goregion: Gene ontology testing of DMRs for Ilumina methylation array...

In Oshlack/missMethyl: Analysing Illumina HumanMethylation BeadChip Data

Gene ontology testing of DMRs for Ilumina methylation array data

Description

Tests gene ontology or KEGG pathway enrichment for differentially methylated regions (DMRs) identified from Illumina's Infinium HumanMethylation450 or MethylationEPIC array, taking into account the differing number of probes per gene present on the array.

Usage

goregion(
  regions,
  all.cpg = NULL,
  collection = c("GO", "KEGG"),
  array.type = c("450K", "EPIC"),
  plot.bias = FALSE,
  prior.prob = TRUE,
  anno = NULL,
  equiv.cpg = TRUE,
  fract.counts = TRUE,
  genomic.features = c("ALL", "TSS200", "TSS1500", "Body", "1stExon", "3'UTR", "5'UTR",
    "ExonBnd"),
  sig.genes = FALSE
)

Arguments

regions	GRanges object of DMR coordinates to test for GO term enrichment.
all.cpg	Character vector of all CpG sites tested. Defaults to all CpG sites on the array.
collection	The collection of pathways to test. Options are "GO" and "KEGG". Defaults to "GO".
array.type	The Illumina methylation array used. Options are "450K" or "EPIC". Defaults to "450K".
plot.bias	Logical, if true a plot showing the bias due to the differing numbers of probes per gene will be displayed.
prior.prob	Logical, if true will take into account the probability of significant differentially methylation due to numbers of probes per gene. If false, a hypergeometric test is performed ignoring any bias in the data.
anno	Optional. A DataFrame object containing the complete array annotation as generated by the minfi. getAnnotation function. Speeds up execution, if provided.
equiv.cpg	Logical, if true then equivalent numbers of cpgs are used for odds calculation rather than total number cpgs. Only used if prior.prob=TRUE.
fract.counts	Logical, if true then fractional counting of cpgs is used to account for cgps that map to multiple genes. Only used if prior.prob=TRUE.
genomic.features	Character vector or scalar indicating whether the gene set enrichment analysis should be restricted to CpGs from specific genomic locations. Options are "ALL", "TSS200","TSS1500","Body","1stExon", "3'UTR","5'UTR","ExonBnd"; and the user can select any combination. Defaults to "ALL".
sig.genes	Logical, if true then the significant differentially methylated genes that overlap with the gene set of interest is outputted as the final column in the results table. Default is FALSE.

Details

This function takes a GRanges object of DMR coordinates, maps them to CpG sites on the array and then to Entrez Gene IDs, and tests for GO term or KEGG pathway enrichment using Wallenius' noncentral hypergeometric test, taking into account the number of CpG sites per gene on the 450K/EPIC array. If prior.prob is set to FALSE, then prior probabilities are not used and it is assumed that each gene is equally likely to have a significant CpG site associated with it. Please not that we have tested goregion and gsaregion extensively using the DMRCate package to identify differentially methylated regions (Peters, et al., 2015).

The testing now also takes into account that some CpGs map to multiple genes. For a small number of gene families, this previously caused their associated GO categories/gene sets to be erroneously overrepresented and thus highly significant. If fract.counts=FALSE then CpGs are allowed to map to multiple genes (this is NOT recommended).

Genes associated with each CpG site are obtained from the annotation package IlluminaHumanMethylation450kanno.ilmn12.hg19 if the array type is "450K". For the EPIC array, the annotation package IlluminaHumanMethylationEPICanno.ilm10b4.hg19 is used. To use a different annotation package, please supply it using the anno argument.

In order to get a list which contains the mapped Entrez gene IDS, please use the getMappedEntrezIDs function. goregion tests all GO or KEGG terms, and false discovery rates are calculated using the method of Benjamini and Hochberg (1995). The topGSA function can be used to display the top 20 most enriched pathways.

If you are interested in which genes overlap with the genes in the gene set, setting sig.genes to TRUE will output an additional column in the results data frame that contains all the significant differentially methylated gene symbols, comma separated. The default is FALSE.

For more generalised gene set testing where the user can specify the gene set/s of interest to be tested, please use the gsaregion function.

Value

A data frame with a row for each GO or KEGG term and the following columns:

Term	GO term if testing GO pathways
Ont	ontology that the GO term belongs to if testing GO pathways. "BP" - biological process, "CC" - cellular component, "MF" - molecular function.
Pathway	the KEGG pathway being tested if testing KEGG terms.
N	number of genes in the GO or KEGG term
DE	number of genes that are differentially methylated
P.DE	p-value for over-representation of the GO or KEGG term term
FDR	False discovery rate
SigGenesInSet	Significant differentially methylated genes overlapping with the gene set of interest.

Author(s)

Jovana Maksimovic

References

Phipson, B., Maksimovic, J., and Oshlack, A. (2016). missMethyl: an R package for analysing methylation data from Illuminas HumanMethylation450 platform. Bioinformatics, 15;32(2), 286–8.

Geeleher, P., Hartnett, L., Egan, L. J., Golden, A., Ali, R. A. R., and Seoighe, C. (2013). Gene-set analysis is severely biased when applied to genome-wide methylation data. Bioinformatics, 29(15), 1851–1857.

Young, M. D., Wakefield, M. J., Smyth, G. K., and Oshlack, A. (2010). Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology, 11, R14.

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., and Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, gkv007.

Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series, B, 57, 289-300.

Peters, T.J., Buckley, M.J., Statham, A.L., Pidsley, R., Samaras, K., Lord, R.V., Clark, S.J.,Molloy, P.L. (2015). De novo identification of differentially methylated regions in the human genome. Epigenetics & Chromatin, 8, 6.

See Also

gometh,gsameth,gsaregion

Examples

## Not run:  # to avoid timeout on Bioconductor build
library(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
library(limma)
library(DMRcate)
library(ExperimentHub)

# Follow the example for the dmrcate function to get some EPIC data from
# ExperimentHub
eh <- ExperimentHub()
FlowSorted.Blood.EPIC <- eh[["EH1136"]]
tcell <- FlowSorted.Blood.EPIC[,colData(FlowSorted.Blood.EPIC)$CD4T==100 |
                                colData(FlowSorted.Blood.EPIC)$CD8T==100]
detP <- detectionP(tcell)
remove <- apply(detP, 1, function (x) any(x > 0.01))
tcell <- tcell[!remove,]
tcell <- preprocessFunnorm(tcell)
#Subset to chr2 only
tcell <- tcell[seqnames(tcell) == "chr2",]
tcellms <- getM(tcell)
tcellms.noSNPs <- rmSNPandCH(tcellms, dist=2, mafcut=0.05)
tcell$Replicate[tcell$Replicate==""] <- tcell$Sample_Name[tcell$Replicate==""]
tcellms.noSNPs <- avearrays(tcellms.noSNPs, tcell$Replicate)
tcell <- tcell[,!duplicated(tcell$Replicate)]
tcell <- tcell[rownames(tcellms.noSNPs),]
colnames(tcellms.noSNPs) <- colnames(tcell)
assays(tcell)[["M"]] <- tcellms.noSNPs
assays(tcell)[["Beta"]] <- ilogit2(tcellms.noSNPs)

# Perform region analysis
type <- factor(tcell$CellType)
design <- model.matrix(~type) 
myannotation <- cpg.annotate("array", tcell, arraytype = "EPIC",
                             analysis.type="differential", design=design, 
                             coef=2)
# Run DMRCate with beta value cut-off filter of 0.1                              
dmrcoutput <- dmrcate(myannotation, lambda=1000, C=2, betacutoff = 0.1)
regions <- extractRanges(dmrcoutput)
length(regions)

ann <- getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
# All CpG sites tested (limited to chr 2)
allcpgs <- rownames(tcell)
# GO testing with prior probabilities taken into account
# Plot of bias due to differing numbers of CpG sites per gene
gst <- goregion(regions = regions, all.cpg = allcpgs, collection = "GO", 
                array.type = "EPIC", plot.bias = TRUE, prior.prob = TRUE, 
                anno = ann)
# Table of top GO results
topGSA(gst, n=10)

# KEGG testing
kegg <- goregion(regions = regions, all.cpg = allcpgs, collection = "KEGG", 
                 array.type = "EPIC", prior.prob=TRUE, anno = ann)
# Table of top KEGG results
topGSA(kegg, n=10)

# Restrict to promoter regions
gst.prom <- goregion(regions = regions, all.cpg = allcpgs, collection = "GO", 
                array.type = "EPIC", plot.bias = TRUE, prior.prob = TRUE, 
                anno = ann, genomic.features = c("TSS200","TSS1500"))
topGSA(gst.prom, n=10)

# Add significant genes in gene set to KEGG output
kegg <- goregion(regions = regions, all.cpg = allcpgs, collection = "KEGG", 
                 array.type = "EPIC", prior.prob=TRUE, anno = ann, 
                 sig.genes = TRUE)
# Table of top KEGG results
topGSA(kegg, n=5)

## End(Not run)

Oshlack/missMethyl documentation built on March 26, 2023, 1:50 p.m.