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.
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][1]).

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`).

  [1]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5418186/