enrichplot (version 1.13.1.994)

pairwise_termsim: pairwise_termsim

Description

Get the similarity matrix

Usage

pairwise_termsim(x, method = "JC", semData = NULL, showCategory = 200)
# S4 method for enrichResult
pairwise_termsim(x, method = "JC", semData = NULL, showCategory = 200)
# S4 method for gseaResult
pairwise_termsim(x, method = "JC", semData = NULL, showCategory = 200)
# S4 method for compareClusterResult
pairwise_termsim(x, method = "JC", semData = NULL, showCategory = 200)
pairwise_termsim.enrichResult(
  x,
  method = "JC",
  semData = NULL,
  showCategory = 200
)
pairwise_termsim.compareClusterResult(
  x,
  method = "JC",
  semData = NULL,
  showCategory = 200
)

Arguments

x

enrichment result.

method

method of calculating the similarity between nodes, one of "Resnik", "Lin", "Rel", "Jiang" , "Wang" and "JC"(Jaccard similarity coefficient) methods.

semData

GOSemSimDATA object, can be obtained through godata function in GOSemSim package.

showCategory

number of enriched terms to display, default value is 200.

Details

This function add similarity matrix to the termsim slot of enrichment result. Users can use the `method` parameter to select the method of calculating similarity. The Jaccard correlation coefficient(JC) is used by default, and it applies to all situations. When users want to calculate the correlation between GO terms or DO terms, they can also choose "Resnik", "Lin", "Rel" or "Jiang" (they are semantic similarity calculation methods from GOSemSim packages), and at this time, the user needs to provide `semData` parameter, which can be obtained through godata function in GOSemSim package.

Examples

# NOT RUN {
    library(clusterProfiler)
    library(org.Hs.eg.db)
    library(enrichplot)
    library(GOSemSim)
    library(DOSE)
    data(geneList)
    gene <- names(geneList)[abs(geneList) > 2]
    ego <- enrichGO(gene  = gene,
        universe      = names(geneList),
        OrgDb         = org.Hs.eg.db,
        ont           = "BP",
        pAdjustMethod = "BH",
        pvalueCutoff  = 0.01,
        qvalueCutoff  = 0.05,
        readable      = TRUE)
    d <- godata('org.Hs.eg.db', ont="BP")
    ego2 <- pairwise_termsim(ego, method="Wang", semData = d)
    emapplot(ego2)
    emapplot_cluster(ego2)
   
# }