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normalization RNASeq Data for QTL analysis

Hi all,

I am looking for the best way to normalize RNA-Seq data generated using the 3’prime technology of Lexogen. Due to this technology, I do not need to include gene length.

However, the data should follow a normal distribution, as I will proceed with linear modeling in matrixeQTL.

I've read quite a lot about RNASeq normalization, but I feel like there are many options and opinions and at the same time not so much info about my concrete situation. For example, here https://pubmed.ncbi.nlm.nih.gov/31861985/ TPM seems to perform best (but I do not need to account for gene length), while log10 was evaluated as not good regarding biological signal.

Starting from raw counts, I see the options now to start either with

  • DESeq / TMM / quantile

to normalize between samples.

But then I think I need further transformation to make the data "normal". For example

  • log, vst or inverse rank transformation

I would be thankful for any advice from you!

rna-seq r edger deseq matrixqtl
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updated by Biostar Biostar • written by Linda • Linda

I found some advice on matrixeQTL faq:

If you go to the "Outliers in expression. Quantile normalization and Kruskal-Wallis test." paragraph, they also suggest some R code for the normalization.

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written by Fabio Marroni Fabio Marroni

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