clEnrich {cogena} | R Documentation |
Gene set enrichment for clusters sourced from coExp function. the enrichment score are based on -log2(p) with p from hyper-geometric test.
clEnrich(genecl_obj, annofile = NULL, sampleLabel = NULL, TermFreq = 0, ncore = 1)
genecl_obj |
a genecl object |
annofile |
gene set annotation file |
sampleLabel |
sameple Label. Do make the label of interest located after the control label in the order of factor. See details. |
TermFreq |
a value from [0,1) to filter low-frequence gene sets |
ncore |
the number of cores used |
sampleLable: Use factor(c("Normal", "Cancer", "Normal"), levels=c("Normal", "Cancer")), instead of factor(c("Normal", "Cancer","Normal")). This parameter will affect the direction of gene regulation in cogena.
Gene sets availiable (See vignette for more):
c2.cp.kegg.v5.0.symbols.gmt.xz (From Msigdb)
c2.cp.reactome.v5.0.symbols.gmt.xz (From Msigdb)
c5.bp.v5.0.symbols.gmt.xz (From Msigdb)
c2.cp.biocarta.v5.0.symbols.gmt.xz (From Msigdb)
c2.all.v5.0.symbols.gmt.xz (From Msigdb)
c2.cp.v5.0.symbols.gmt.xz (From Msigdb)
c5.mf.v5.0.symbols.gmt.xz (From Msigdb)
a list containing the enrichment score for each clustering methods and cluster numbers included in the genecl_obj
Gene sets are from
1. http://www.broadinstitute.org/gsea/msigdb/index.jsp
2. http://amp.pharm.mssm.edu/Enrichr/
#annotaion annoGMT <- "c2.cp.kegg.v5.0.symbols.gmt.xz" annofile <- system.file("extdata", annoGMT, package="cogena") utils::data(Psoriasis) clMethods <- c("hierarchical","kmeans","diana","fanny","som","model","sota","pam","clara","agnes") genecl_result <- coExp(DEexprs, nClust=2:3, clMethods=c("hierarchical","kmeans"), metric="correlation", method="complete", ncore=2, verbose=TRUE) clen_res <- clEnrich(genecl_result, annofile=annofile, sampleLabel=sampleLabel)