topGSA {missMethyl} | R Documentation |
After using gsameth
, calling topGSA will output the top 20 most
significantly enriched pathways.
topGSA(gsa, number = 20, sort = TRUE)
gsa |
Matrix, from output of |
number |
Scalar, number of pathway results to output. Default is 20. |
sort |
Logical, should the table be ordered by p-value. Default is TRUE. |
This function will output the top 20 most significant pathways from a
pathway analysis using the gsameth
function. The output is ordered by
p-value.
A matrix ordered by P.DE, with a row for each gene set and the following columns:
N |
number of genes in the gene set |
DE |
number of genes that are differentially methylated |
P.DE |
p-value for over-representation of the gene set |
FDR |
False discovery rate, calculated using the method of Benjamini and Hochberg (1995) |
.
SigGenesInSet |
Significant differentially methylated genes overlapping with the gene set of interest. |
Belinda Phipson
library(IlluminaHumanMethylation450kanno.ilmn12.hg19) library(org.Hs.eg.db) library(limma) ann <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19) # Randomly select 1000 CpGs to be significantly differentially methylated sigcpgs <- sample(rownames(ann),1000,replace=FALSE) # All CpG sites tested allcpgs <- rownames(ann) # Use org.Hs.eg.db to extract a GO term GOtoID <- toTable(org.Hs.egGO2EG) setname1 <- GOtoID$go_id[1] setname1 keep.set1 <- GOtoID$go_id %in% setname1 set1 <- GOtoID$gene_id[keep.set1] setname2 <- GOtoID$go_id[2] setname2 keep.set2 <- GOtoID$go_id %in% setname2 set2 <- GOtoID$gene_id[keep.set2] # Make the gene sets into a list sets <- list(set1, set2) names(sets) <- c(setname1,setname2) # Testing with prior probabilities taken into account # Plot of bias due to differing numbers of CpG sites per gene gst <- gsameth(sig.cpg = sigcpgs, all.cpg = allcpgs, collection = sets, plot.bias = TRUE, prior.prob = TRUE) topGSA(gst) # Testing ignoring bias gst.bias <- gsameth(sig.cpg = sigcpgs, all.cpg = allcpgs, collection = sets, prior.prob = FALSE) topGSA(gst.bias)