topGSA: Get table of top 20 enriched pathways

Description Usage Arguments Details Value Author(s) See Also Examples

Description

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

Usage

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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)


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