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  • RNASeq- Which method appropriate for Normalization?

    Hi,
    This question is to the people who have worked with RRBS methylation data and RNASeq data. I have RRBS methylation data for cell lines and the RNASeq data for the same cell lines. For RNASeq data I have used cuffdiff program for differential expression analysis but I am not quiet happy with it as it does not give per sample fpkm or read count information so that I can compare the expression in groups with the expression of each sample in those groups to validate the results of differential expression analysis. I would like to use other method of differential expression but I have a question that since I will be integrating the data with RRBS, should I consider only the method which generates the read counts in fpkm units(like cufflinks-cuffdiff)? I am thinking this because fpkm normalizes the reads by transcript length which other methods do not.

    My second question is that is there a standard method to integrate RRBS methylation data and RNASeq paired end data?

    Any suggestions will be greatly appreciated. Thanks.

  • #2
    Why would the length matter? You should be comparing the same gene between conditions (then the length doesn't matter). Comparing different genes in the same condition (where a length correction would be necessary) doesn't make sense anyways (different kinetics of the proteins, different regulation/repression, different protein half life, etc.).

    For a read on normalization methods, this paper http://www.ncbi.nlm.nih.gov/pubmed/22988256 is the one to go to.
    Use DESeq2 or EdgeR (as short conclusion).

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    • #3
      Originally posted by bastianwur View Post
      Use DESeq2 or EdgeR (as short conclusion).
      Totally agree. Using FPKM or RPKM in this case can be done because cufflinks outputs it automatically in the isoforms.fpkm_tracking file so you don't bother using other units.
      What you need is to do a between lane normalization. This because different experiments can have different sequencing depths and statistical properties, due to the fact that you don't get the results in the same run. Thus, I would recommend a full quantile normalization or a global scaling. This will ensure you that the two lanes in the end have the same max value, mean and variance. Thus, they will be comparable.

      EDIT:
      I used EdgeR with cufflinks output and full quantile normalization, you can do that with many Bioconductor packages in R. I did it with EDASeq.
      Last edited by Macspider; 07-20-2016, 07:53 AM.

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