Question: GAGE package statistical tests
0
gravatar for smt8n
2.8 years ago by
smt8n0
smt8n0 wrote:

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

ADD COMMENTlink written 2.8 years ago by smt8n0
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 151 users visited in the last hour