Get table of top 20 enriched pathways

Description

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

Usage

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

Arguments

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:

Author(s)

Belinda Phipson

See Also

gsameth

Examples

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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(missMethyl)
Setting options('download.file.method.GEOquery'='auto')
Setting options('GEOquery.inmemory.gpl'=FALSE)

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/missMethyl/topGSA.Rd_%03d_medium.png", width=480, height=480)
> ### Name: topGSA
> ### Title: Get table of top 20 enriched pathways
> ### Aliases: topGSA
> 
> ### ** Examples
> 
> library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
Loading required package: minfi
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: lattice
Loading required package: GenomicRanges
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biostrings
Loading required package: XVector
Loading required package: bumphunter
Loading required package: foreach
Loading required package: iterators
Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22
> library(org.Hs.eg.db)
Loading required package: AnnotationDbi
> library(limma)

Attaching package: 'limma'

The following object is masked from 'package:BiocGenerics':

    plotMA

> 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$go_id[1]
> setname1
[1] "GO:0008150"
> keep.set1 <- GOtoID$go_id %in% setname1
> set1 <- GOtoID$gene_id[keep.set1]
> setname2 <- GOtoID$go_id[2]
> setname2
[1] "GO:0001869"
> 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)
Warning message:
In alias2SymbolTable(flat$symbol) :
  Multiple symbols ignored for one or more aliases
> topGSA(gst)
             N DE       P.DE       FDR
GO:0001869   2  0 0.06306581 0.1261316
GO:0008150 645 22 0.59784931 0.5978493
> 
> # Testing ignoring bias
> gst.bias <- gsameth(sig.cpg = sigcpgs, all.cpg = allcpgs, collection = sets, prior.prob = FALSE)
Warning message:
In alias2SymbolTable(flat$symbol) :
  Multiple symbols ignored for one or more aliases
> topGSA(gst.bias)
             N DE       P.DE       FDR
GO:0001869   2  0 0.07710909 0.1542182
GO:0008150 645 22 0.71573407 0.7157341
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>