How to use TPM from RNA seq data analysis for differential gene expression analysis? which statistical methods are reuired to be performed.

Question

[ASid]

i am a newbie in RNA seq data analysis. I want to check a gene as DEG in a dataset of RNA-chip seq experiment. The data provided is in the form of a single column for each treatment type and lists the expression level of each gene normalized to transcripts per million (TPM).i need help on using what type of analysis using R to find out the DEGs.

The snapshot of data is like this.

Gene EntrezID Normal_TPM Diabetes_TPM
Apoa1 11806 14668.15 2875.06
Mup3 17842 9992.58 1697.63
Serpina3k 20714 8031.3 2849.67
Alb 11657 6801.26 6912.08
Cyp2e1 13106 6580.8 7816.79

Thanks in advance

Answer 1

[Govardhan Anande]

To analyse differential expression analysis of genes in R, you can use DESeq, DESeq2 or edgeR.

Formula for TPM is here, so if you can get total reads aligned for each sample then you can find out aligned reads freq, which you can use as input for above programs and can perform differential expression analysis.

According to your snapshot, it looks like your data is already analysed for differential expression.

Correct me if I am wrong.

Thanks

[cpcantalapiedra]

edgeR works with raw counts, so maybe EBseq could be a better advice for TPMs?

http://deweylab.biostat.wisc.edu/rsem/README.html

[michael.ante]

Hi Govardhan, I think you're mixing up CPM (counts per million) with TPM (transcripts per million). See here how it's computed.

[Govardhan Anande]

yeah, so you can get TPM formula here then.