TPM (trancripts per million) RNA-Seq analysis

Question

[Spacebio]

I have a .csv file containing RNA-Seq data already normalysed (to TPM). I would like to do a differential expression analysis, I read the DESeq, DESeq2, edgeR and Limma vignettes but I don't know where to start. Any suggestions? The snapshot of my data is like this:

Gene EntrezID APC_1 APC_2 
Apoa1 11806 14668.15 2875.06 
Mup3 17842 9992.58 1697.63 
Serpina3k 20714 8031.3 2849.67

[Santosh Anand]

See Starting from count matrices at https://www.bioconductor.org/help/workflows/rnaseqGene/

Normally, it is better to start with unnormalized data, but you can still follow the pipeline with the TPM normalization.

[Spacebio]

Thank you very very much!

Answer 1

[grant.hovhannisyan]

Software such as Deseq2 or edgeR require unnormalised raw counts of reads (obtained for example, by htseq-count, featurecounts or directly by STAR mapper), because they do their own normalizations internally. But, there are workarounds. As mentioned by @Santosh, you can follow one the step-by-step tutorials on how to perform differential gene expression analysis.