I have some questions regarding RNA seq analysis if you can suggest anything it will help me a lot.

  1. I am currently normalizing RNA seq data for comparing genes expression within and between samples. Which normalization method would you recommend for this type of analysis? FPKM, TPM, TMM? Also I want to make a heatmap to see genes expressed in different conditions. Do you think transforming normalized data (like log2, z-score) is a good idea for this?
  2. Also I want to build a co expression network, I am just wondering if normalization like FPKM, TPM, TMM has any influence on building a coexpression network?
  3. Another thing I want to do is use Pearson correlation but I am confused if it will only find linear relationships among the normally distributed data. But normalization methods do not assume that counts to be normally distributed. So, Is it a bad idea to find pairwise coexpression of genes using pearson correlation? If so, which method would you recommend is reliable for building coexpression networks?

Please help me with this, I will be very grateful to you

Thanks in advance

  • 1
    $\begingroup$ Hi Citu, i suggest you to have a look at this article: nature.com/articles/s41598-018-29077-3. You may find anwsers to your questions! $\endgroup$ Jun 9 '20 at 8:11
  • $\begingroup$ Thanks, I will look at it $\endgroup$
    – Citu
    Jun 9 '20 at 8:56
  • $\begingroup$ To put it in a nutshell, you may rely on TPM without normalization. For the correlation measurement, I recommend the use of a ranked version of the Pearson coefficient (either Mutual Ranking or Highest Reciprocal Ranking, where HRR(A,B)=max(rank(cor(A,B)), rank(cor(B,A)) $\endgroup$ Jun 9 '20 at 9:02
  • $\begingroup$ There is definitely a lack of biological information needed to make the call on a transformation IMO. Anyway you MUST transform/normalise your data before using Pearsons correlation statistics and if correctly done you can then use this for co-expression with confidence. Q-Q plots and residual analysis help in correct normalisation $\endgroup$
    – M__
    Jun 9 '20 at 15:09
  • $\begingroup$ Alternatively, use the Spearman rho coefficient. It is indeed less sensitive to outliers. $\endgroup$ Jun 10 '20 at 5:26

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