Using functional enrichment results in gprofiler2 format to create an enrichment map with multiple groups from same or different enrichment analyses

Source: R/methodsEmap.R

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.

createEnrichMapMultiComplex(
  gostObjectList,
  queryInfo,
  showCategory = 30L,
  groupCategory = FALSE,
  categoryLabel = 1,
  categoryNode = 1,
  line = 1,
  force = TRUE,
  ...
)

Arguments

gostObjectList

a list of 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 queryList parameter.

queryInfo

a data.frame contains one row per group being displayed. The number of rows must correspond to the number of entries for the gostObjectList parameter. The mandatory columns are:

  • queryName: a character string representing the name of the query retained for this group). The query names must exist in the associated gostObjectList objects and follow the same order.

  • source: a 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 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".

  • removeRoot: a logical that specified if the root terms of the selected source should be removed (when present).

  • termIDs: a 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 ("").

  • groupName: a character strings that contains the name of the group to be shown in the legend. Each group has to have a unique name.

showCategory

a positive integer or a vector of characters representing terms. If a integer, the first n terms will be displayed. If vector of terms, the selected terms will be displayed. Default: 30L.

groupCategory

a logical indicating if the categories should be grouped. Default: FALSE.

categoryLabel

a positive numeric representing the amount by which plotting category nodes label size should be scaled relative to the default (1). Default: 1.

categoryNode

a positive numeric representing the amount by which plotting category nodes should be scaled relative to the default (1). Default: 1.

line

a non-negative numeric representing the scale of line width. Default: 1.

force

a logical indicating if the repulsion between overlapping text labels should be forced. Default: TRUE.

...

additional arguments that will be pass to the emapplot function.

Value

a ggplot object which is the enrichment map for enrichment results.

Author

Astrid Deschênes

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)