Question: DESeq2 - GAGE workflow
gravatar for sup230
2.2 years ago by
sup23030 wrote:

I have some confusion about GAGE workflow. I understand GAGE is a type of functional class scoring tools with no preset cutoff used to identify significant genes. But I have seen several workflow/model scripts where they used the output from DESeq2 which is selected based on p-adjusted value of 0.05. Shouldn't the experimental set be the entire expression data? I guess in brief, I am confused about exactly which two groups are compared in order to extract pathways that are considered disturbed with statistical significance. If I choose to use a subset of genes that are selected as significant as a result of DESeq2 analyses and run GAGE with gsets=kegg.sigmet, what is the comparison made in this case? 

kegg_human<-kegg.gsets(species = "hsa", id.type = "kegg")

Also, what is the key difference in algorithm behind between GSEA and GAGE? I read in papers that GSEA uses Kolmogorov-Smirnov statistics and GAGE uses Wilcoxon Mann-Whitney test. I guess these are both non-parametric ranking tests, but is the difference that GAGE uses two sample t-test based on the ranking while GSEA tests whether the shape of cumulative functions are different? 

One last question for the result of GAGE, if I just look at the result without specifying greater or less, the output table shows both greater and less columns but the q-values listed do not match to those when I got separate lists for up/down regulated gene sets. Why is this? 

Thank you for your help!

deseq2 gage package • 790 views
ADD COMMENTlink written 2.2 years ago by sup23030
Please log in to add an answer.


Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 324 users visited in the last hour