[bigmw]

eg2id and id2eg are the pair of functions for ID mapping from and to Entrez Genes. For info:
?eg2id

Also, if I do want to convert gene set ID from Entrez to symbol.
How can I do it?

Thank you.

[bigmw]

GAGE and other methods (GSEA etc) require all genes included. This way GAGE test gene perturbations within pathways against the background of all genes. You selected a list of differentially expressed gene first, it is expected that you don’t any pathways standing out in that perforeground, right? Including all genes instead of a selected list of genes give you a major advantages: you included all your data in the analysis, which is usually more powerful. In addition, you don’t need some more or less arbitrary q-/p-value cutoff.

Otherwise, you code seem to work fine. You may want to check the DESeq section and the native workflow on the demo code:

http://www.bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf

Thank you, I followed it, after DESeq. 1724 differentially expressed genes were used for pathway analysis.

res <- nbinomTest( cds, 'control, 'treat' )

resSig <- res[ res$padj < 0.01 & (res$log2FoldChange >1| res$log2FoldChange < -1), ]

resSig <- na.omit(resSig)

require(gage)
...

Am I doing it right?

[crazyhottommy]

The pathview package provides two functions: eg2id and id2eg, for ID mapping/conversion for major research species. For details:
?pathview::eg2id

BTW, I would suggest you to convert your data ID from symbol to Entrez Gene, rather than your gene set ID from Entrez to symbol. The former should be much faster as it only need to call the conversion function once.

Also, if I do want to convert gene set ID from Entrez to symbol.
How can I do it?

Thank you.

[crazyhottommy]

BTW, has a separate tutorial on data preparation, you can check Section 5 -- gene or transcript ID conversion:
http://www.bioconductor.org/packages...c/dataPrep.pdf

Thank you, I followed it, after DESeq. 1724 differentially expressed genes were used for pathway analysis.

res <- nbinomTest( cds, 'control, 'treat' )

resSig <- res[ res$padj < 0.01 & (res$log2FoldChange >1| res$log2FoldChange < -1), ]

resSig <- na.omit(resSig)

require(gage)
datakegg.gs)
deseq.fc<- resSig$log2FoldChange
names(deseq.fc)<- resSig$id
sum(is.infinite(deseq.fc)) # there are some infinite numbers, if use DESeq2, no such problem.
deseq.fc[deseq.fc>10]=10
deseq.fc[deseq.fc<-10]=-10
exp.fc<- deseq.fc

#kegg.gsets works with 3000 KEGG speicies
data(korg)
head(korg[,1:3], n=20)


#let's get the annotation files for mouse and convert the gene set to gene symbol format
kg.mouse<- kegg.gsets("mouse")
kegg.gs<- kg.mouse$kg.sets[kg.mouse$sigmet.idx]
lapplykegg.gs[1:3],head)



# to convert IDs among gene/transcript ID to Entrez GeneID or reverse, use eg2id and id2eg in the pathview package
library(pathview)
data(bods)
bods

gene.symbol.eg<- id2eg(ids=names(exp.fc), category='SYMBOL', org='Mm')
# convert the gene symbol to Entrez Gene ID
head(gene.symbol.eg, n=100)
head(gene.symbol.eg[,2], n=10)

names(exp.fc)<- gene.symbol.eg[,2]

fc.kegg.p<- gage(exp.fc, gsets= kegg.gs, ref=NULL, samp=NULL)
sel<- fc.kegg.p$greater[,"q.val"] < 0.1 & !is.na(fc.kegg.p$greater[,"q.val"])
table(sel)

sel.l<- fc.kegg.p$less[,"q.val"] < 0.1 & !is.na(fc.kegg.p$greater[,"q.val"])
table(sel.l)



> table(sel.l)
sel.l
FALSE
202

> table(sel)
sel
FALSE
202

Am I doing it right?

[bigmw]

BTW, has a separate tutorial on data preparation, you can check Section 5 -- gene or transcript ID conversion:

http://www.bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/dataPrep.pdf

[bigmw]

The pathview package provides two functions: eg2id and id2eg, for ID mapping/conversion for major research species. For details:
?pathview::eg2id

BTW, I would suggest you to convert your data ID from symbol to Entrez Gene, rather than your gene set ID from Entrez to symbol. The former should be much faster as it only need to call the conversion function once.

[crazyhottommy]

Hi there, thank you for making this awesome tool.

I am working with mouse data, I want to know how to convert the gene set into gene symbol format.

kg.mouse<- kegg.gsets("mouse")
kegg.gs<- kg.mouse$kg.sets[kg.mouse$sigmet.idx]
lapply(kegg.gs[1:3],head)


the eg2sym function is only for human data. I can not do things below:

data(egSymb)
kegg.gs.sym<- lapply(kegg.gs, eg2sym)

Thank you!
Tommy

[tigerxu]

I have followed the default workflows of gage and pathview on the example RNA-seq dataset. I also used the fold changes inferred by deseq2, then followed by the gage and pathview. I found both pipelines will output different results. The pipeline based on the fold changes by deseq2 generate much fewer significant pathways. For example below

> gage.kegg.sig<-sigGeneSet(gage.kegg.p, outname="sig.kegg",pdf.size=c(7,8))
[1] "there are 22 signficantly up-regulated gene sets"
[1] "there are 17 signficantly down-regulated gene sets"

> deseq2.kegg.sig<-sigGeneSet(deseq2.kegg.p, outname="deseq2.sig.kegg",pdf.size=c(7,8))
[1] "gs.data needs to be a matrix-like object!"
[1] "No heatmap produced for down-regulated gene sets, only 1 or none signficant."
[1] "gs.data needs to be a matrix-like object!"
[1] "there are 7 signficantly up-regulated gene sets"
[1] "there are 0 signficantly down-regulated gene sets"

I'm wondering which pipeline is more reliable for biological interpretation. Why the pipeline based on deseq2 return much fewer pathways? Can anyone give me some advice?

Thanks!

[tigerxu]

Just checked the source code for sigGeneSet and internal functions gs.heatmap. there seems to be a potential conflict in argument margins indeed. Will have the problem fixed. you can check the updated version 2.14.3 in the next couple of days here:
http://www.bioconductor.org/packages...html/gage.html

Okay, thank! I will try version 2.14.3 later.

[bigmw]

Just checked the source code for sigGeneSet and internal functions gs.heatmap. there seems to be a potential conflict in argument margins indeed. Will have the problem fixed. you can check the updated version 2.14.3 in the next couple of days here:

http://www.bioconductor.org/packages/release/bioc/html/gage.html

[tigerxu]

This is the latest version. Do you still get the problem?

The problem is still there. But I have modified the margins parameters in the internal function sigGeneSet within the gage package. It can work!

[bigmw]

This is the latest version. Do you still get the problem?

[tigerxu]

You may want to check the version of the gage package you are running, which can be seen by:
sessionInfo()

other attached packages:
[1] gage_2.14.2 GenomicAlignments_1.0.2
[3] BSgenome_1.32.0 Rsamtools_1.16.1
[5] Biostrings_2.32.0 XVector_0.4.0
[7] DESeq2_1.4.5 RcppArmadillo_0.4.300.8.0
[9] Rcpp_0.11.2 GenomicRanges_1.16.3
[11] GenomeInfoDb_1.0.2 IRanges_1.22.9
[13] BiocGenerics_0.10.0

Is the version of gage not proper?

[bigmw]

You may want to check the version of the gage package you are running, which can be seen by:
sessionInfo()

[tigerxu]

Forgot that sigGeneSet function has been updated to give users more control on the margin and font size. sigGeneSet calls a internal function heatmap2 to generate the heatmaps. So check the argument for this function
args(gage:::heatmap2)
The argument two relevant arguments here are margins and cexRow, which control the margins for column/row names and row name font size, you may do something like:
kegg.sig<-sigGeneSet(cnts.kegg.p,outname="~/RNAseq/13_Acute-Changes/14_GAGE_native_A1A2/A1A2All/A1A2All.kegg",pdf.size = c(7,12), margins = c(5,10))

I have a question about the margin argument in the sigGeneSet function when I run the following command
> rcount.kegg.sig<-sigGeneSet(rcount.kegg.p, outname="sig.kegg",pdf.size=c(7,12),margins=c(5, 10))
Error in heatmap2(gs.data, Colv = F, Rowv = F, dendrogram = "none", col = cols, :
formal argument "margins" matched by multiple actual arguments

Can anyone help me?

Thanks!