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Lin
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@lin-19103
Last seen 4.2 years ago
Hi all,
it is my first time to work with RNA-seq data, and with this data a differential expression analysis and co-expression network analysis should be done. Now I read in the pipeline [RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR] for differential expression analysis that at first the counts are filtered (CPM), and then normlization is done with TMM. However, for the co-expression analysis I would like to use normalized data but another filtering method. So my question is: Can I also apply the TMM normalization method to unfiltered data, and then filter the normalized data afterwards? Or do you see any problem with this/have other suggestions?
Thanks in advance!
You should take a look at point 4 in the WGCNA FAQ: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/faq.html
Hi Kevin, thanks for your answer! But my question was rather if there is a problem using TMM normalization FIRST (with unfiltered data), and filter the data afterwards (because I want to use another pipeline where the whole filtering is implemented).
I see. Gordon has already answered. Based on your logic, you have CPM counts, and then you apply TMM to those?
Yes, exactly, that would be what I thought of... Because I need normalized data, but would like to use another filtering procedure during the co-expression analysis... And with this filter I would loose too many transcripts before (and would double-filter).
Typically, we filter the raw counts, then normalise, and then make statistical inferences on the normalised counts. After that, we may apply a further transformation on the normalised counts for the purposes of conducting downstream analyses.