Problems running GAGE

[sindrle]

Hi!
Here is my code, my input is from DESeq2 results.

deseq2.fc=timepointsResults$log2FoldChange;
names(deseq2.fc)=rownames(timepointsResults);
exp.fc=deseq2.fc;
out.suffix="timepointsResultsDESeq2"
require(gage);
data(kegg.gs);
dir.create("/Volumes/timemachine/HTseq_DEseq2/1_Results/GAGE_upregulated_timepointsResults");
setwd("/Volumes/timemachine/HTseq_DEseq2/1_Results/GAGE_upregulated_timepointsResults");
fc.kegg.p <- gage(exp.fc, gsets = kegg.gs, ref = NULL, samp = NULL, same.dir = TRUE) ;
sel <- fc.kegg.p$greater[, "q.val"] > 0.1 & !is.na(fc.kegg.p$greater[, "q.val"]) ;
path.ids <- rownames(fc.kegg.p$greater)[sel] ;
path.ids2 <- substr(path.ids, 1, 8) ;
require(pathview) ;
pv.out.list <- sapply(path.ids2[1:3], function(pid) pathview(gene.data = exp.fc, pathway.id = pid, species = "hsa", out.suffix=out.suffix))

I just want to look for the top 3 upregulated pathways, but I only get an "404 not found" error.

Anyone understand this?

[sindrle]

This is just wired..
I updated from R 3.0.1 to newest and installed a new package "httr" for GAGE.

And it works.....

Now I just have to understand how a "significantly upregulated pathway" is calculated as significant...

[bigmw]

It is always important to update R/Bioconductor to use the latest features of gage and pathview pacakges. In addition, if you installed the packages under current release versions of R(3.0.2)/ Bioconductor(2.13), you don’t really need to worry about any dependencies like httr.
I guess you are using the joint workflow described in the vignette “RNA-Seq Data Pathway and Gene-set Analysis Workflows”. Notice that the native GAGE/Pathview workflow may give you more sensitive results. You may check the main vignette “Generally Applicable Gene-set/Pathway Analysis” for more details of gage package:

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

To understand how GAGE tests for significant pathways, check the paper at: http://www.biomedcentral.com/1471-2105/10/161. HTH.

[sindrle]

Thanks!
Ill check it out.

[sindrle]

"Notice that the native GAGE/Pathview workflow may give you more sensitive results"

How come?

[bigmw]

The joint workflows are there for users’ convenience. You can do the differential expression analysis (at individual gene level) with well established tools like DESeq2/DESeq, edgeR, Limma and Cufflinks and input the results into GAGE/Pathview workflow for pathway analysis and visualization.

The reason the native GAGE/Pathview workflow is more sensitive is due to the special design of GAGE method. For two-state comparison, it does pair-wise comparison between experiments vs controls (disease vs normal etc), i.e. conducts gene set or pathway test between each sample pairs and then summarizes the results. The more samples (hence independent experiment-control sample pairs) you have, the more testing power you get this way. In the joint workflow, we can’t really decompose the two groups into experiment-control pairs, and we can only take the log2 fold changes from upstream analysis, which is a summary or average fold change without sample size or experiment design info. Therefore, gene set test on this is single column data is not as powerful as the GAGE default pair-wise comparison scheme. In addition, you can analyze and visualize pathway changes in every single experiment or sample (Figure 2 in vignette).
To see the difference between the two types of workflows, you can try the native workflow out and compare the results to joint workflows. You may also read the GAGE paper for more details, at http://www.biomedcentral.com/1471-2105/10/161.

[sindrle]

Ah, well thanks alot! I will redo my analysis after I have read some more about it.