Package 'famat' reference manual

Title:	Functional analysis of metabolic and transcriptomic data
Description:	Famat is made to collect data about lists of genes and metabolites provided by user, and to visualize it through a Shiny app. Information collected is: - Pathways containing some of the user's genes and metabolites (obtained using a pathway enrichment analysis). - Direct interactions between user's elements inside pathways. - Information about elements (their identifiers and descriptions). - Go terms enrichment analysis performed on user's genes. The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. - an heatmap showing which elements from the list are in pathways (pathways are structured in hierarchies). - hierarchies of enriched go terms using Molecular Function and Biological Process.
Authors:	Mathieu Charles [aut, cre]
Maintainer:	Mathieu Charles <[email protected]>
License:	GPL-3
Version:	1.15.0
Built:	2024-07-13 05:08:13 UTC
Source:	https://github.com/bioc/famat

Data preparation for Shiny interface

Description

Complete and prepare data obtained with interactions function, to use it in the Shiny interface. GO terms enrichment analysis is performed using clusterProfiler.

Usage

    compl_data(listparam)
compl_data(listparam)

Arguments

listparam

Output from interactions function

Value

A list containing :

`heatmap`	Dataframe heatmap-like, with in abscissa elements of pathways ("X" is written if an element is present in a pathway), and with in ordinate hierarchies of pathways
`meta_list`	User's metabolites given in `path_enrich` function
`allResBP`	Results of Go BP terms enrichment analysis performed by clusterProfileR (20 best)
`go_genelist`	Dataframe containing enriched GO terms per genes of user's list
`allResMF`	Results of Go MF terms enrichment analysis performed by clusterProfileR (20 best)
`types`	Dataframe containing categories of pathways per pathways
`genetype`	List of genes categories, containing genes of user's list
`metatab`	Dataframe of metabolites and informations about them, as names and chebi ids
`genetab`	Dataframe of genes and informations about them, as gene symbols and names
`intetab`	Dataframe of direct interactions between elements of user's list, and informations about them as elements in the interaction, how they interact, and which pathways are concerned
`gomf_tab`	Dataframe of Go MF terms hierarchies containing our enriched GO terms, plus description of these GO terms and genes of user's list concerned by enriched GO terms
`gobp_tab`	Dataframe of Go BP terms hierarchies containing our enriched GO terms, plus description of these GO terms and genes of user's list concerned by enriched GO terms
`gene_list`	User's genes given in `path_enrich` function
`gomflist`	List containing GO MF terms hierarchies, with indices in the joliMF dataframe and genes concerned by the hierarchie
`gobplist`	List containing GO BP terms hierarchies, with indices in the joliMF dataframe and genes concerned by the hierarchy
`hierabrite`	List of pathways categories, containing pathways concerned by a category and their indices in "trait" dataframe
`hierapath`	List of hierarchies of pathways, containing indices of pathways in "trait" dataframe and elements contained in the hierarchy.
`save_cluster_elem`	Vector of clustered elements
`centrality`	Matrix trait-like with values of centralities (number of direct interactions between an element of user's list and other elements of the pathway) instead of "X"
`inter_values`	Matrix trait-like with values representing direct interactions (3/2/1, respectively for genes/metabolites implicated in a direct interaction, and for elements not implicated in a direct interaction) instead of "X"
`gene_notin`	Dataframe of genes which aren't in pathways and informations about them, as gene symbols and names
`sub`	Matrix trait-like with pathway and element informations instead of "X"

This list is used by rshiny function.

Author(s)

Emilie Secherre [email protected]

References

Guangchuang Yu, Li-Gen Wang, Yanyan Han and Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology 2012, 16(5):284-287

Examples

    ## load example data
    data(interactions_result)

    compl_data_result=compl_data(interactions_result)
## load example data
    data(interactions_result)

    compl_data_result=compl_data(interactions_result)

Output of `compl_data` function

Description

Enriched and structured informations about genes, metabolites, their interactions, pathways and enriched GO terms.

Usage

data("compl_data_result")data("compl_data_result")

Format

List of 22 items.

heatmap

Dataframe heatmap-like, with in abscissa elements of pathways ("X" is written if an element is present in a pathway), and with in ordinate hierarchies of pathways. A data frame with 16 rows and 9 variables.

path_name: Hierarchies of pathways obtained by pathways enrichment analysis. Pathways are given by their name.
path_id: Identifiers of pathways in hierarchies.
meta_ratio: Metabolites ratio, so the part of user's metabolites in the total number of metabolites in the pathway.
gene_ratio: Genes ratio, so the part of user's genes in the total number of genes in the pathway.
blank: Just an empty column to separate heatmap data from pathways data (NA).
SLC6A12: The value "1" shows that the element SLC6A12 is in the pathway on the row, "0" shows it is not in this pathway (0–1).
Betaine: The value "1" shows that the element Betaine is in the pathway on the row, "0" shows it is not in this pathway (0–1).
ATP: The value "1" shows that the element ATP is in the pathway on the row, "0" shows it is not in this pathway (0–1).
Betaine / SLC6A12: The value "1" shows that the interaction Betaine / SLC6A12 is in the pathway on the row, "0" shows it is not in this pathway (0–1).

meta_list

Vector containing user's metabolites (C00002, C00719)

allResBP

Results of Go BP terms enrichment analysis performed by clusterProfileR (20 best). A data frame with 20 rows and 9 variables.

ID: Identifiers of enriched GO terms.
Description: Names of enriched GO terms.
GeneRatio: Number of user's genes concerned by the enriched GO term, by total number of user's genes.
BgRatio: Number of genes concerned by the enriched GO term, by the total number of annotated genes in the database.
pvalue: Pvalue of the go term enrichment analysis (0.001392161–0.011536208).
p.adjust: Adjusted pvalue of the go term enrichment analysis (0.01713291–0.02670275).
qvalue: Qvalue of the go term enrichment analysis (0.001803464–0.002810816).
geneID: Entrez Gene identifiers of all genes concerned by the enriched GO term (30, 6539).
Count: Number of genes concerned by the enriched GO term described by an Entrez Gene identifier(1).

go_genelist

Dataframe containing enriched GO terms per genes of user's list. A data frame with 13 rows and 2 variables.

hgnc_symbol: Gene symbol of the gene concerned by an enriched GO term (SLC6A12, ACAA1).
go_id: Identifier of the GO term concerning the gene (GO:0005328, GO:0015293, GO:0003333, GO:0015812, GO:0015171, GO:0042165, GO:0008028, GO:0006635, GO:0006625, GO:0033540, GO:0036109, GO:0008206, GO:0000038)

allResMF

Results of Go MF terms enrichment analysis performed by clusterProfileR (20 best) A data frame with 20 rows and 9 variables.

ID: Identifiers of enriched GO terms.
Description: Names of enriched GO terms.
GeneRatio: Number of user's genes concerned by the enriched GO term, by total number of user's genes.
BgRatio: Number of genes concerned by the enriched GO term, by the total number of annotated genes in the database.
pvalue: Pvalue of the go term enrichment analysis (0.001392161–0.011536208).
p.adjust: Adjusted pvalue of the go term enrichment analysis (0.01713291–0.02670275).
qvalue: Qvalue of the go term enrichment analysis (0.001803464–0.002810816).
geneID: Entrez Gene identifiers of all genes concerned by the enriched GO term (30, 6539).
Count: Number of genes concerned by the enriched GO term described by an Entrez Gene identifier(1).

types

Dataframe containing categories of pathways per pathways. A data frame with 12 rows and 2 variables.

id: Identifier of pathways from pathways enrichment analysis.
root: Name of the pathway category concerning a pathway.

genetype

List of genes categories, containing genes of user's list. A list of 3 items.

metatab

Dataframe of metabolites and informations about them, as names and chebi ids. A data frame with 2 rows and 2 variables.

id: Name of user's metabolites (ATP, Betaine).
name: Chebi identifier of user's metabolites (CHEBI:15422, CHEBI:17750).

genetab

Dataframe of genes and informations about them, as gene symbols and names. A data frame with 1 rows and 2 variables.

id: Gene symbols of user's genes contained in pathways (SLC6A12).
name: Name of user's genes contained in pathways (solute carrier family 6 member 12).

intetab

Dataframe of direct interactions between elements of user's list, and informations about them as elements in the interaction, how they interact, and which pathways are concerned A data frame with 1 row and 8 variables.

tag: Summary of elements concerned by the interaction (Betaine / SLC6A12)
first_item: First element of the direct interaction (Betaine)
link: Description of how the two elements interact (Control(In: ACTIVATION of BiochemicalReaction), controls-transport-of-chemical).
sec_item: Second element of the direct interaction (SLC6A12)
go: Value "1" means that a gene of the interaction is concerned by an enriched GO term, "0" means no element is concerned by an enriched GO term (1).
path: Pathways containing the direct interaction ("R-HSA-112310, R-HSA-112315, R-HSA-112316, R-HSA-382551, R-HSA-425366, R-HSA-425393, R-HSA-425407, R-HSA-888590, R-HSA-352230, R-HSA-442660, R-HSA-888593")
type: Interaction type, can be gene/gene, metabolite/metabolite, or gene/metabolite (g/m)
cat: Categories of pathways containing the direct interaction (Neuronal System, Transport of small molecules)

gomf_tab

Dataframe of Go MF terms hierarchies containing our enriched GO terms, plus description of these GO terms and genes of user's list concerned by enriched GO terms. A data frame with 93 rows and 3 variables.

goterm: Hierarchies of enriched GO terms.
go_name: Names of GO terms.
genes: Genes concerned by GO terms.

gobp_tab

Dataframe of Go BP terms hierarchies containing our enriched GO terms, plus description of these GO terms and genes of user's list concerned by enriched GO terms. A data frame with 107 rows and 3 variables.

goterm: Hierarchies of enriched GO terms.
go_name: Names of GO terms.
genes: Genes concerned by GO terms.

gene_list

Vector containing user's genes (ACAA1, SLC6A12)

gomflist

List containing GO MF terms hierarchies, with indices in the joliMF dataframe and genes concerned by the hierarchie. A list of 3 items.

gobplist

List containing GO BP terms hierarchies, with indices in the joliMF dataframe and genes concerned by the hierarchy. A list of 5 items.

hierabrite

List of pathways categories, containing pathways concerned by a category and their indices in "trait" dataframe. A list of 3 items.

hierapath

List of hierarchies of pathways, containing indices of pathways in "trait" dataframe and elements contained in the hierarchy. A list of 3 items.

save_cluster_elem

Vector of clustered elements

centrality

Matrix heatmap-like with values of centralities (number of direct interactions between an element of user's list and other elements of the pathway) instead of "X". Other cells contain the value "0" (0–65). An integer matrix with 16 rows and 9 columns.

inter_values

Matrix heatmap-like with values representing direct interactions (3/2/1, respectively for genes/metabolites implicated in a direct interaction, and for elements not implicated in a direct interaction) instead of "X". Other cells contain the value "0" (0–3). An integer matrix with 16 rows and 9 columns.

gene_notin

Dataframe of genes which aren't in pathways and informations about them, as gene symbols and names. A data frame with 1 row and 2 variables.

id: Gene symbols of genes (ACAA1).
name: Names of genes (acetyl-CoA acyltransferase 1).

sub

Matrix heatmap-like with pathway and element informations instead of "X". Cells with no informations contain only "". A character matrix with 16 rows and 9 columns.

Source

compl_data function

List of genes.

Description

Example of a list of genes that can be provided by an user.

Usage

data("genes")data("genes")

Format

A vector with 2 observations (ACAA1, SLC6A12).

Source

Sample of data from a study on chickens, under heat-stress condition.

Interactions between genes and metabolites

Description

Gather informations about direct interactions between genes and metabolites inside a pathway, and about pathways themselves. These informations are direct interactions between these two elements and number of relations between an element from the list provided by the user and other elements of the pathway (centrality). Direct interactions extraction was performed using BioPax, KGML and GPML files parsed with PaxtoolsR, graphite and author's parsers.

Usage

    interactions(listk, listr, listw)
interactions(listk, listr, listw)

Arguments

`listk`	Output from `path_enrich` function, with "KEGG" argument.
`listr`	Output from `path_enrich` function, with "REAC" argument.
`listw`	Output from `path_enrich` function, with "WP" argument.

Value

A list containing :

`size`	Dataframe containing pathways, genes and metabolites in pathways (from the list or not), and number of elements in pathways
`pathtot`	Dataframe containing pathways names and ids from pathway enrichment analysis on Reactome, Kegg and Wikipathways pathways
`tagged`	Dataframe containing direct interactions between elements from the user's list per pathways
`keggchebiname`	Dataframe containing all human metabolites ids (kegg and chebi) and names
`central`	List of pathways, each pathway containing the number of direct interactions between an element of user's list and other elements in the pathway
`no_path`	Dataframe containing direct interactions between elements from the user's list, but not per pathways
`genes`	User's genes given in `path_enrich` function
`meta`	User's metabolites given in `path_enrich` function

This list is used by compl_data function.

Author(s)

Emilie Secherre [email protected]

References

Luna, A., Babur, O., Aksoy, A. B, Demir, E., Sander, C. (2015).“PaxtoolsR: Pathway Analysis in R Using Pathway Commons.” Bioinformatics.

Sales G, Calura E, Cavalieri D, Romualdi C (2012). “graphite - a Bioconductor package to convert pathway topology to gene network.” BMC Bioinformatics. https://bmcbioinformatics.biomedcentral.com/articles/10 . 1186/1471-2105-13-20.

Examples

    ## load example data
    data(listk)
    data(listr)
    data(listw)

    interactions_result=interactions(listk,listr,listw)
## load example data
    data(listk)
    data(listr)
    data(listw)

    interactions_result=interactions(listk,listr,listw)

Output of `interactions` function

Description

List containing informations about interactions between genes and metabolites, centrality and pathways. Direct interactions extraction was performed using BioPax, KGML and GPML files parsed with PaxtoolsR, graphite and author's parsers.

Usage

data("interactions_result")data("interactions_result")

Format

List of 8 items.

size

Description on which elements (from user's list or not) are contained in pathways from pathway enrichment analysis. A data frame with 286 rows and 9 variables.

path: Pathways obtained throught pathways enrichment analysis on KEgg, Reactome and Wikipathways pathways.
nb_gene_query: Number of user's genes contained in the pathway (0–2).
gene_que: User's genes contained in the pathway (ACAA1, SLC6A12, ACAA1 # SLC6A12).
nb_gene_tot: Total number of genes contained in the pathway (0–2075).
genes: All the genes contained in the pathway.
nb_meta_query: Number of user's metabolites contained in the pathway (0–2).
meta_que: User's metabolites contained in the pathway (Betaine, ATP, Betaine # ATP, ATP # Betaine).
nb_meta_tot: Total number of metabolites contained in the pathway (0–915).
meta: All the metabolites contained in the pathway.

pathtot

All results of pathways enrichment analysis performed on Kegg, Reactome and Wikipathways pathways. A data frame with 286 rows and 2 variables.

name: Name of pathways resulting in genes pathway enrichment analysis performed on Kegg, Reactome and Wikipathways.
id: Identifiers of pathways resulting in genes pathway enrichment analysis performed on Kegg, Reactome and Wikipathways.

tagged

Description of all direct interactions between user's elements in pathways. A data frame with 11 rows and 6 variables.

from: First element of the direct interaction (Betaine, SLC6A12)
link: Description of how the two elements interact (Control(In: ACTIVATION of BiochemicalReaction), controls-transport-of-chemical).
to: Second element of the direct interaction (Betaine, SLC6A12)
path: Pathway containing the direct interaction (R-HSA-112310, R-HSA-112315, R-HSA-112316, R-HSA-382551, R-HSA-425366, R-HSA-425393, R-HSA-425407, R-HSA-888590, R-HSA-352230, R-HSA-442660, R-HSA-888593)
tag: Summary of elements concerned by the interaction (Betaine / SLC6A12, SLC6A12 / Betaine)
type: Interaction type, can be gene/gene, metabolite/metabolite, or gene/metabolite (g/m)

keggchebiname

Dataframe containing all human metabolites ids (kegg and chebi) and names. A data frame with 16075 rows and 3 variables.

kegg: Kegg_compound identifiers of all human metabolites.
chebi: Chebi identifiers of all human metabolites.
name: Names of all human metabolites.

central

List of pathways, each pathway containing the number of direct interactions between an element of user's list and other elements in the pathway. A list of 138 items.

no_path

Dataframe containing direct interactions between elements from the user's list, but not per pathways. A data frame with 1 rows and 6 variables.

from: First element of the direct interaction (Betaine, SLC6A12)
link: Description of how the two elements interact (Control(In: ACTIVATION of BiochemicalReaction), controls-transport-of-chemical).
to: Second element of the direct interaction (Betaine, SLC6A12)
path: Pathways containing the direct interaction ("R-HSA-112310, R-HSA-112315, R-HSA-112316, R-HSA-382551, R-HSA-425366, R-HSA-425393, R-HSA-425407, R-HSA-888590, R-HSA-352230, R-HSA-442660, R-HSA-888593")
tag: Summary of elements concerned by the interaction (Betaine / SLC6A12)
type: Interaction type, can be gene/gene, metabolite/metabolite, or gene/metabolite (g/m)

genes

Vector containing user's genes (ACAA1, SLC6A12)

meta

Vector containing user's metabolites (C00002, C00719)

Source

interactions function.

References

Luna, A., Babur, O., Aksoy, A. B, Demir, E., Sander, C. (2015).“PaxtoolsR: Pathway Analysis in R Using Pathway Commons.” Bioinformatics.

Pathway enrichment analysis results for KEGG pathways.

Description

Results of pathways enrichment analysis on the list of genes and metabolites, using KEGG pathways. Pathways enrichment analysis is performed using MPINet for metabolites and gprofiler2 for genes.

Usage

data("listk")data("listk")

Format

A list of 4 items.

resmeta

Pathway enrichment analysis results for metabolites. A data frame with 7 rows and 2 variables.

name: Name of pathways resulting in metabolites pathway enrichment analysis.
id: Identifiers of pathways resulting in metabolites pathway enrichment analysis.

resgene

Pathway enrichment analysis results for genes. A data frame with 11 rows and 2 variables.

name: Name of pathways resulting in genes pathway enrichment analysis.
id: Identifiers of pathways resulting in genes pathway enrichment analysis.

gened

Vector containing user's genes (ACAA1, SLC6A12)

metad

Vector containing user's metabolites (C00002, C00719)

Source

path_enrich function.

References

Yanjun Xu, Chunquan Li and Xia Li (2013). MPINet: The package can implement the network-based metabolite pathway identification of pathways.. R package version 1.0. https://CRAN.R-project.org/package=MPINet

Liis Kolberg and Uku Raudvere (2020). gprofiler2: Interface to the 'g:Profiler' Toolset. R package version 0.2.0. https://CRAN.R-project.org/package=gprofiler2

Pathway enrichment analysis results for Reactome pathways.

Description

Results of pathways enrichment analysis on the list of genes and metabolites, using Reactome pathways. Pathways enrichment analysis is performed using MPINet for metabolites and gprofiler2 for genes.

Usage

data("listr")data("listr")

Format

A list of 4 items.

resmeta

Pathway enrichment analysis results for metabolites. A data frame with 278 rows and 2 variables.

name: Name of pathways resulting in metabolites pathway enrichment analysis.
id: Identifiers of pathways resulting in metabolites pathway enrichment analysis.

resgene

Pathway enrichment analysis results for genes. A data frame with 27 rows and 2 variables.

name: Name of pathways resulting in genes pathway enrichment analysis.
id: Identifiers of pathways resulting in genes pathway enrichment analysis.

gened

Vector containing user's genes (ACAA1, SLC6A12)

metad

Vector containing user's metabolites (C00002, C00719)

Source

path_enrich function.

References

Liis Kolberg and Uku Raudvere (2020). gprofiler2: Interface to the 'g:Profiler' Toolset. R package version 0.2.0. https://CRAN.R-project.org/package=gprofiler2

Pathway enrichment analysis results for Wikipathways pathways.

Description

Results of pathways enrichment analysis on the list of genes and metabolites, using Wikipathways pathways. Pathways enrichment analysis is performed using MPINet for metabolites and gprofiler2 for genes.

Usage

data("listw")data("listw")

Format

A list of 4 items.

resmeta

Pathway enrichment analysis results for metabolites. A data frame with 48 rows and 2 variables.

name: Name of pathways resulting in metabolites pathway enrichment analysis.
id: Identifiers of pathways resulting in metabolites pathway enrichment analysis.

resgene

Pathway enrichment analysis results for genes. A data frame with 8 rows and 2 variables.

name: Name of pathways resulting in genes pathway enrichment analysis.
id: Identifiers of pathways resulting in genes pathway enrichment analysis.

gened

Vector containing user's genes (ACAA1, SLC6A12)

metad

Vector containing user's metabolites (C00002, C00719)

Source

path_enrich function.

References

Liis Kolberg and Uku Raudvere (2020). gprofiler2: Interface to the 'g:Profiler' Toolset. R package version 0.2.0. https://CRAN.R-project.org/package=gprofiler2

List of metabolites.

Description

Example of a list of metabolites that can be provided by an user.

Usage

data("meta")data("meta")

Format

A vector with 2 observations (C00002, C00719).

Source

Sample of data from a study on chickens, under heat-stress condition.

The variables in the environment variable `MPINetData` of the system

Description

The variables in the environment variable MPINetData of the system.

Format

An environment variable

Author(s)

Yanjun Xu <[email protected]>, Chunquan Li <[email protected]> and Xia Li <[email protected]>

Pathway enrichment analysis

Description

Perform a pathway enrichment analysis using a list of genes and a list of metabolites. Pathways enrichment analysis is performed using MPINet for metabolites and gprofiler2 for genes.

Usage

    path_enrich(source, metabo, genes)
path_enrich(source, metabo, genes)

Arguments

`source`	Pathways database used, either Kegg ("KEGG"), Reactome ("REAC") or Wikipathays ("WP")
`metabo`	Dataframe with three columns : the first column contain the list of metabolites, the second some quantitative data about the metabolites, the last one words "DOWN" or "UP" depending on the metabolites concentration behavior in a certain condition. Last two columns can contain only/some NAs. All metabolites ids are KEGG Compound ids.
`genes`	Dataframe with three columns : the first column contain the list of genes, the second some quantitative data about the genes, the last one words "DOWN" or "UP" depending on the genes expression behavior in a certain condition. Last two columns can contain only/some NAs. All genes ids are gene symbol.

Value

A list containing :

`resmeta`	Results of metabolites pathway enrichment analysis
`resgene`	Results of genes pathway enrichment analysis
`genes`	Vector containing genes
`metabo`	Vector containing metabolites

This list is used by interactions function.

Author(s)

Emilie Secherre [email protected]

References

Liis Kolberg and Uku Raudvere (2020). gprofiler2: Interface to the 'g:Profiler' Toolset. R package version 0.2.0. https://CRAN.R-project.org/package=gprofiler2

Examples

    ## load example data
    data(genes)
    data(meta)

    ## perform pathway enrichment analysis on Reactome pathways
    listr=path_enrich("REAC", meta, genes)
## load example data
    data(genes)
    data(meta)

    ## perform pathway enrichment analysis on Reactome pathways
    listr=path_enrich("REAC", meta, genes)

Shiny interface

Description

Visualize and filter all functionnal informations gathered by famat using a Shiny interface.

Usage

rshiny(listdata)
rshiny(listdata)

Arguments

listdata

Output from compl_data function

Value

Shiny interface

Author(s)

Emilie Secherre [email protected]

References

Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2020). shiny: Web Application Framework for R. R package version 1.5.0. https://CRAN.R-project.org/package=shiny

Examples

    ## load example data
    data(compl_data_result)

    ## Not run: rshiny(compl_data_result)
## load example data
    data(compl_data_result)

    ## Not run: rshiny(compl_data_result)

Package 'famat'

Help Index

Data preparation for Shiny interface

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Output of compl_data function

Description

Usage

Format

Source

List of genes.

Description

Usage

Format

Source

Interactions between genes and metabolites

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Output of interactions function

Description

Usage

Format

Source

References

Pathway enrichment analysis results for KEGG pathways.

Description

Usage

Format

Source

References

Pathway enrichment analysis results for Reactome pathways.

Description

Usage

Format

Source

References

Pathway enrichment analysis results for Wikipathways pathways.

Description

Usage

Format

Source

References

List of metabolites.

Description

Usage

Format

Source

The variables in the environment variable MPINetData of the system

Description

Format

Author(s)

Pathway enrichment analysis

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Shiny interface

Description

Usage

Arguments

Value

Author(s)

References

See Also

Output of `compl_data` function

Output of `interactions` function

The variables in the environment variable `MPINetData` of the system