Hi, I'm new to both proteomics and biostar and would like to start with maybe a simple, but a more general question.
I know that for RNA-Seq, people just assume normality to the data mostly, because to achieve normality with such a small number of samples is always risky and difficult. I was wondering how this is handled in the proteomics field. What kind of tests one might use to analyze the data, when a t-test is not applicable due to the non-normal behavior of the data?
What alternatives do I have there?
I saw that some people are using LIMMA. Can LIMMA handle non-normal distributed data?
thanks
G.
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written
by
gogeni5529 •
1 answer
When using limma in proteomics/metabolomics, the data are usually log-transformed first, which is usually makes it normal/more homoscedastic (or assumed to be so). (Example paper).
By the way, people do not usually assume normality for RNA-seq data. They usually model raw counts with negative binomials (e.g. DESeq2, edgeR) or do some other transformations (e.g. limma-voom).
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