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Differential Expression gene In Rna-Seq Experiment using mt.maxT
Entering edit mode
13 months ago
oghzzang • 40

Hi everyone.

I performed RNA-seq experiment on two independent groups for Group1 and Group2. number of Group1 samples is 15 and number of Group2 samples is 4. and number of genes is 20000.

And I am doing rna-seq data analysis using mt.maxT function of multtest R package (Permutation 5000).

this is summary data


Min. 1st Qu. Median Mean 3rd Qu. Max.

1 5126 10251 10251 15376 20501


Min. 1st Qu. Median Mean 3rd Qu. Max. NA's

-5.5036 -1.1150 -0.0513 -0.0511 0.9765 8.0654 878


Min. 1st Qu. Median Mean 3rd Qu. Max. NA's

0.0004 0.1404 0.3884 0.4334 0.6998 1.0000 878


Min. 1st Qu. Median Mean 3rd Qu. Max. NA's

0.0580 1.0000 1.0000 0.9992 1.0000 1.0000 878


Min. 1st Qu. Median Mean 3rd Qu. Max. NA's

0.2102 0.5610 0.7761 0.7387 0.9330 1.0000 878

my smallest fdr p-value among 20000 genes is 0.21 and unadjusted p-value among 20000 genes is 0.0004.

In this situation, can I set cutoff using raw p value (unadjusted p value)?

rna-seq • 420 views
Entering edit mode

The most common practice is to apply the Benjamini-Hochberg method to adjust the p-values for multiple testing, after differential analysis with edgeR / DESeq2 / limma-voom. Do you have a compelling reason to use a different correction procedure? And are you using the standard tools to perform the differential gene expression analysis?

Entering edit mode

Thanks h.mon.

I can't use other tools (edgeR or DESeq2). Because My data is quartile normalized by TCGA pipeline (no TCGA data). Additionally, I have only rsem quartile normalized data. :(


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