R/methodsEmap.R
In adeschen/gprofiler2cytoscape: From functional enrichment results to biological networks
Defines functions
createEnrichMapMultiComplex
createEnrichMapMultiBasic
createEnrichMap
Documented in
createEnrichMap
createEnrichMapMultiBasic
createEnrichMapMultiComplex
#' @title Using functional enrichment results in gprofiler2 format to create
#' an enrichment map
#'
#' @description User selected enrichment terms are used to create an enrichment
#' map. The selection of the term can by specifying by the
#' source of the terms (GO:MF, REAC, TF, etc.) or by listing the selected
#' term IDs. The map is only generated when there is at least on
#' significant term to graph.
#'
#' @param gostObject a \code{list} corresponding to gprofiler2 enrichment
#' output that contains and that contains
#' the results from an enrichment analysis.
#'
#' @param query a \code{character} string representing the name of the query
#' that is going to be used to generate the graph. The query must exist in the
#' \code{gostObject} object.
#'
#' @param source a \code{character} string representing the selected source
#' that will be used to generate the network. To hand-pick the terms to be
#' used, "TERM_ID" should be used and the list of selected term IDs should
#' be passed through the \code{termIDs} parameter. The possible sources are
#' "GO:BP" for Gene Ontology Biological Process, "GO:CC" for Gene Ontology
#' Cellular Component, "GO:MF" for Gene Ontology Molecular Function,
#' "KEGG" for Kegg, "REAC" for Reactome, "TF" for TRANSFAC, "MIRNA" for
#' miRTarBase, "CORUM" for CORUM database, "HP" for Human phenotype ontology
#' and "WP" for WikiPathways. Default: "TERM_ID".
#'
#' @param removeRoot a \code{logical} that specified if the root terms of
#' the selected source should be removed (when present). Default: \code{TRUE}.
#'
#' @param termIDs a \code{vector} of \code{character} strings that contains the
#' term IDS retained for the creation of the network. Default: \code{NULL}.
#'
#' @param showCategory a positive \code{integer} or a \code{vector} of
#' \code{characters} representing terms. If a \code{integer}, the first
#' \code{n} terms will be displayed. If \code{vector} of terms,
#' the selected terms will be displayed. Default: \code{30L}.
#'
#' @param groupCategory a \code{logical} indicating if the categories should
#' be grouped. Default: \code{FALSE}.
#'
#' @param categoryLabel a positive \code{numeric} representing the amount by
#' which plotting category nodes label size should be scaled relative
#' to the default (1). Default: \code{1}.s
#'
#' @param categoryNode a positive \code{numeric} representing the amount by
#' which plotting category nodes should be scaled relative to the default (1).
#' Default: \code{1}.
#'
#' @param line a non-negative \code{numeric} representing the scale of line
#' width. Default: \code{1}.
#'
#' @param ... additional arguments that will be pass to the
#' \code{\link[enrichplot]{emapplot}} function.
#'
#' @return a \code{ggplot} object which is the enrichment map for enrichment
#' results.
#'
#' @examples
#'
#' ## Loading dataset containing result from an enrichment analysis done with
#' ## gprofiler2
#' data(parentalNapaVsDMSOEnrichment)
#'
#' ## Extract query information (only one in this dataset)
#' query <- unique(parentalNapaVsDMSOEnrichment$result$query)
#'
#' ## Create graph for Gene Ontology - Cellular Component related results
#' createEnrichMap(gostObject=parentalNapaVsDMSOEnrichment,
#' query=query, source="GO:CC", removeRoot=TRUE)
#'
#'
#' @author Astrid Deschênes
#' @importFrom gprofiler2 gconvert
#' @importFrom strex match_arg
#' @encoding UTF-8
#' @export
createEnrichMap <- function(gostObject, query, source=c("TERM_ID", "GO:MF",
"GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA", "CORUM",
"HP", "WP"), termIDs=NULL, removeRoot=TRUE,
showCategory=30L, groupCategory=FALSE, categoryLabel=1,
categoryNode=1, line=1, ...) {
## Validate source is among the possible choices
source <- match_arg(source, ignore_case=TRUE)
## Validate parameters
validateCreateEnrichMapArguments(gostObject=gostObject, query=query,
source=source, termIDs=termIDs, removeRoot=removeRoot,
showCategory=showCategory, categoryLabel=categoryLabel,
groupCategory=groupCategory, categoryNode=categoryNode,
line=line)
## Extract results
gostResults <- gostObject$result
## Retain results associated to query
gostResults <- gostResults[gostResults$query == query,]
## Filter results
if (source == "TERM_ID") {
gostResults <- gostResults[gostResults$term_id %in% termIDs,]
} else {
gostResults <- gostResults[gostResults$source == source,]
}
## Remove root term if required
if (removeRoot) {
gostResults <- removeRootTerm(gostResults)
if (nrow(gostResults) == 0) {
stop("With removal of the root term, there is no ",
"enrichment term left")
}
}
meta <- gostObject$meta
backgroundGenes <- meta$query_metadata$queries[[query]]
significantMethod <- meta$query_metadata$significance_threshold_method
## Create basic emap
emap <- createBasicEmap(gostResults=gostResults,
backgroundGenes=backgroundGenes,
showCategory=showCategory, categoryLabel=categoryLabel,
groupCategory=groupCategory, categoryNode=categoryNode,
significantMethod=significantMethod, line=line, ...)
return(emap)
}
#' @title Using functional enrichment results in gprofiler2 format to create
#' an enrichment map with multiple groups from different enrichment analyses
#'
#' @description User selected enrichment terms are used to create an enrichment
#' map. The selection of the term can by specifying by the
#' source of the terms (GO:MF, REAC, TF, etc.) or by listing the selected
#' term IDs. The map is only generated when there is at least on
#' significant term to graph.
#'
#' @param gostObjectList a \code{list} of \code{gprofiler2} objects that
#' contain the results from an enrichment analysis. The list must contain at
#' least 2 entries. The number of entries must correspond to the number of
#' entries for the \code{queryList} parameter.
#'
#' @param queryList a \code{list} of \code{character} strings representing the
#' names of the queries that are going to be used to generate the graph.
#' The query names must exist in the associated \code{gostObjectList} objects
#' and follow the same order. The number of entries must correspond to the
#' number of entries for the \code{gostObjectList} parameter.
#'
#' @param source a \code{character} string representing the selected source
#' that will be used to generate the network. To hand-pick the terms to be
#' used, "TERM_ID" should be used and the list of selected term IDs should
#' be passed through the \code{termIDs} parameter. The possible sources are
#' "GO:BP" for Gene Ontology Biological Process, "GO:CC" for Gene Ontology
#' Cellular Component, "GO:MF" for Gene Ontology Molecular Function,
#' "KEGG" for Kegg, "REAC" for Reactome, "TF" for TRANSFAC, "MIRNA" for
#' miRTarBase, "CORUM" for CORUM database, "HP" for Human phenotype ontology
#' and "WP" for WikiPathways. Default: "TERM_ID".
#'
#' @param removeRoot a \code{logical} that specified if the root terms of
#' the selected source should be removed (when present). Default: \code{TRUE}.
#'
#' @param termIDs a \code{vector} of \code{character} strings that contains the
#' term IDS retained for the creation of the network. Default: \code{NULL}.
#'
#' @param showCategory a positive \code{integer} or a \code{vector} of
#' \code{characters} representing terms. If a \code{integer}, the first
#' \code{n} terms will be displayed. If \code{vector} of terms,
#' the selected terms will be displayed. Default: \code{30L}.
#'
#' @param groupCategory a \code{logical} indicating if the categories should
#' be grouped. Default: \code{FALSE}.
#'
#' @param categoryLabel a positive \code{numeric} representing the amount by
#' which plotting category nodes label size should be scaled relative
#' to the default (1). Default: \code{1}.
#'
#' @param categoryNode a positive \code{numeric} representing the amount by
#' which plotting category nodes should be scaled relative to the default (1).
#' Default: \code{1}.
#'
#' @param line a non-negative \code{numeric} representing the scale of line
#' width. Default: \code{1}.
#'
#' @param ... additional arguments that will be pass to the
#' \code{\link[enrichplot]{emapplot}} function.
#'
#' @return a \code{ggplot} object which is the enrichment map for enrichment
#' results.
#'
#' @examples
#'
#' ## Loading dataset containing results from 2 enrichment analyses done with
#' ## gprofiler2
#' data(parentalNapaVsDMSOEnrichment)
#' data(rosaNapaVsDMSOEnrichment)
#'
#' ## Extract query information (only one in each dataset)
#' query1 <- unique(parentalNapaVsDMSOEnrichment$result$query)[1]
#' query2 <- unique(rosaNapaVsDMSOEnrichment$result$query)[1]
#'
#' ## Create graph for KEGG related results from
#' ## 2 enrichment analyses
#' createEnrichMapMultiBasic(gostObjectList=list(parentalNapaVsDMSOEnrichment,
#' rosaNapaVsDMSOEnrichment),
#' queryList=list(query1, query2), source="KEGG", removeRoot=TRUE)
#'
#'
#' @author Astrid Deschênes
#' @importFrom gprofiler2 gconvert
#' @importFrom strex match_arg
#' @encoding UTF-8
#' @export
createEnrichMapMultiBasic <- function(gostObjectList, queryList,
source=c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF",
"MIRNA", "HPA", "CORUM", "HP", "WP"), termIDs=NULL, removeRoot=TRUE,
showCategory=30L, groupCategory=FALSE, categoryLabel=1,
categoryNode=1, line=1, ...) {
## Validate source is among the possible choices
source <- match_arg(source, ignore_case=TRUE)
## Validate parameters
validateCreateEnrichMapMultiArguments(gostObjectList=gostObjectList,
queryList=queryList, source=source, termIDs=termIDs,
removeRoot=removeRoot, showCategory=showCategory,
categoryLabel=categoryLabel, groupCategory=groupCategory,
categoryNode=categoryNode, line=line)
## Extract results
gostResultsList <- lapply(gostObjectList, FUN=function(x) {x$result})
## Retain results associated to query
gostResultsList <- lapply(seq_len(length(queryList)),
FUN=function(i, queryL, gostL) {
gostL[[i]][gostL[[i]]$query == queryL[[i]], ]},
queryL=queryList, gostL=gostResultsList)
## Filter results
if (source == "TERM_ID") {
gostResultsList <- lapply(gostResultsList, FUN=function(x, termIDs) {
x[x$term_id %in% termIDs,]}, termIDs=termIDs)
} else {
gostResultsList <- lapply(gostResultsList, FUN=function(x, source) {
x[x$source == source,]}, source=source)
}
## Remove root term if required
if (removeRoot) {
gostResultsList <- lapply(gostResultsList, FUN=function(x) {
removeRootTerm(x)})
}
## Validate that at least one term is left
if (sum(unlist(lapply(gostResultsList, FUN=function(x) {nrow(x)})))
== 0) {
stop("With removal of the root term, there is no ",
"enrichment term left")
}
## Create multi categories emap
emap <- createMultiEmap(gostResultsList=gostResultsList,
queryList=queryList, showCategory=showCategory,
categoryLabel=categoryLabel, groupCategory=groupCategory,
categoryNode=categoryNode, line=line, ...)
return(emap)
}
#' @title Using functional enrichment results in gprofiler2 format to create
#' an enrichment map with multiple groups from same or different enrichment
#' analyses
#'
#' @description User selected enrichment terms are used to create an enrichment
#' map. The selection of the term can by specifying by the
#' source of the terms (GO:MF, REAC, TF, etc.) or by listing the selected
#' term IDs. The map is only generated when there is at least on
#' significant term to graph.
#'
#' @param gostObjectList a \code{list} of \code{gprofiler2} objects that
#' contain the results from an enrichment analysis. The list must contain at
#' least 2 entries. The number of entries must correspond to the number of
#' entries for the \code{queryList} parameter.
#'
#' @param queryInfo a \code{data.frame} contains one row per group being
#' displayed. The number of rows must correspond to the
#' number of entries for the \code{gostObjectList} parameter.
#' The mandatory columns are:
#' \itemize{
#' \item{\code{queryName}: a \code{character} string representing the name
#' of the query retained for this group). The query names must exist in the
#' associated \code{gostObjectList} objects and follow the same order. }
#' \item{\code{source}: a \code{character} string representing the selected
#' source that will be used to generate the network. To hand-pick the terms to
#' be used, "TERM_ID" should be used and the list of selected term IDs should
#' be passed through the \code{termIDs} parameter. The possible sources are
#' "GO:BP" for Gene Ontology Biological Process, "GO:CC" for Gene Ontology
#' Cellular Component, "GO:MF" for Gene Ontology Molecular Function,
#' "KEGG" for Kegg, "REAC" for Reactome, "TF" for TRANSFAC, "MIRNA" for
#' miRTarBase, "CORUM" for CORUM database, "HP" for Human phenotype ontology
#' and "WP" for WikiPathways. Default: "TERM_ID". }
#' \item{\code{removeRoot}: a \code{logical} that specified if the root terms
#' of the selected source should be removed (when present). }
#' \item{\code{termIDs}: a \code{character} strings that contains the
#' term IDS retained for the creation of the network separated by a comma ','
#' when the "TERM_ID" source is selected. Otherwise, it should be a empty
#' string (""). }
#' \item{\code{groupName}: a \code{character} strings that contains the
#' name of the group to be shown in the legend. Each group has to have a
#' unique name. }
#' }
#'
#' @param showCategory a positive \code{integer} or a \code{vector} of
#' \code{characters} representing terms. If a \code{integer}, the first
#' \code{n} terms will be displayed. If \code{vector} of terms,
#' the selected terms will be displayed. Default: \code{30L}.
#'
#' @param groupCategory a \code{logical} indicating if the categories should
#' be grouped. Default: \code{FALSE}.
#'
#' @param categoryLabel a positive \code{numeric} representing the amount by
#' which plotting category nodes label size should be scaled relative
#' to the default (1). Default: \code{1}.
#'
#' @param categoryNode a positive \code{numeric} representing the amount by
#' which plotting category nodes should be scaled relative to the default (1).
#' Default: \code{1}.
#'
#' @param line a non-negative \code{numeric} representing the scale of line
#' width. Default: \code{1}.
#'
#' @param ... additional arguments that will be pass to the
#' \code{\link[enrichplot]{emapplot}} function.
#'
#' @return a \code{ggplot} object which is the enrichment map for enrichment
#' results.
#'
#' @examples
#'
#' ## Loading dataset containing results from 2 enrichment analyses done with
#' ## gprofiler2
#' data(parentalNapaVsDMSOEnrichment)
#' data(rosaNapaVsDMSOEnrichment)
#'
#' ## TODO
#' gostObjectList=list(parentalNapaVsDMSOEnrichment,
#' parentalNapaVsDMSOEnrichment, rosaNapaVsDMSOEnrichment,
#' rosaNapaVsDMSOEnrichment)
#'
#' ## Create data frame containing required information enabling the
#' ## selection of the retained enriched terms for each enrichment analysis.
#' ## One line per enrichment analyses present in the gostObjectList parameter
#' ## With this data frame, the enrichment results will be split in 4 groups:
#' ## 1) KEGG significant terms from parental napa vs DMSO (no root term)
#' ## 2) REACTOME significant terms from parental napa vs DMSO (no root term)
#' ## 3) KEGG significant terms from rosa napa vs DMSO (no root term)
#' ## 4) REACTOME significant terms from rosa napa vs DMSO (no root term)
#' queryDataFrame <- data.frame(queryName=c("parental_napa_vs_DMSO",
#' "parental_napa_vs_DMSO", "rosa_napa_vs_DMSO", "rosa_napa_vs_DMSO"),
#' source=c("KEGG", "REAC", "KEGG", "REAC"),
#' removeRoot=c(TRUE, TRUE, TRUE, TRUE), termIDs=c("", "", "", ""),
#' groupName=c("parental - KEGG", "parental - Reactome",
#' "rosa - KEGG", "rosa - Reactome"), stringsAsFactors=FALSE)
#'
#' ## Create graph for KEGG and REACTOME significant results from
#' ## 2 enrichment analyses
#' createEnrichMapMultiComplex(gostObjectList=gostObjectList,
#' queryInfo=queryDataFrame, line=1.5)
#'
#' @author Astrid Deschênes
#' @importFrom gprofiler2 gconvert
#' @importFrom strex match_arg
#' @importFrom stringr str_split str_trim
#' @encoding UTF-8
#' @export
createEnrichMapMultiComplex <- function(gostObjectList, queryInfo,
showCategory=30L, groupCategory=FALSE, categoryLabel=1,
categoryNode=1, line=1, ...) {
## Validate parameters
validateCreateEnrichMapMultiComplexArg(gostObjectList=gostObjectList,
queryInfo=queryInfo, showCategory=showCategory,
categoryLabel=categoryLabel, groupCategory=groupCategory,
categoryNode=categoryNode, line=line)
## Extract results
gostResultsList <- lapply(gostObjectList, FUN=function(x) {x$result})
## Retain results associated to query
gostResultsList <- lapply(seq_len(length(gostObjectList)),
FUN=function(i, queryI, gostL) {
gostL[[i]][gostL[[i]]$query == queryI$queryName[[i]], ]},
queryI=queryInfo, gostL=gostResultsList)
## Filter results
gostResultsList <- lapply(seq_len(length(gostObjectList)),
FUN=function(i, queryI, gostL) {
if (queryI$source[i] == "TERM_ID") {
terms <- unlist(str_split(queryI$termIDs[i], ","))
terms <- str_trim(terms)
return(gostL[[i]][gostL[[i]]$term_id %in% terms, ])
} else {
return(gostL[[i]][gostL[[i]]$source == queryI$source[i], ])
}
}, queryI=queryInfo, gostL=gostResultsList)
## Remove root terms when requested
gostResultsList <- lapply(seq_len(length(gostObjectList)),
FUN=function(i, queryI, gostL) {
if (queryI$removeRoot[i]) {
return(removeRootTerm(gostL[[i]]))
} else {
return(gostL[[i]])
}
}, queryI=queryInfo, gostL=gostResultsList)
## Validate that at least one term is left
if (sum(unlist(lapply(gostResultsList, FUN=function(x) {nrow(x)})))
== 0) {
stop("With removal of the root term, there is no ",
"enrichment term left")
}
## Create multi categories emap
emap <- createMultiEmap(gostResultsList=gostResultsList,
queryList=queryInfo$groupName, showCategory=showCategory,
categoryLabel=categoryLabel, groupCategory=groupCategory,
categoryNode=categoryNode, line=line, ...)
return(emap)
}
adeschen/gprofiler2cytoscape documentation built on Nov. 4, 2024, 3:16 p.m.
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Defines functions createEnrichMapMultiComplex createEnrichMapMultiBasic createEnrichMap
Documented in createEnrichMap createEnrichMapMultiBasic createEnrichMapMultiComplex
#' @title Using functional enrichment results in gprofiler2 format to create
#' an enrichment map
#'
#' @description User selected enrichment terms are used to create an enrichment
#' map. The selection of the term can by specifying by the
#' source of the terms (GO:MF, REAC, TF, etc.) or by listing the selected
#' term IDs. The map is only generated when there is at least on
#' significant term to graph.
#'
#' @param gostObject a \code{list} corresponding to gprofiler2 enrichment
#' output that contains and that contains
#' the results from an enrichment analysis.
#'
#' @param query a \code{character} string representing the name of the query
#' that is going to be used to generate the graph. The query must exist in the
#' \code{gostObject} object.
#'
#' @param source a \code{character} string representing the selected source
#' that will be used to generate the network. To hand-pick the terms to be
#' used, "TERM_ID" should be used and the list of selected term IDs should
#' be passed through the \code{termIDs} parameter. The possible sources are
#' "GO:BP" for Gene Ontology Biological Process, "GO:CC" for Gene Ontology
#' Cellular Component, "GO:MF" for Gene Ontology Molecular Function,
#' "KEGG" for Kegg, "REAC" for Reactome, "TF" for TRANSFAC, "MIRNA" for
#' miRTarBase, "CORUM" for CORUM database, "HP" for Human phenotype ontology
#' and "WP" for WikiPathways. Default: "TERM_ID".
#'
#' @param removeRoot a \code{logical} that specified if the root terms of
#' the selected source should be removed (when present). Default: \code{TRUE}.
#'
#' @param termIDs a \code{vector} of \code{character} strings that contains the
#' term IDS retained for the creation of the network. Default: \code{NULL}.
#'
#' @param showCategory a positive \code{integer} or a \code{vector} of
#' \code{characters} representing terms. If a \code{integer}, the first
#' \code{n} terms will be displayed. If \code{vector} of terms,
#' the selected terms will be displayed. Default: \code{30L}.
#'
#' @param groupCategory a \code{logical} indicating if the categories should
#' be grouped. Default: \code{FALSE}.
#'
#' @param categoryLabel a positive \code{numeric} representing the amount by
#' which plotting category nodes label size should be scaled relative
#' to the default (1). Default: \code{1}.s
#'
#' @param categoryNode a positive \code{numeric} representing the amount by
#' which plotting category nodes should be scaled relative to the default (1).
#' Default: \code{1}.
#'
#' @param line a non-negative \code{numeric} representing the scale of line
#' width. Default: \code{1}.
#'
#' @param ... additional arguments that will be pass to the
#' \code{\link[enrichplot]{emapplot}} function.
#'
#' @return a \code{ggplot} object which is the enrichment map for enrichment
#' results.
#'
#' @examples
#'
#' ## Loading dataset containing result from an enrichment analysis done with
#' ## gprofiler2
#' data(parentalNapaVsDMSOEnrichment)
#'
#' ## Extract query information (only one in this dataset)
#' query <- unique(parentalNapaVsDMSOEnrichment$result$query)
#'
#' ## Create graph for Gene Ontology - Cellular Component related results
#' createEnrichMap(gostObject=parentalNapaVsDMSOEnrichment,
#' query=query, source="GO:CC", removeRoot=TRUE)
#'
#'
#' @author Astrid Deschênes
#' @importFrom gprofiler2 gconvert
#' @importFrom strex match_arg
#' @encoding UTF-8
#' @export
createEnrichMap <- function(gostObject, query, source=c("TERM_ID", "GO:MF",
"GO:CC", "GO:BP", "KEGG", "REAC", "TF", "MIRNA", "HPA", "CORUM",
"HP", "WP"), termIDs=NULL, removeRoot=TRUE,
showCategory=30L, groupCategory=FALSE, categoryLabel=1,
categoryNode=1, line=1, ...) {
## Validate source is among the possible choices
source <- match_arg(source, ignore_case=TRUE)
## Validate parameters
validateCreateEnrichMapArguments(gostObject=gostObject, query=query,
source=source, termIDs=termIDs, removeRoot=removeRoot,
showCategory=showCategory, categoryLabel=categoryLabel,
groupCategory=groupCategory, categoryNode=categoryNode,
line=line)
## Extract results
gostResults <- gostObject$result
## Retain results associated to query
gostResults <- gostResults[gostResults$query == query,]
## Filter results
if (source == "TERM_ID") {
gostResults <- gostResults[gostResults$term_id %in% termIDs,]
} else {
gostResults <- gostResults[gostResults$source == source,]
}
## Remove root term if required
if (removeRoot) {
gostResults <- removeRootTerm(gostResults)
if (nrow(gostResults) == 0) {
stop("With removal of the root term, there is no ",
"enrichment term left")
}
}
meta <- gostObject$meta
backgroundGenes <- meta$query_metadata$queries[[query]]
significantMethod <- meta$query_metadata$significance_threshold_method
## Create basic emap
emap <- createBasicEmap(gostResults=gostResults,
backgroundGenes=backgroundGenes,
showCategory=showCategory, categoryLabel=categoryLabel,
groupCategory=groupCategory, categoryNode=categoryNode,
significantMethod=significantMethod, line=line, ...)
return(emap)
}
#' @title Using functional enrichment results in gprofiler2 format to create
#' an enrichment map with multiple groups from different enrichment analyses
#'
#' @description User selected enrichment terms are used to create an enrichment
#' map. The selection of the term can by specifying by the
#' source of the terms (GO:MF, REAC, TF, etc.) or by listing the selected
#' term IDs. The map is only generated when there is at least on
#' significant term to graph.
#'
#' @param gostObjectList a \code{list} of \code{gprofiler2} objects that
#' contain the results from an enrichment analysis. The list must contain at
#' least 2 entries. The number of entries must correspond to the number of
#' entries for the \code{queryList} parameter.
#'
#' @param queryList a \code{list} of \code{character} strings representing the
#' names of the queries that are going to be used to generate the graph.
#' The query names must exist in the associated \code{gostObjectList} objects
#' and follow the same order. The number of entries must correspond to the
#' number of entries for the \code{gostObjectList} parameter.
#'
#' @param source a \code{character} string representing the selected source
#' that will be used to generate the network. To hand-pick the terms to be
#' used, "TERM_ID" should be used and the list of selected term IDs should
#' be passed through the \code{termIDs} parameter. The possible sources are
#' "GO:BP" for Gene Ontology Biological Process, "GO:CC" for Gene Ontology
#' Cellular Component, "GO:MF" for Gene Ontology Molecular Function,
#' "KEGG" for Kegg, "REAC" for Reactome, "TF" for TRANSFAC, "MIRNA" for
#' miRTarBase, "CORUM" for CORUM database, "HP" for Human phenotype ontology
#' and "WP" for WikiPathways. Default: "TERM_ID".
#'
#' @param removeRoot a \code{logical} that specified if the root terms of
#' the selected source should be removed (when present). Default: \code{TRUE}.
#'
#' @param termIDs a \code{vector} of \code{character} strings that contains the
#' term IDS retained for the creation of the network. Default: \code{NULL}.
#'
#' @param showCategory a positive \code{integer} or a \code{vector} of
#' \code{characters} representing terms. If a \code{integer}, the first
#' \code{n} terms will be displayed. If \code{vector} of terms,
#' the selected terms will be displayed. Default: \code{30L}.
#'
#' @param groupCategory a \code{logical} indicating if the categories should
#' be grouped. Default: \code{FALSE}.
#'
#' @param categoryLabel a positive \code{numeric} representing the amount by
#' which plotting category nodes label size should be scaled relative
#' to the default (1). Default: \code{1}.
#'
#' @param categoryNode a positive \code{numeric} representing the amount by
#' which plotting category nodes should be scaled relative to the default (1).
#' Default: \code{1}.
#'
#' @param line a non-negative \code{numeric} representing the scale of line
#' width. Default: \code{1}.
#'
#' @param ... additional arguments that will be pass to the
#' \code{\link[enrichplot]{emapplot}} function.
#'
#' @return a \code{ggplot} object which is the enrichment map for enrichment
#' results.
#'
#' @examples
#'
#' ## Loading dataset containing results from 2 enrichment analyses done with
#' ## gprofiler2
#' data(parentalNapaVsDMSOEnrichment)
#' data(rosaNapaVsDMSOEnrichment)
#'
#' ## Extract query information (only one in each dataset)
#' query1 <- unique(parentalNapaVsDMSOEnrichment$result$query)[1]
#' query2 <- unique(rosaNapaVsDMSOEnrichment$result$query)[1]
#'
#' ## Create graph for KEGG related results from
#' ## 2 enrichment analyses
#' createEnrichMapMultiBasic(gostObjectList=list(parentalNapaVsDMSOEnrichment,
#' rosaNapaVsDMSOEnrichment),
#' queryList=list(query1, query2), source="KEGG", removeRoot=TRUE)
#'
#'
#' @author Astrid Deschênes
#' @importFrom gprofiler2 gconvert
#' @importFrom strex match_arg
#' @encoding UTF-8
#' @export
createEnrichMapMultiBasic <- function(gostObjectList, queryList,
source=c("TERM_ID", "GO:MF", "GO:CC", "GO:BP", "KEGG", "REAC", "TF",
"MIRNA", "HPA", "CORUM", "HP", "WP"), termIDs=NULL, removeRoot=TRUE,
showCategory=30L, groupCategory=FALSE, categoryLabel=1,
categoryNode=1, line=1, ...) {
## Validate source is among the possible choices
source <- match_arg(source, ignore_case=TRUE)
## Validate parameters
validateCreateEnrichMapMultiArguments(gostObjectList=gostObjectList,
queryList=queryList, source=source, termIDs=termIDs,
removeRoot=removeRoot, showCategory=showCategory,
categoryLabel=categoryLabel, groupCategory=groupCategory,
categoryNode=categoryNode, line=line)
## Extract results
gostResultsList <- lapply(gostObjectList, FUN=function(x) {x$result})
## Retain results associated to query
gostResultsList <- lapply(seq_len(length(queryList)),
FUN=function(i, queryL, gostL) {
gostL[[i]][gostL[[i]]$query == queryL[[i]], ]},
queryL=queryList, gostL=gostResultsList)
## Filter results
if (source == "TERM_ID") {
gostResultsList <- lapply(gostResultsList, FUN=function(x, termIDs) {
x[x$term_id %in% termIDs,]}, termIDs=termIDs)
} else {
gostResultsList <- lapply(gostResultsList, FUN=function(x, source) {
x[x$source == source,]}, source=source)
}
## Remove root term if required
if (removeRoot) {
gostResultsList <- lapply(gostResultsList, FUN=function(x) {
removeRootTerm(x)})
}
## Validate that at least one term is left
if (sum(unlist(lapply(gostResultsList, FUN=function(x) {nrow(x)})))
== 0) {
stop("With removal of the root term, there is no ",
"enrichment term left")
}
## Create multi categories emap
emap <- createMultiEmap(gostResultsList=gostResultsList,
queryList=queryList, showCategory=showCategory,
categoryLabel=categoryLabel, groupCategory=groupCategory,
categoryNode=categoryNode, line=line, ...)
return(emap)
}
#' @title Using functional enrichment results in gprofiler2 format to create
#' an enrichment map with multiple groups from same or different enrichment
#' analyses
#'
#' @description User selected enrichment terms are used to create an enrichment
#' map. The selection of the term can by specifying by the
#' source of the terms (GO:MF, REAC, TF, etc.) or by listing the selected
#' term IDs. The map is only generated when there is at least on
#' significant term to graph.
#'
#' @param gostObjectList a \code{list} of \code{gprofiler2} objects that
#' contain the results from an enrichment analysis. The list must contain at
#' least 2 entries. The number of entries must correspond to the number of
#' entries for the \code{queryList} parameter.
#'
#' @param queryInfo a \code{data.frame} contains one row per group being
#' displayed. The number of rows must correspond to the
#' number of entries for the \code{gostObjectList} parameter.
#' The mandatory columns are:
#' \itemize{
#' \item{\code{queryName}: a \code{character} string representing the name
#' of the query retained for this group). The query names must exist in the
#' associated \code{gostObjectList} objects and follow the same order. }
#' \item{\code{source}: a \code{character} string representing the selected
#' source that will be used to generate the network. To hand-pick the terms to
#' be used, "TERM_ID" should be used and the list of selected term IDs should
#' be passed through the \code{termIDs} parameter. The possible sources are
#' "GO:BP" for Gene Ontology Biological Process, "GO:CC" for Gene Ontology
#' Cellular Component, "GO:MF" for Gene Ontology Molecular Function,
#' "KEGG" for Kegg, "REAC" for Reactome, "TF" for TRANSFAC, "MIRNA" for
#' miRTarBase, "CORUM" for CORUM database, "HP" for Human phenotype ontology
#' and "WP" for WikiPathways. Default: "TERM_ID". }
#' \item{\code{removeRoot}: a \code{logical} that specified if the root terms
#' of the selected source should be removed (when present). }
#' \item{\code{termIDs}: a \code{character} strings that contains the
#' term IDS retained for the creation of the network separated by a comma ','
#' when the "TERM_ID" source is selected. Otherwise, it should be a empty
#' string (""). }
#' \item{\code{groupName}: a \code{character} strings that contains the
#' name of the group to be shown in the legend. Each group has to have a
#' unique name. }
#' }
#'
#' @param showCategory a positive \code{integer} or a \code{vector} of
#' \code{characters} representing terms. If a \code{integer}, the first
#' \code{n} terms will be displayed. If \code{vector} of terms,
#' the selected terms will be displayed. Default: \code{30L}.
#'
#' @param groupCategory a \code{logical} indicating if the categories should
#' be grouped. Default: \code{FALSE}.
#'
#' @param categoryLabel a positive \code{numeric} representing the amount by
#' which plotting category nodes label size should be scaled relative
#' to the default (1). Default: \code{1}.
#'
#' @param categoryNode a positive \code{numeric} representing the amount by
#' which plotting category nodes should be scaled relative to the default (1).
#' Default: \code{1}.
#'
#' @param line a non-negative \code{numeric} representing the scale of line
#' width. Default: \code{1}.
#'
#' @param ... additional arguments that will be pass to the
#' \code{\link[enrichplot]{emapplot}} function.
#'
#' @return a \code{ggplot} object which is the enrichment map for enrichment
#' results.
#'
#' @examples
#'
#' ## Loading dataset containing results from 2 enrichment analyses done with
#' ## gprofiler2
#' data(parentalNapaVsDMSOEnrichment)
#' data(rosaNapaVsDMSOEnrichment)
#'
#' ## TODO
#' gostObjectList=list(parentalNapaVsDMSOEnrichment,
#' parentalNapaVsDMSOEnrichment, rosaNapaVsDMSOEnrichment,
#' rosaNapaVsDMSOEnrichment)
#'
#' ## Create data frame containing required information enabling the
#' ## selection of the retained enriched terms for each enrichment analysis.
#' ## One line per enrichment analyses present in the gostObjectList parameter
#' ## With this data frame, the enrichment results will be split in 4 groups:
#' ## 1) KEGG significant terms from parental napa vs DMSO (no root term)
#' ## 2) REACTOME significant terms from parental napa vs DMSO (no root term)
#' ## 3) KEGG significant terms from rosa napa vs DMSO (no root term)
#' ## 4) REACTOME significant terms from rosa napa vs DMSO (no root term)
#' queryDataFrame <- data.frame(queryName=c("parental_napa_vs_DMSO",
#' "parental_napa_vs_DMSO", "rosa_napa_vs_DMSO", "rosa_napa_vs_DMSO"),
#' source=c("KEGG", "REAC", "KEGG", "REAC"),
#' removeRoot=c(TRUE, TRUE, TRUE, TRUE), termIDs=c("", "", "", ""),
#' groupName=c("parental - KEGG", "parental - Reactome",
#' "rosa - KEGG", "rosa - Reactome"), stringsAsFactors=FALSE)
#'
#' ## Create graph for KEGG and REACTOME significant results from
#' ## 2 enrichment analyses
#' createEnrichMapMultiComplex(gostObjectList=gostObjectList,
#' queryInfo=queryDataFrame, line=1.5)
#'
#' @author Astrid Deschênes
#' @importFrom gprofiler2 gconvert
#' @importFrom strex match_arg
#' @importFrom stringr str_split str_trim
#' @encoding UTF-8
#' @export
createEnrichMapMultiComplex <- function(gostObjectList, queryInfo,
showCategory=30L, groupCategory=FALSE, categoryLabel=1,
categoryNode=1, line=1, ...) {
## Validate parameters
validateCreateEnrichMapMultiComplexArg(gostObjectList=gostObjectList,
queryInfo=queryInfo, showCategory=showCategory,
categoryLabel=categoryLabel, groupCategory=groupCategory,
categoryNode=categoryNode, line=line)
## Extract results
gostResultsList <- lapply(gostObjectList, FUN=function(x) {x$result})
## Retain results associated to query
gostResultsList <- lapply(seq_len(length(gostObjectList)),
FUN=function(i, queryI, gostL) {
gostL[[i]][gostL[[i]]$query == queryI$queryName[[i]], ]},
queryI=queryInfo, gostL=gostResultsList)
## Filter results
gostResultsList <- lapply(seq_len(length(gostObjectList)),
FUN=function(i, queryI, gostL) {
if (queryI$source[i] == "TERM_ID") {
terms <- unlist(str_split(queryI$termIDs[i], ","))
terms <- str_trim(terms)
return(gostL[[i]][gostL[[i]]$term_id %in% terms, ])
} else {
return(gostL[[i]][gostL[[i]]$source == queryI$source[i], ])
}
}, queryI=queryInfo, gostL=gostResultsList)
## Remove root terms when requested
gostResultsList <- lapply(seq_len(length(gostObjectList)),
FUN=function(i, queryI, gostL) {
if (queryI$removeRoot[i]) {
return(removeRootTerm(gostL[[i]]))
} else {
return(gostL[[i]])
}
}, queryI=queryInfo, gostL=gostResultsList)
## Validate that at least one term is left
if (sum(unlist(lapply(gostResultsList, FUN=function(x) {nrow(x)})))
== 0) {
stop("With removal of the root term, there is no ",
"enrichment term left")
}
## Create multi categories emap
emap <- createMultiEmap(gostResultsList=gostResultsList,
queryList=queryInfo$groupName, showCategory=showCategory,
categoryLabel=categoryLabel, groupCategory=groupCategory,
categoryNode=categoryNode, line=line, ...)
return(emap)
}
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