RNA-Seq analysis with Python

Question

[biology_inform]

I will start working in a lab and I will do RNA-Seq data analysis in the lab and I know Python and a little bit (very little) R. Is there any tutorial, video, book, online course or etc to give me an introductory perspective about RNA-Seq analysis in Python? Or is it impossible to do RNA-Seq analysis in Python? :(

Thanks in advance

Answer 1

[rpolicastro]

Some of the best tools for RNA-seq analysis are command line programs and R libraries. For alignment there are the command line programs STAR, salmon, or kallisto. For differential expression the most popular software are the R libraries edgeR or DESeq2. Software for QC and intermediate steps come in all flavors including command line, R, and python. Because of the above considerations it would be difficult to do everything contained within a single language or framework.

Bioconductor has a great vignette on RNA-seq you can follow to learn more.

[biology_inform]

Thank you so I need to start improving myself in R.

[Dunois]

You might also find this post helpful on the way. (And probably this post too.)

Answer 2

[Starlitnightly]

OmicVerse is the fundamental package for multi omics included bulk and single cell analysis with Python. For more information, please read our paper: OmicVerse: A single pipeline for exploring the entire transcriptome universe

The original name of the omicverse was Pyomic, but we wanted to address a whole universe of transcriptomics, so we changed the name to OmicVerse, it aimed to solve all task in RNA-seq.

You can find our tutorials from readthedocs.

Answer 3

[Zhenyu Zhang]

Since you say you know a little bit R, that's enough to ask ChatGPT to give you analysis code for your purpose.

[seidel]

You don't even need ChatGPT. Just take the hit and extend your R knowledge. There are excellent, easy to follow tutorials for edgeR and DESeq2 in R.

Answer 4

[Radu Tanasa]

Actually, you can do a lot in Python for RNA-seq analysis, simply by leveraging the best data format of all times, AnnData. Although it was built for single cell, you can adapt it to RNA-seq by loading 1 sample per row, as opposed to 1 cell. Then, you can go ahead and run normalization (TMM with conorm for example), log1p transform, scaling, PCA, UMAP. For DGE you can use rank_genes_groups if you want to use Wilcoxon rank sum test (good if you have at least 8 replicates per condition), or PyDESeq2.