptor = "CXCR3", cell_colors = cols)
Creates a heatmap of correlation values between receptors and transcription factors either with boolean threshold or with continuous values displayed
cor_heatmap( dom, bool = FALSE, bool_thresh = 0.15, title = TRUE, feats = NULL, recs = NULL, mark_connections = FALSE, ... )cor_heatmap( dom, bool = FALSE, bool_thresh = 0.15, title = TRUE, feats = NULL, recs = NULL, mark_connections = FALSE, ... )
dom |
A |
bool |
Boolean indicating whether the heatmap should be continuous or boolean. If boolean then bool_thresh will be used to determine how to define activity as positive or negative. |
bool_thresh |
Numeric indicating the threshold separating 'on' or 'off' for feature activity if making a boolean heatmap. |
title |
Either a string to use as the title or a boolean describing whether to include a title.
In order to pass the 'main' parameter to |
feats |
Either a vector of features to include in the heatmap or 'all' for all features. If left NULL then the features selected for the signaling network will be shown. |
recs |
Either a vector of receptors to include in the heatmap or 'all' for all receptors. If left NULL then the receptors selected in the signaling network connected to the features plotted will be shown. |
mark_connections |
Boolean indicating whether to add an 'x' in cells where there is a connected receptor or TF. Default FALSE. |
... |
Other parameters to pass to |
A heatmap rendered to the active graphics device
Heatmap Plotting Functions:
feat_heatmap(),
incoming_signaling_heatmap(),
signaling_heatmap()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny # basic usage cor_heatmap(dom, title = "PBMC R-TF Correlations") # show correlations above a specific value cor_heatmap(dom, bool = TRUE, bool_thresh = 0.1) # identify combinations that are connected cor_heatmap(dom, bool = FALSE, mark_connections = TRUE)data(DominoObjects) dom <- DominoObjects$built_dom_tiny # basic usage cor_heatmap(dom, title = "PBMC R-TF Correlations") # show correlations above a specific value cor_heatmap(dom, bool = TRUE, bool_thresh = 0.1) # identify combinations that are connected cor_heatmap(dom, bool = FALSE, mark_connections = TRUE)
Create a correlation plot between transcription factor activity scores and receptor expression
cor_scatter(dom, tf, rec, remove_rec_dropout = NULL, ...)cor_scatter(dom, tf, rec, remove_rec_dropout = NULL, ...)
dom |
A |
tf |
Target TF for plotting AUC score |
rec |
Target receptor for plotting expression |
remove_rec_dropout |
Whether to remove cells with zero expression for plot.
Default (NULL) will use the value of |
... |
Other parameters to pass to |
A ggplot scatter plot rendered in the active graphics device
Other plotting:
circos_ligand_receptor(),
plot_differential_linkages()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny cor_scatter(dom, "FLI1","CXCR3")data(DominoObjects) dom <- DominoObjects$built_dom_tiny cor_scatter(dom, "FLI1","CXCR3")
Count occurrences of linkages across multiple domino results from a linkage summary
count_linkage( link_summary, cluster, group.by = NULL, linkage = "rec_lig", subject_names = NULL )count_linkage( link_summary, cluster, group.by = NULL, linkage = "rec_lig", subject_names = NULL )
link_summary |
a |
cluster |
the name of the cell cluster being compared across multiple domino results |
group.by |
the name of the column in |
linkage |
a stored linkage from the |
subject_names |
a vector of subject_names from the linkage_summary to restrict counting to.
If NULL, all subjects in |
A data frame with columns for the unique linkage features and the counts of how many times the linkage occurred across the compared domino results. If group.by is used, counts of the linkages are also provided as columns named by the unique values of the group.by variable.
summarize_linkages() to create linkage_summary() objects, test_differential_linkages() to compare links across groups
Differential Signaling Workflow:
plot_differential_linkages(),
summarize_linkages(),
test_differential_linkages()
data(LinkageSummary) count_linkage( link_summary = LinkageSummary$linkage_sum_tiny, cluster = "C1", group.by = "group", linkage = "rec") count_linkage( link_summary = LinkageSummary$linkage_sum_tiny, cluster = "C1", group.by = "group", linkage = "tfs_rec", subject_names = c("P1", "P2", "P4", "P5"))data(LinkageSummary) count_linkage( link_summary = LinkageSummary$linkage_sum_tiny, cluster = "C1", group.by = "group", linkage = "rec") count_linkage( link_summary = LinkageSummary$linkage_sum_tiny, cluster = "C1", group.by = "group", linkage = "tfs_rec", subject_names = c("P1", "P2", "P4", "P5"))
domino() object and prepare it for network constructionThis function reads in a receptor ligand signaling database, cell level
features of some kind (ie. output from pySCENIC), z-scored single cell data,
and cluster id for single cell data, calculates a correlation matrix between
receptors and other features (this is transcription factor module scores if
using pySCENIC), and finds features enriched by cluster. It will return a
domino() object prepared for build_domino(), which will calculate a signaling
network.
create_domino( rl_map, features, counts, z_scores, clusters, tf_targets = NULL, verbose = TRUE, use_complexes = TRUE, rec_min_thresh = 0.025, remove_rec_dropout = FALSE, tf_selection_method = "clusters", tf_variance_quantile = 0.5 )create_domino( rl_map, features, counts, z_scores, clusters, tf_targets = NULL, verbose = TRUE, use_complexes = TRUE, rec_min_thresh = 0.025, remove_rec_dropout = FALSE, tf_selection_method = "clusters", tf_variance_quantile = 0.5 )
rl_map |
Data frame where each row describes a receptor-ligand interaction with required columns gene_A & gene_B including the gene names for the receptor and ligand and type_A & type_B annotating if genes A and B are a ligand (L) or receptor (R). Each interaction must pair one receptor with one ligand (rows that do not, such as ligand-ligand or missing types) are excluded with a warning. |
features |
Either a path to a csv containing cell level features of interest (ie. the auc matrix from pySCENIC) or named matrix with cells as columns and features as rows. |
counts |
Counts matrix for the data. Used to threshold receptors based on expression ( |
z_scores |
Matrix containing z-scored expression data for all cells with cells as columns and features as rows. Receptor genes that pass |
clusters |
Named factor containing cell cluster with names as cells. |
tf_targets |
Optional. A list where names are transcription factors and the stored values are character vectors of genes in the transcription factor's regulon. |
verbose |
Boolean indicating whether or not to print progress during computation. |
use_complexes |
Boolean indicating whether you wish to use receptor/ligand complexes in the receptor ligand signaling database. If FALSE, receptor/ligand pairs where either functions as a protein complex will not be considered when constructing the signaling network. |
rec_min_thresh |
Minimum expression level of receptors by cell. Default is 0.025 or 2.5 percent of all cells in the data set. This is important when calculating correlation to connect receptors to transcription activity. If this threshold is too low then correlation calculations will proceed with very few cells with non-zero expression. |
remove_rec_dropout |
Whether to remove receptors with 0 expression counts when calculating correlations. This can reduce false positive correlation calculations when receptors have high dropout rates. |
tf_selection_method |
Selection of which method to target transcription factors. Options:
|
tf_variance_quantile |
Quantile of coefficient of variation used to threshold features to take if |
A domino() object
create_rl_map_cellphonedb() for creating receptor-ligand maps,
create_regulon_list_scenic() for creating regulon lists from pySCENIC output,
and build_domino() for building signaling networks from domino() objects created by this function.
data(CellPhoneDB) data(SCENIC) data(PBMC) pbmc_dom_tiny <- create_domino( rl_map = CellPhoneDB$rl_map_tiny, features = SCENIC$auc_tiny, counts = PBMC$count_tiny, z_scores = PBMC$zscore_tiny, clusters = PBMC$clusters_tiny, tf_targets = SCENIC$regulon_list_tiny, use_complexes = TRUE, remove_rec_dropout = FALSE, verbose = FALSE )data(CellPhoneDB) data(SCENIC) data(PBMC) pbmc_dom_tiny <- create_domino( rl_map = CellPhoneDB$rl_map_tiny, features = SCENIC$auc_tiny, counts = PBMC$count_tiny, z_scores = PBMC$zscore_tiny, clusters = PBMC$clusters_tiny, tf_targets = SCENIC$regulon_list_tiny, use_complexes = TRUE, remove_rec_dropout = FALSE, verbose = FALSE )
Generates a list of transcription factors and the genes targeted by the transcription factor as part of their regulon inferred by pySCENIC
create_regulon_list_scenic(regulons)create_regulon_list_scenic(regulons)
regulons |
Data frame or file path to the table of the output of the ctx function from pySCENIC |
A list where names are transcription factors and the stored values are character vectors of genes in the inferred regulons
data(SCENIC) regulon_list_tiny <- create_regulon_list_scenic(regulons = SCENIC$regulons_tiny)data(SCENIC) regulon_list_tiny <- create_regulon_list_scenic(regulons = SCENIC$regulons_tiny)
Generates a data frame of receptor-ligand interactions from a CellPhoneDB database annotating the genes encoding the interacting ligands and receptors to be queried in transcriptomic data.
create_rl_map_cellphonedb( genes, proteins, interactions, complexes = NULL, database_name = "CellPhoneDB", gene_conv = NULL, gene_conv_host = "https://www.ensembl.org", alternate_convert = FALSE, alternate_convert_table = NULL )create_rl_map_cellphonedb( genes, proteins, interactions, complexes = NULL, database_name = "CellPhoneDB", gene_conv = NULL, gene_conv_host = "https://www.ensembl.org", alternate_convert = FALSE, alternate_convert_table = NULL )
genes |
data frame or file path to table of gene names in uniprot, hgnc_symbol, or ensembl format in CellPhoneDB database format |
proteins |
data frame or file path to table of protein features in CellPhoneDB format |
interactions |
data frame or file path to table of protein-protein interactions in CellPhoneDB format |
complexes |
optional: data frame or file path to table of protein complexes in CellPhoneDB format |
database_name |
name of the database being used, stored in output |
gene_conv |
character vector of length 2 formatted as (from, to) or (source, target)
if gene conversion to orthologs is desired; options are ENSMUSG, ENSG, MGI, or HGNC (for from) and MGI or HGNC (for to). Identifiers in the |
gene_conv_host |
host for conversion; default ensembl, could also use mirrors if desired |
alternate_convert |
boolean if you would like to use a non-ensembl method of conversion (must supply table; not recommended, use only if ensembl is down) |
alternate_convert_table |
a data frame with column names corresponding to gene symbol types (mm.ens, hs.ens, mgi, hgnc) and rows corresponding to the gene symbols themselves for use with alternate_convert = TRUE |
Data frame where each row describes a possible receptor-ligand interaction
create_domino() where output of this function can be used
data(CellPhoneDB) rl_map_tiny <- create_rl_map_cellphonedb(genes = CellPhoneDB$genes_tiny, proteins = CellPhoneDB$proteins_tiny, interactions = CellPhoneDB$interactions_tiny, complexes = CellPhoneDB$complexes_tiny) ## Not run: rl_map_tiny_conv <- create_rl_map_cellphonedb(genes = CellPhoneDB$genes_tiny, proteins = CellPhoneDB$proteins_tiny, interactions = CellPhoneDB$interactions_tiny, gene_conv = c("HGNC", "MGI"), gene_conv_host = "https://beta.ensembl.org") ## End(Not run) # Using alternate conversion table instead of biomaRt # (this simplified table is for illustration only, this is # NOT the correct way to convert between HGNC and MGI symbols) hs_genes <- unique(CellPhoneDB$genes_tiny$gene_name) ortho_table <- data.frame( hgnc = hs_genes, mgi = paste0(substr(hs_genes, 1, 1), tolower(substr(hs_genes, 2, nchar(hs_genes))))) rl_map_tiny_alt <- create_rl_map_cellphonedb(genes = CellPhoneDB$genes_tiny, proteins = CellPhoneDB$proteins_tiny, interactions = CellPhoneDB$interactions_tiny, complexes = CellPhoneDB$complexes_tiny, gene_conv = c("HGNC", "MGI"), alternate_convert = TRUE, alternate_convert_table = ortho_table)data(CellPhoneDB) rl_map_tiny <- create_rl_map_cellphonedb(genes = CellPhoneDB$genes_tiny, proteins = CellPhoneDB$proteins_tiny, interactions = CellPhoneDB$interactions_tiny, complexes = CellPhoneDB$complexes_tiny) ## Not run: rl_map_tiny_conv <- create_rl_map_cellphonedb(genes = CellPhoneDB$genes_tiny, proteins = CellPhoneDB$proteins_tiny, interactions = CellPhoneDB$interactions_tiny, gene_conv = c("HGNC", "MGI"), gene_conv_host = "https://beta.ensembl.org") ## End(Not run) # Using alternate conversion table instead of biomaRt # (this simplified table is for illustration only, this is # NOT the correct way to convert between HGNC and MGI symbols) hs_genes <- unique(CellPhoneDB$genes_tiny$gene_name) ortho_table <- data.frame( hgnc = hs_genes, mgi = paste0(substr(hs_genes, 1, 1), tolower(substr(hs_genes, 2, nchar(hs_genes))))) rl_map_tiny_alt <- create_rl_map_cellphonedb(genes = CellPhoneDB$genes_tiny, proteins = CellPhoneDB$proteins_tiny, interactions = CellPhoneDB$interactions_tiny, complexes = CellPhoneDB$complexes_tiny, gene_conv = c("HGNC", "MGI"), alternate_convert = TRUE, alternate_convert_table = ortho_table)
A function to pull cluster information from a domino() object
dom_clusters(dom, labels = FALSE)dom_clusters(dom, labels = FALSE)
dom |
a |
labels |
A boolean for whether to return the cluster labels for each cell or the clusters used for inferring communication |
A vector containing either the names of the clusters used or factors of the cluster label for each individual cell
Other accessors:
dom_correlations(),
dom_counts(),
dom_database(),
dom_de(),
dom_info(),
dom_linkages(),
dom_network_items(),
dom_signaling(),
dom_tf_activation(),
dom_zscores()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny cluster_names <- dom_clusters(dom) cell_cluster_label <- dom_clusters(dom, labels = TRUE)data(DominoObjects) dom <- DominoObjects$built_dom_tiny cluster_names <- dom_clusters(dom) cell_cluster_label <- dom_clusters(dom, labels = TRUE)
A function to pull receptor-transcription factor correlations from a domino() object
dom_correlations(dom, type = "rl")dom_correlations(dom, type = "rl")
dom |
a |
type |
either "rl" or "complex", to select between the receptor-ligand or complex correlation matrix |
A matrix containing the correlation values for each receptor (row) by transcription factor (column)
Other accessors:
dom_clusters(),
dom_counts(),
dom_database(),
dom_de(),
dom_info(),
dom_linkages(),
dom_network_items(),
dom_signaling(),
dom_tf_activation(),
dom_zscores()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny cor_matrix <- dom_correlations(dom, "rl")data(DominoObjects) dom <- DominoObjects$built_dom_tiny cor_matrix <- dom_correlations(dom, "rl")
A function to pull gene expression from a domino() object
dom_counts(dom)dom_counts(dom)
dom |
a |
A matrix containing the gene expression values for each gene (row) by cell (column)
Other accessors:
dom_clusters(),
dom_correlations(),
dom_database(),
dom_de(),
dom_info(),
dom_linkages(),
dom_network_items(),
dom_signaling(),
dom_tf_activation(),
dom_zscores()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny counts <- dom_counts(dom)data(DominoObjects) dom <- DominoObjects$built_dom_tiny counts <- dom_counts(dom)
A function to pull database information from a domino() object
dom_database(dom, name_only = TRUE)dom_database(dom, name_only = TRUE)
dom |
a |
name_only |
a boolean for whether to return only the name of the database used or the entire database that is stored. Default TRUE. |
A vector of unique databases used in building the domino() object OR
a data frame that includes the database information used in the domino() object creation
Other accessors:
dom_clusters(),
dom_correlations(),
dom_counts(),
dom_de(),
dom_info(),
dom_linkages(),
dom_network_items(),
dom_signaling(),
dom_tf_activation(),
dom_zscores()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny database_name <- dom_database(dom) full_database <- dom_database(dom, name_only = FALSE)data(DominoObjects) dom <- DominoObjects$built_dom_tiny database_name <- dom_database(dom) full_database <- dom_database(dom, name_only = FALSE)
A function to pull differential expression p-values from a domino() object
dom_de(dom)dom_de(dom)
dom |
a |
A matrix containing the p-values for differential expression of transcription factors (rows) in each cluster (columns)
Other accessors:
dom_clusters(),
dom_correlations(),
dom_counts(),
dom_database(),
dom_info(),
dom_linkages(),
dom_network_items(),
dom_signaling(),
dom_tf_activation(),
dom_zscores()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny de_mat <- dom_de(dom)data(DominoObjects) dom <- DominoObjects$built_dom_tiny de_mat <- dom_de(dom)
A function to pull the parameters used when running build_domino() from a domino() object
dom_info(dom)dom_info(dom)
dom |
a |
A list containing booleans for whether the object has been created and built, the parameters
used in create_domino() and build_domino() to infer the signaling network, and the
dominoSignal versions used to create and build the object (NULL if not recorded)
Other accessors:
dom_clusters(),
dom_correlations(),
dom_counts(),
dom_database(),
dom_de(),
dom_linkages(),
dom_network_items(),
dom_signaling(),
dom_tf_activation(),
dom_zscores()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny build_details <- dom_info(dom)data(DominoObjects) dom <- DominoObjects$built_dom_tiny build_details <- dom_info(dom)
A function to pull linkages from a domino() object
dom_linkages( dom, link_type = c("complexes", "receptor-ligand", "tf-target", "tf-receptor", "receptor", "incoming-ligand"), by_cluster = FALSE )dom_linkages( dom, link_type = c("complexes", "receptor-ligand", "tf-target", "tf-receptor", "receptor", "incoming-ligand"), by_cluster = FALSE )
dom |
a |
link_type |
one value (out of "complexes", "receptor-ligand", "tf-target", "tf-receptor", "receptor", "incoming-ligand") used to select the desired type of linkage. Note that "receptor" and "incoming-ligand" are only available when by_cluster is set to TRUE. |
by_cluster |
A boolean to indicate whether to return linkages overall or by cluster |
A list containing linkages between some combination of receptors, ligands, transcription factors, and clusters
Other accessors:
dom_clusters(),
dom_correlations(),
dom_counts(),
dom_database(),
dom_de(),
dom_info(),
dom_network_items(),
dom_signaling(),
dom_tf_activation(),
dom_zscores()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny complexes <- dom_linkages(dom, "complexes") tf_rec_by_cluster <- dom_linkages(dom, "tf-receptor", TRUE)data(DominoObjects) dom <- DominoObjects$built_dom_tiny complexes <- dom_linkages(dom, "complexes") tf_rec_by_cluster <- dom_linkages(dom, "tf-receptor", TRUE)
This function collates all of the features, receptors, or ligands found in a
signaling network anywhere in a list of clusters. This can be useful for
comparing signaling networks across two separate conditions. In order to run
this build_domino() must be run on the object previously.
dom_network_items(dom, clusters = NULL, which_return = NULL)dom_network_items(dom, clusters = NULL, which_return = NULL)
dom |
a |
clusters |
vector indicating clusters to collate network items from. If left as NULL then all clusters will be included. |
which_return |
string indicating whether to collate "features", "receptors", or "ligands". If NULL then a list of all three will be returned. |
A vector containing all features, receptors, or ligands in the data set or a list containing all three.
Other accessors:
dom_clusters(),
dom_correlations(),
dom_counts(),
dom_database(),
dom_de(),
dom_info(),
dom_linkages(),
dom_signaling(),
dom_tf_activation(),
dom_zscores()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny monocyte_receptors <- dom_network_items(dom, "CD14_monocyte", "receptors") all_tfs <- dom_network_items(dom, which_return = "features")data(DominoObjects) dom <- DominoObjects$built_dom_tiny monocyte_receptors <- dom_network_items(dom, "CD14_monocyte", "receptors") all_tfs <- dom_network_items(dom, which_return = "features")
A function to pull signaling matrices from a domino() object
dom_signaling() and dom_signalling() are synonyms.
dom_signaling(dom, cluster = NULL) dom_signalling(dom, cluster = NULL)dom_signaling(dom, cluster = NULL) dom_signalling(dom, cluster = NULL)
dom |
a |
cluster |
either NULL to indicate global signaling or a specific cluster for which a signaling matrix is desired |
A data frame containing the signaling score through each ligand (row) by each cluster (column) OR . a data frame containing the global summed signaling scores between receptors (rows) and ligands (columns) of each cluster
Other accessors:
dom_clusters(),
dom_correlations(),
dom_counts(),
dom_database(),
dom_de(),
dom_info(),
dom_linkages(),
dom_network_items(),
dom_tf_activation(),
dom_zscores()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny monocyte_signaling <- dom_signaling(dom, cluster = "CD14_monocyte")data(DominoObjects) dom <- DominoObjects$built_dom_tiny monocyte_signaling <- dom_signaling(dom, cluster = "CD14_monocyte")
A function to pull transcription factor activity scores from a domino() object
dom_tf_activation(dom)dom_tf_activation(dom)
dom |
a |
A matrix containing the transcription factor activity scores for each TF (row) by cell (column)
Other accessors:
dom_clusters(),
dom_correlations(),
dom_counts(),
dom_database(),
dom_de(),
dom_info(),
dom_linkages(),
dom_network_items(),
dom_signaling(),
dom_zscores()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny tf_activation <- dom_tf_activation(dom)data(DominoObjects) dom <- DominoObjects$built_dom_tiny tf_activation <- dom_tf_activation(dom)
Constructs a data frame of ligand-receptor-TF signaling triplets from a built domino() object.
Expression values are averaged across cells within each cluster (ligands from sending clusters,
receptors and TFs from receiving clusters).
dom_to_df(dom, send_clusters = NULL, rec_clusters = NULL, exp_type = "counts")dom_to_df(dom, send_clusters = NULL, rec_clusters = NULL, exp_type = "counts")
dom |
A built |
send_clusters |
Character/factor vector of cluster names for ligand signals. If NULL (default), uses all clusters in dom. |
rec_clusters |
Character/factor vector of cluster names for receptor/TF signals. If NULL (default), uses all clusters in dom. |
exp_type |
Either "counts" or "z_scores" for expression type |
Data frame with columns: ligand, receptor, transcription_factor, ligand_exp, rec_exp, tf_auc, sending_cl, receiving_cl. Each row is a ligand-receptor-TF triplet from a sender-to-receiver cluster pair with corresponding mean expression values. Empty data frame returned if no valid interactions found.
data("DominoObjects") dom <- DominoObjects$built_dom_tiny df <- dom_to_df(dom, exp_type = "z_scores") head(df)data("DominoObjects") dom <- DominoObjects$built_dom_tiny df <- dom_to_df(dom, exp_type = "z_scores") head(df)
A function to pull z-scored expression from a domino() object
dom_zscores(dom)dom_zscores(dom)
dom |
a |
A matrix containing the z-scored gene expression values for each gene (row) by cell (column)
Other accessors:
dom_clusters(),
dom_correlations(),
dom_counts(),
dom_database(),
dom_de(),
dom_info(),
dom_linkages(),
dom_network_items(),
dom_signaling(),
dom_tf_activation()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny zscores <- dom_zscores(dom)data(DominoObjects) dom <- DominoObjects$built_dom_tiny zscores <- dom_zscores(dom)
The domino class contains all information necessary to calculate receptor-ligand signaling. It contains z-scored expression, cell cluster labels, feature values, and a referenced receptor-ligand database formatted as a receptor-ligand map. Calculated intermediate values are also stored.
An instance of class domino
db_infoList of data sets from receptor-ligand database
countsRaw count gene expression data
z_scoresMatrix of z-scored expression data with cells as columns
clustersNamed factor with cluster identity of each cell
featuresMatrix of features (TFs) to correlate receptor-ligand expression with. Cells are columns and features are rows.
corCorrelation matrix of receptor expression to features.
linkagesList of lists containing info linking cluster->tf->rec->lig
clust_deData frame containing differential expression results for features by cluster.
miscList of miscellaneous info pertaining to run parameters etc., including the
dominoSignal versions used to create (create_version) and build (build_version) the object.
cl_signaling_matricesIncoming signaling matrix for each cluster
signalingSignaling matrix between all clusters.
A list of two domino objects built from the example data subsets (PBMC, SCENIC, and CellPhoneDB).
A domino() object created using create_domino().
The same object after running build_domino().
data("DominoObjects")data("DominoObjects")
An object of class list of length 2.
Generated with data-raw/tiny_data.R.
Creates a heatmap of transcription factor activity scores by cells grouped by cluster.
feat_heatmap( dom, feats = NULL, bool = FALSE, bool_thresh = 0.2, title = TRUE, norm = FALSE, cols = NULL, ann_cols = TRUE, min_thresh = NULL, max_thresh = NULL, ... )feat_heatmap( dom, feats = NULL, bool = FALSE, bool_thresh = 0.2, title = TRUE, norm = FALSE, cols = NULL, ann_cols = TRUE, min_thresh = NULL, max_thresh = NULL, ... )
dom |
A |
feats |
Either a vector of features to include in the heatmap or 'all' for all features. If left NULL then the features selected for the signaling network will be shown. |
bool |
Boolean indicating whether the heatmap should be continuous or boolean. If boolean then bool_thresh will be used to determine how to define activity as positive or negative. |
bool_thresh |
Numeric indicating the threshold separating 'on' or 'off' for feature activity if making a boolean heatmap. |
title |
Either a string to use as the title or a boolean describing whether to include a title.
In order to pass the 'main' parameter to |
norm |
Boolean indicating whether or not to normalize the transcription factors to their max value. |
cols |
Named vector of colors to annotate cells by cluster color. Values are taken as colors and names as cluster. If left as NULL then default ggplot colors will be generated. |
ann_cols |
Boolean indicating whether to include cell cluster as a column annotation.
Colors can be defined with cols. If FALSE then custom annotations can be passed to |
min_thresh |
Minimum threshold for color scaling if not a boolean heatmap |
max_thresh |
Maximum threshold for color scaling if not a boolean heatmap |
... |
Other parameters to pass to |
A heatmap rendered to the active graphics device
Heatmap Plotting Functions:
cor_heatmap(),
incoming_signaling_heatmap(),
signaling_heatmap()
# basic usage data(DominoObjects) dom <- DominoObjects$built_dom_tiny feat_heatmap(dom) # using thresholds feat_heatmap( dom, min_thresh = 0.1, max_thresh = 0.6, bool = FALSE )# basic usage data(DominoObjects) dom <- DominoObjects$built_dom_tiny feat_heatmap(dom) # using thresholds feat_heatmap( dom, min_thresh = 0.1, max_thresh = 0.6, bool = FALSE )
Create a gene association network for genes from a given cluster. The selected cluster acts as the receptor for the gene association network, so only ligands, receptors, and features associated with the receptor cluster will be included in the plot.
gene_network( dom, clust = NULL, OutgoingSignalingClust = NULL, class_cols = c(lig = "#FF685F", rec = "#47a7ff", feat = "#39C740"), cols = NULL, lig_scale = 1, layout = "grid", ... )gene_network( dom, clust = NULL, OutgoingSignalingClust = NULL, class_cols = c(lig = "#FF685F", rec = "#47a7ff", feat = "#39C740"), cols = NULL, lig_scale = 1, layout = "grid", ... )
dom |
A |
clust |
Receptor cluster to create the gene association network for. A vector of clusters may be provided. This is a required parameter. |
OutgoingSignalingClust |
Vector of clusters to plot the outgoing signaling from |
class_cols |
Named vector of colors used to color classes of vertices. Values must be colors and names must be classes ('rec', 'lig', and 'feat' for receptors, ligands, and features). |
cols |
Named vector of colors for individual genes. Genes not included in this vector will be colored according to class_cols. |
lig_scale |
FALSE or a numeric value to scale the size of ligand vertices based on z-scored expression in the data set. |
layout |
Type of layout to use. Options are 'grid', 'random', 'sphere', 'circle', 'fr' for Fruchterman-Reingold force directed layout, and 'kk' for Kamada Kawai for directed layout. |
... |
Other parameters to pass to plot() with an igraph object. See igraph manual for options. |
An igraph plot rendered to the active graphics device. A list with the igraph object (graph) and
the layout matrix (layout) is returned invisibly, or NULL if no signaling is found for the clusters.
Network Plotting Functions:
signaling_network()
# basic usage data(DominoObjects) dom <- DominoObjects$built_dom_tiny gene_network( dom, clust = "CD8_T_cell", OutgoingSignalingClust = "CD14_monocyte" )# basic usage data(DominoObjects) dom <- DominoObjects$built_dom_tiny gene_network( dom, clust = "CD8_T_cell", OutgoingSignalingClust = "CD14_monocyte" )
Creates a heatmap of a cluster incoming signaling matrix. Each cluster has a
list of ligands capable of activating its enriched transcription factors. The
function creates a heatmap of cluster average expression for all of those
ligands. A list of all cluster incoming signaling matrices can be found in
the cl_signaling_matrices slot of a domino() object as an alternative to this
plotting function.
incoming_signaling_heatmap() and incoming_signalling_heatmap() are synonyms.
incoming_signaling_heatmap( dom, rec_clust, clusts = NULL, min_thresh = -Inf, max_thresh = Inf, scale = "none", normalize = "none", title = TRUE, ... ) incoming_signalling_heatmap( dom, rec_clust, clusts = NULL, min_thresh = -Inf, max_thresh = Inf, scale = "none", normalize = "none", title = TRUE, ... )incoming_signaling_heatmap( dom, rec_clust, clusts = NULL, min_thresh = -Inf, max_thresh = Inf, scale = "none", normalize = "none", title = TRUE, ... ) incoming_signalling_heatmap( dom, rec_clust, clusts = NULL, min_thresh = -Inf, max_thresh = Inf, scale = "none", normalize = "none", title = TRUE, ... )
dom |
A |
rec_clust |
Which cluster to select as the receptor. Must match naming of clusters in the |
clusts |
Vector of clusters to be included. If NULL then all clusters are used. |
min_thresh |
Minimum signaling threshold for plotting. Defaults to -Inf for no threshold. |
max_thresh |
Maximum signaling threshold for plotting. Defaults to Inf for no threshold. |
scale |
How to scale the values (after thresholding). Options are 'none', 'sqrt' for square root, or 'log' for log10(x+1). |
normalize |
Options to normalize the matrix. Accepted inputs are 'none' for no normalization, 'rec_norm' to normalize to the maximum value with each receptor cluster, or 'lig_norm' to normalize to the maximum value within each ligand cluster |
title |
Either a string to use as the title or a boolean describing whether to include a title.
In order to pass the 'main' parameter to |
... |
Other parameters to pass to |
a Heatmap rendered to the active graphics device
Heatmap Plotting Functions:
cor_heatmap(),
feat_heatmap(),
signaling_heatmap()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny # incoming signaling of the CD8 T cells incoming_signaling_heatmap(dom, "CD8_T_cell")data(DominoObjects) dom <- DominoObjects$built_dom_tiny # incoming signaling of the CD8 T cells incoming_signaling_heatmap(dom, "CD8_T_cell")
The linkage summary class contains linkages established in multiple domino
objects through gene regulatory network inference and reference to receptor-
ligand databases. A data frame summarizing meta features that describe the
domino() objects compared in the linkage summary facilitates comparisons of
established linkages and differential signaling interactions across categorical
sample covariates.
an instance of class linkage_summary
subject_namesunique names for each domino result included in the summary
subject_metadata.frame with each row describing one subject and columns describing features of the
subjects by which to draw comparisons of signaling networks. The first column holds the subject names, in the same order as subject_names.
subject_linkagesnested list of linkages inferred for each subject, named by and in the same order as subject_names. Lists are stored in a
hierarchical structure of subject-cluster-linkages, including
transcription factors (tfs), links between transcription factors and receptors (tfs_rec), active receptors (rec), links between
receptors and ligands(rec_lig), and incoming ligands (incoming_lig)
A list containing a mock linkages summary across six subjects in two groups,
and the result of running test_differential_linkages() on it.
data("LinkageSummary")data("LinkageSummary")
A list of:
A linkage_summary() object created with mock data.
Results from running test_differential_linkages() for receptors (linkage = "rec") in cluster "C1", grouped by "group".
Generated with data-raw/tiny_data.R.
Creates a data frame of mean ligand expression for use in plotting a circos plot of ligand expression and saving tables of mean expression.
mean_ligand_expression(x, ligands, cell_ident, cell_barcodes, destination)mean_ligand_expression(x, ligands, cell_ident, cell_barcodes, destination)
x |
Gene by cell expression matrix |
ligands |
Character vector of ligand genes to be quantified |
cell_ident |
Vector of cell type (identity) names for which to calculate mean ligand gene expression |
cell_barcodes |
Vector of cell barcodes (colnames of x) belonging to cell_ident to calculate mean expression across |
destination |
Name of the receptor with which each ligand interacts |
A data frame of ligand expression targeting the specified receptor
data(DominoObjects) counts <- dom_counts(DominoObjects$built_dom_tiny) mean_exp <- mean_ligand_expression(counts, ligands = c("PTPRC", "FASLG"), cell_ident = "CD14_monocyte", cell_barcodes = colnames(counts), destination = "FAS" )data(DominoObjects) counts <- dom_counts(DominoObjects$built_dom_tiny) mean_exp <- mean_ligand_expression(counts, ligands = c("PTPRC", "FASLG"), cell_ident = "CD14_monocyte", cell_barcodes = colnames(counts), destination = "FAS" )
A subset of the preprocessed 10x Genomics PBMC 3k scRNAseq data.
data("PBMC")data("PBMC")
A list of:
Subset of the sparse counts matrix (dgCMatrix), genes x cells.
Subset of z-scored expression matrix, genes x cells.
Factor of cell-type labels named by cell barcode.
https://doi.org/10.5281/zenodo.10124865 (file pbmc3k_sce.rds); formatted with data-raw/tiny_data.R.
Plot differential linkages among domino results ranked by a comparative statistic
plot_differential_linkages( differential_linkages, test_statistic, stat_range = c(0, 1), stat_ranking = c("ascending", "descending"), group_palette = NULL, gradient = c(minimum = "red", maximum = "gray90") )plot_differential_linkages( differential_linkages, test_statistic, stat_range = c(0, 1), stat_ranking = c("ascending", "descending"), group_palette = NULL, gradient = c(minimum = "red", maximum = "gray90") )
differential_linkages |
a data frame output from the |
test_statistic |
column name of differential_linkages where the test statistic used for ranking linkages is stored (p.value, p.adj, odds.ratio) |
stat_range |
a two value vector of the minimum and maximum values of test_statistic for plotting linkage features |
stat_ranking |
whether to rank the features by ascending or descending values of test_statistic |
group_palette |
a named vector of colors to use for each group being compared |
gradient |
a named two value vector of colors to use for the minimum and maximum values of test_statistic |
A heatmap-class object of features ranked by test_statistic annotated with the proportion of subjects that showed active linkage of the features.
Other plotting:
circos_ligand_receptor(),
cor_scatter()
Differential Signaling Workflow:
count_linkage(),
summarize_linkages(),
test_differential_linkages()
data(LinkageSummary) plot_differential_linkages( differential_linkages = LinkageSummary$linkage_diff_tiny, test_statistic = "p.value", stat_range = c(0, 1), stat_ranking = "ascending" )data(LinkageSummary) plot_differential_linkages( differential_linkages = LinkageSummary$linkage_diff_tiny, test_statistic = "p.value", stat_range = c(0, 1), stat_ranking = "ascending" )
Prints a summary of a domino() object
## S4 method for signature 'domino' print(x, ...)## S4 method for signature 'domino' print(x, ...)
x |
A |
... |
Additional arguments to be passed to other methods |
A printed description of the number of cells and clusters in the domino() object
data(DominoObjects) print(DominoObjects$built_dom_tiny)data(DominoObjects) print(DominoObjects$built_dom_tiny)
Prints a description of a linkage summary object
## S4 method for signature 'linkage_summary' print(x, ...)## S4 method for signature 'linkage_summary' print(x, ...)
x |
A linkage summary object |
... |
Additional arguments to be passed to other methods |
A printed description of the number of subjects, groups, and clusters in the linkage summary object
data(LinkageSummary) print(LinkageSummary$linkage_sum_tiny)data(LinkageSummary) print(LinkageSummary$linkage_sum_tiny)
domino() objectThis function renames the clusters used to build a domino() object
rename_clusters(dom, clust_conv, warning = FALSE)rename_clusters(dom, clust_conv, warning = FALSE)
dom |
a |
clust_conv |
named vector of conversions from old to new clusters. Values are taken as new clusters IDs and names as old cluster IDs. |
warning |
logical. If TRUE, will warn if a cluster is not found in the conversion table. Default is FALSE. |
A domino() object with clusters renamed in all applicable slots.
data(DominoObjects) dom <- DominoObjects$built_dom_tiny new_clust <- c( "CD8_T_cell" = "CD8+ T Cells", "CD14_monocyte" = "CD14+ Monocytes", "B_cell" = "B Cells" ) pbmc_dom_built_tiny <- rename_clusters(dom, new_clust)data(DominoObjects) dom <- DominoObjects$built_dom_tiny new_clust <- c( "CD8_T_cell" = "CD8+ T Cells", "CD14_monocyte" = "CD14+ Monocytes", "B_cell" = "B Cells" ) pbmc_dom_built_tiny <- rename_clusters(dom, new_clust)
A subset of pySCENIC output for the PBMC example data.
data("SCENIC")data("SCENIC")
A list of:
Matrix of AUC scores from pySCENIC for a subset of TFs and cells.
Data frame subset of the pySCENIC regulon table.
The pySCENIC regulons formatted as a named list by create_regulon_list_scenic().
https://doi.org/10.5281/zenodo.10124865 (files auc_pbmc_3k.csv, regulons_pbmc_3k.csv); formatted with data-raw/tiny_data.R
Shows content overview of a domino() object
## S4 method for signature 'domino' show(object)## S4 method for signature 'domino' show(object)
object |
A |
A printed description of cell numbers and clusters in the object
data(DominoObjects) show(DominoObjects$built_dom_tiny)data(DominoObjects) show(DominoObjects$built_dom_tiny)
Shows content overview of linkage_summary object
## S4 method for signature 'linkage_summary' show(object)## S4 method for signature 'linkage_summary' show(object)
object |
A linkage_summary object |
A printed description of the number of subjects, groups, and clusters in the linkage summary object
data(LinkageSummary) LinkageSummary$linkage_sum_tiny show(LinkageSummary$linkage_sum_tiny)data(LinkageSummary) LinkageSummary$linkage_sum_tiny show(LinkageSummary$linkage_sum_tiny)
Creates a heatmap of the signaling network. Alternatively, the network
matrix can be accessed directly in the signaling slot of a domino() object using
the dom_signaling() function.
signaling_heatmap() and signalling_heatmap() are synonyms.
signaling_heatmap( dom, clusts = NULL, min_thresh = -Inf, max_thresh = Inf, scale = "none", normalize = "none", ... ) signalling_heatmap( dom, clusts = NULL, min_thresh = -Inf, max_thresh = Inf, scale = "none", normalize = "none", ... )signaling_heatmap( dom, clusts = NULL, min_thresh = -Inf, max_thresh = Inf, scale = "none", normalize = "none", ... ) signalling_heatmap( dom, clusts = NULL, min_thresh = -Inf, max_thresh = Inf, scale = "none", normalize = "none", ... )
dom |
A |
clusts |
vector of clusters to be included. If NULL then all clusters are used. |
min_thresh |
minimum signaling threshold for plotting. Defaults to -Inf for no threshold. |
max_thresh |
maximum signaling threshold for plotting. Defaults to Inf for no threshold. |
scale |
how to scale the values (after thresholding). Options are 'none', 'sqrt' for square root, or 'log' for log10(x+1). |
normalize |
options to normalize the matrix. Normalization is done after thresholding and scaling. Accepted inputs are 'none' for no normalization, 'rec_norm' to normalize to the maximum value with each receptor cluster, or 'lig_norm' to normalize to the maximum value within each ligand cluster |
... |
other parameters to pass to |
A heatmap rendered to the active graphics device
Heatmap Plotting Functions:
cor_heatmap(),
feat_heatmap(),
incoming_signaling_heatmap()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny # basic usage signaling_heatmap(dom) # scale signaling_heatmap(dom, scale = "sqrt") # normalize signaling_heatmap(dom, normalize = "lig_norm")data(DominoObjects) dom <- DominoObjects$built_dom_tiny # basic usage signaling_heatmap(dom) # scale signaling_heatmap(dom, scale = "sqrt") # normalize signaling_heatmap(dom, normalize = "lig_norm")
Creates a network diagram of signaling between clusters. Nodes are clusters
and directed edges indicate signaling from one cluster to another. Edges are
colored based on the color scheme of the ligand expressing cluster
signaling_network() and signalling_network() are synonyms.
signaling_network( dom, cols = NULL, edge_weight = 0.3, clusts = NULL, showOutgoingSignalingClusts = NULL, showIncomingSignalingClusts = NULL, min_thresh = -Inf, max_thresh = Inf, normalize = "none", scale = "sq", layout = "circle", scale_by = "rec_sig", vert_scale = 3, plot_title = NULL, ... ) signalling_network( dom, cols = NULL, edge_weight = 0.3, clusts = NULL, showOutgoingSignalingClusts = NULL, showIncomingSignalingClusts = NULL, min_thresh = -Inf, max_thresh = Inf, normalize = "none", scale = "sq", layout = "circle", scale_by = "rec_sig", vert_scale = 3, plot_title = NULL, ... )signaling_network( dom, cols = NULL, edge_weight = 0.3, clusts = NULL, showOutgoingSignalingClusts = NULL, showIncomingSignalingClusts = NULL, min_thresh = -Inf, max_thresh = Inf, normalize = "none", scale = "sq", layout = "circle", scale_by = "rec_sig", vert_scale = 3, plot_title = NULL, ... ) signalling_network( dom, cols = NULL, edge_weight = 0.3, clusts = NULL, showOutgoingSignalingClusts = NULL, showIncomingSignalingClusts = NULL, min_thresh = -Inf, max_thresh = Inf, normalize = "none", scale = "sq", layout = "circle", scale_by = "rec_sig", vert_scale = 3, plot_title = NULL, ... )
dom |
A |
cols |
named vector indicating the colors for clusters. Values are colors and names must match clusters in
the |
edge_weight |
weight for determining thickness of edges on plot. Signaling values are multiplied by this value |
clusts |
vector of clusters to be included in the network plot |
showOutgoingSignalingClusts |
vector of clusters to plot the outgoing signaling from |
showIncomingSignalingClusts |
vector of clusters to plot the incoming signaling on |
min_thresh |
minimum signaling threshold. Values lower than the threshold will be set to the threshold. Defaults to -Inf for no threshold |
max_thresh |
maximum signaling threshold for plotting. Values higher than the threshold will be set to the threshold. Defaults to Inf for no threshold |
normalize |
options to normalize the signaling matrix. Accepted inputs are 'none' for no normalization, 'rec_norm' to normalize to the maximum value with each receptor cluster, or 'lig_norm' to normalize to the maximum value within each ligand cluster |
scale |
how to scale the values (after thresholding). Options are 'none', 'sqrt' for square root, 'log' for log10(x+1), or 'sq' for square |
layout |
type of layout to use. Options are 'random', 'sphere', 'circle', 'fr' for Fruchterman-Reingold force directed layout, and 'kk' for Kamada Kawai for directed layout |
scale_by |
how to size vertices. Options are 'lig_sig' for summed outgoing signaling, 'rec_sig' for summed incoming signaling, and 'none'. In the former two cases the values are scaled with asinh after summing all incoming or outgoing signaling. Vertices with no incoming/outgoing signaling due to other parameters are given a size of 0. |
vert_scale |
integer used to scale size of vertices with or without variable scaling from scale_by parameter. |
plot_title |
text for the plot's title. |
... |
other parameters to be passed to plot when used with an igraph object. |
An igraph plot rendered to the active graphics device
Network Plotting Functions:
gene_network()
data(DominoObjects) dom <- DominoObjects$built_dom_tiny # basic usage signaling_network(dom, edge_weight = 2) # scaling, thresholds, layouts, selecting clusters signaling_network( dom, showOutgoingSignalingClusts = "CD14_monocyte", scale = "none", normalize = "none", layout = "fr", scale_by = "none", vert_scale = 5, edge_weight = 2 )data(DominoObjects) dom <- DominoObjects$built_dom_tiny # basic usage signaling_network(dom, edge_weight = 2) # scaling, thresholds, layouts, selecting clusters signaling_network( dom, showOutgoingSignalingClusts = "CD14_monocyte", scale = "none", normalize = "none", layout = "fr", scale_by = "none", vert_scale = 5, edge_weight = 2 )
Subsets a linkage summary object by subject names or metadata
## S4 method for signature 'linkage_summary' subset(x, subset)## S4 method for signature 'linkage_summary' subset(x, subset)
x |
A linkage_summary object |
subset |
A logical expression referencing |
A linkage_summary object containing only the subjects that match the specified criteria.
data(LinkageSummary) links <- LinkageSummary$linkage_sum_tiny subset(links, subset = !subject_names %in% c("P1", "P2")) subset(links, subset = group == "G1") subset(links, subset = subject_names %in% c("P1", "P2", "P4") | group == "G2")data(LinkageSummary) links <- LinkageSummary$linkage_sum_tiny subset(links, subset = !subject_names %in% c("P1", "P2")) subset(links, subset = group == "G1") subset(links, subset = subject_names %in% c("P1", "P2", "P4") | group == "G2")
Creates a linkage_summary() object storing the linkages learned in different domino() objects as nested
lists to facilitate comparisons of networks learned by domino() across subject covariates.
summarize_linkages(domino_results, subject_meta, subject_names = NULL) summarise_linkages(domino_results, subject_meta, subject_names = NULL)summarize_linkages(domino_results, subject_meta, subject_names = NULL) summarise_linkages(domino_results, subject_meta, subject_names = NULL)
domino_results |
list of analysis results with one |
subject_meta |
data frame that includes the subject features by which the objects could be grouped.
The first column must be subject names. Rows are reordered to match order of |
subject_names |
vector of subject names in domino_results. If NULL, defaults to first column of subject_meta. |
A linkage_summary() object consisting of nested lists of the active transcription factors,
active receptors, and incoming ligands for each cluster across multiple domino results
Differential Signaling Workflow:
count_linkage(),
plot_differential_linkages(),
test_differential_linkages()
data(PBMC) data(SCENIC) data(CellPhoneDB) data(DominoObjects) # create alternative clustering by shuffling cluster assignments clusters_tiny_alt <- setNames( PBMC$clusters_tiny[c(121:240, 1:120, 241:360)], names(PBMC$clusters_tiny) ) clusters_tiny_alt <- as.factor(clusters_tiny_alt) # build an alternative domino object pbmc_dom_tiny_alt <- create_domino( rl_map = CellPhoneDB$rl_map_tiny, features = SCENIC$auc_tiny, counts = PBMC$count_tiny, z_scores = PBMC$zscore_tiny, clusters = clusters_tiny_alt, tf_targets = SCENIC$regulon_list_tiny, use_complexes = TRUE, remove_rec_dropout = FALSE, verbose = FALSE ) pbmc_dom_built_tiny_alt <- build_domino( dom = pbmc_dom_tiny_alt, max_tf_pval = .05, max_tf_per_clust = Inf, max_rec_per_tf = Inf, rec_tf_cor_threshold = .1, min_rec_percentage = 0.01 ) # create a list of domino objects dom_ls <- list( dom1 = DominoObjects$built_dom_tiny, dom2 = pbmc_dom_built_tiny_alt ) # compare the linkages across the two domino objects meta_df <- data.frame("ID" = c("dom1", "dom2"), "group" = c("A", "B")) summarize_linkages( domino_results = dom_ls, subject_meta = meta_df, subject_names = meta_df$ID )data(PBMC) data(SCENIC) data(CellPhoneDB) data(DominoObjects) # create alternative clustering by shuffling cluster assignments clusters_tiny_alt <- setNames( PBMC$clusters_tiny[c(121:240, 1:120, 241:360)], names(PBMC$clusters_tiny) ) clusters_tiny_alt <- as.factor(clusters_tiny_alt) # build an alternative domino object pbmc_dom_tiny_alt <- create_domino( rl_map = CellPhoneDB$rl_map_tiny, features = SCENIC$auc_tiny, counts = PBMC$count_tiny, z_scores = PBMC$zscore_tiny, clusters = clusters_tiny_alt, tf_targets = SCENIC$regulon_list_tiny, use_complexes = TRUE, remove_rec_dropout = FALSE, verbose = FALSE ) pbmc_dom_built_tiny_alt <- build_domino( dom = pbmc_dom_tiny_alt, max_tf_pval = .05, max_tf_per_clust = Inf, max_rec_per_tf = Inf, rec_tf_cor_threshold = .1, min_rec_percentage = 0.01 ) # create a list of domino objects dom_ls <- list( dom1 = DominoObjects$built_dom_tiny, dom2 = pbmc_dom_built_tiny_alt ) # compare the linkages across the two domino objects meta_df <- data.frame("ID" = c("dom1", "dom2"), "group" = c("A", "B")) summarize_linkages( domino_results = dom_ls, subject_meta = meta_df, subject_names = meta_df$ID )
Statistical test for differential linkages across multiple domino results
test_differential_linkages( link_summary, cluster, group.by, linkage = "rec_lig", subject_names = NULL )test_differential_linkages( link_summary, cluster, group.by, linkage = "rec_lig", subject_names = NULL )
link_summary |
a |
cluster |
the name of the cell cluster being compared across multiple |
group.by |
the name of the column in |
linkage |
a stored linkage from the |
subject_names |
a vector of subject_names from the linkage_summary to restrict testing to.
If NULL, all subjects in |
A data frame of results from the test of the differential linkages. Rows correspond to each linkage tested. Columns correspond to:
'cluster' : the name of the cell cluster being compared
'linkage' : the type of linkage being compared
'group.by' : the grouping variable
'test_name' : the test used for comparison (Fisher's exact test)
'feature' : individual linkages compared
'test statistics' : an odds ratio (for 2 groups), p-value, and fdr-adjusted p-value.
'total_count' : total number of subjects where the linkage is active
'X_count' : number of subjects in each category of group.by (X) where the linkage is active
'total_n' : number of total subjects compared
'X_n' : total number of subjects in each category of group.by (X)
summarize_linkages() to create linkage_summary() objects
Differential Signaling Workflow:
count_linkage(),
plot_differential_linkages(),
summarize_linkages()
data(LinkageSummary) tiny_differential_linkage_c1 <- test_differential_linkages( link_summary = LinkageSummary$linkage_sum_tiny, cluster = "C1", group.by = "group", linkage = "rec", subject_names = c("P1", "P2", "P4", "P5") )data(LinkageSummary) tiny_differential_linkage_c1 <- test_differential_linkages( link_summary = LinkageSummary$linkage_sum_tiny, cluster = "C1", group.by = "group", linkage = "rec", subject_names = c("P1", "P2", "P4", "P5") )