Statistical analysis of Spectral count data : edgeR, limma or others ?
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Last seen 16 months ago

Hi BioC community,

I have spectral count data (proteomics) to analyze. I have two groups (A ad B) to compare with 8 patients by group. Each sample was processed in duplicate, leading to 16 distinct patients in the study and 32 samples (16 by group).

As for the method to use to detect differentially expressed proteins between the two groups, I read a lot of things in the support site and in publications, in particular :

- Use the classical edgeR with TMM normalization or DEseq (Anders et al, nature protocol, 2013)

- Use edgeR with total spectral count normalisation (like in the msms package)

- Use limma on the log2 (count+1) with quantile normalization and eBayes(trend=TRUE, robust=TRUE) : voom for spectral counts

- Use edgeR and its quasi negative binomial approach : voom for spectral counts

- PLGEM package

- ...etc

As my data are count data, I wanted to use edgeR or DEseq package. But with the post of Gordon Smyth, 5 months ago ( voom for spectral counts ), I wanted to switch to limma with quantile normalization but I have no reference that I can cite.

Did you learn more about comparing the different approach on spectral count data ? Do you have recommendations to give ? Is there an article that compare the different methods and that I can cite please ?

Thanks a lot for your help,


spectral counts proteomics limma edger deseq • 2.6k views
Entering edit mode
Last seen 41 minutes ago
WEHI, Melbourne, Australia

I don't have anything to add to my earlier post. As far as a reference, you could cite

for limma or

for quasi-likelihood but we haven't published anything specifically about proteomics spectral data yet. It's too early for comparison papers I think.

Entering edit mode

Thanks a lot for your answer and the references,



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