GAGE package statistical tests
0
0
Entering edit mode
smt8n • 0
@smt8n-9982
Last seen 4.8 years ago

Dear all,

 

I am trying to learn the GAGE gene-set enrichment package and apply it to the RNA-Seq data I have. I follow the RNA-Seq workflow: http://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf, section 7.1, workflow with DESeq2.

 

What perplexes me is the big difference in results depending on the statistical test I choose. When I used the default (t-test), I got 5 significant pathways, with the top q-value of the order 10^-3. Although the original paper (Luo et al, 2009) claims, refering to Kim/Volsky 2005 paper, that for gene sets of 10 genes and more the assumption of normality is fine, I decided to double-check that with Kolmogorov-Smirnov and got 14 significant pathways with the top q-value of the order 10^-8.

 

The difference did not seem as minor to me as I would expect. I also tried rank.test=TRUE and got the result much closer to the default case (7 significant pathways, 10^-4 top q-value). This option supposedly takes care of possible not-normality of the distribution assumed in the t-test, but I am not sure whether the other t-test assumption, "fold changes of genes are independent and identically distributed", is taken care of.

 

As I said, the difference between the rank test and the default is not that big and, not having run K-S, I could possibly be satisfied. Now I frankly do not know what to think of the results. Could anybody, please, share any suggestions on how to approach the situation.

 

Thank you

Slava

t-test GAGE package Kolmogorov-Smirnov test Gene-Set enrichment • 555 views
ADD COMMENT

Login before adding your answer.

Traffic: 285 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6