cluster_enrichment — cluster_enrichment • leapR

Cluster enrichment Run enrichment (Fisher's exact) on clusters (lists of identifier groups)

Usage

cluster_enrichment(eset, assay_name, geneset, clusters, sigfilter = 0.05)

Arguments

eset: is an SummarizedExperiment containing data that is clustered
assay_name: is the name of the assay
geneset: is a GeneSet object for pathway annotation
clusters: is a list of clusters (gene lists) to calculate enrichment on, generally the result of the `cutree` function
sigfilter: minimum significance threshold default is .05

Value

data frame with enrichment results

Details

This function will calculate enrichment (Fisher's exact test for membership overlap) on

a series of lists of genes, such as from a set of clusters. The results are returned as

a list of results matrices in the order of the input clusters.

Examples

        library(leapR)
        library(BiocFileCache)
        
        path <- tools::R_user_dir("leapR", which = "cache")
        bfc <- BiocFileCache(path, ask = FALSE)
        
        url <- "https://api.figshare.com/v2/file/download/56536214"
        tc <- bfcadd(bfc, "tdat", fpath = url)
#> 
#> Error while performing HEAD request.
#>    Proceeding without cache information.
        load(tc)

        # read in the pathways
        data("ncipid")

        # for the example we will limit the number of transcripts considered
        #- arbitrarily in this case
        transdata <- SummarizedExperiment::assay(tset,'transcriptomics')
        transdata[which(is.na(transdata),arr.ind=TRUE)]<-0.0
        # perform heirarchical clustering on the  data
        transdata.hc <- hclust(dist(transdata), method="ward.D2")

        transdata.hc.clusters <- cutree(transdata.hc, k=5)
        clust.list <- lapply(seq_len(5), function(x) {
           return(names(which(transdata.hc.clusters==x)))})
        #calculates enrichment for each of the clusters individually a
        #and returns a list of enrichment results
        transdata.hc.enrichment <- leapR::cluster_enrichment(eset=tset,
                assay_name='transcriptomics',
                geneset=ncipid,
                clusters=clust.list)