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gene expression variance equality

Hi,

I am analyzing RNA seq data and trying to identify differentially expressed genes between to populations using ttest (my data is in triplicates)

I was wondering weather I need to check for the variance assumptions before applying ttest.

if so- what test will be recommended to use on such data?

I have tried Bartlett's, Levene's (with absolute values), Levene's (with quadratic values), Brown-Forsythe and two-sample F-test

and got different though close results. I would live to hear your thoughts! Tnx!

rna-seq gene expression variance test
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updated by e.rempel e.rempel • written by galozs • galozs

1 answer

Hi,

citation from this thread (answer from Gordon Smyth - author of limma):

Unequal variances are not usually a major concern in microarray data. The two-sample t-test is known to be highly robust against unequal variances, and the limma moderated t-test inherits this property.

Thus, I would recommend that you run limma (or DESeq2 or EdgeR) for your data.

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written by e.rempel e.rempel

I am looking at exactly 2 conditions and prefer working with ttest and my own codes.

Thanks!

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written by galozs • galozs

May I ask why? limma and other tools inherit the "good" properties of t-Test and have some additional advantages. Personally, I would use t-Test in case of (real) many replicates and a few genes.

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written by e.rempel e.rempel

honestly, I used cuffdiff before and realized that using my own code and analysis gives more reliable results. I didn't try the other tools you mentioned, yet. maybe I'll give it a try in the future.

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written by galozs • galozs

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