Get table of top 20 enriched pathways

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Description

After using gsameth, calling topGSA will output the top 20 most significantly enriched pathways.

Usage

1
topGSA(gsa, number = 20, sort = TRUE)

Arguments

gsa

matrix, from output of gsameth

number

scalar, number of pathway results to output. Default is 20

sort

logical, should the table be ordered by p-value. Default is TRUE.

Details

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.

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).

Author(s)

Belinda Phipson

See Also

gsameth

Examples

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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)