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  • Normalization for DE of RNAseq data

    I am using CLC workstation to perform differential expression analysis of RNA seq reads (two sample comparison). I have genrated RPKM values and log2 transformed those values. Can I compare those values between samples for foldchange of expression values (RPKM)? or other normalizations (scaling/quantile etc) would be required? if so which one is best?

    Many Thanx

  • #2
    Look at this thread (which happens to be in the list of 'Similar threads' above):

    Discussion of next-gen sequencing related bioinformatics: resources, algorithms, open source efforts, etc


    In particular, Simon Anders has a number of insightful comments about normalisation (in that thread, and in other threads):
    I don't know the Genomic Workbench, but your post illustrates precisely the issues I have with these software suites. They give you easy access to many different methods published in the literature and give the you the illusion that you could perform a sound analysis without having to read all the papers describing, discussing and comparing these methods.
    This thread is also useful:

    Discussion of next-gen sequencing related bioinformatics: resources, algorithms, open source efforts, etc


    In short, asking "which one is best" suggests a need to do more reading. If there were one method that worked all the time, then there would only be one recommendation, and most software (probably including CLC) would use it as the default processing method.
    Last edited by gringer; 02-20-2012, 11:50 PM.

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