I've written my first real #blog post! Horray!

We've recently had to run a differential expression analysis on samples with 2 different experimental variables, so we had to consider how to interpret such a model with #deseq2

Here are my ramblings: https://mrhedmad.github.io/blog/posts/on_2d_lm_deas/

Please fact-check me and leave feedback! I'd love it 😍

@bioconductor we would like to be able to continue using #DESeq2 from RStudio 4.2 :thisisfinefire: please 🥹

#pydeseq2 - a python version of #deseq2 was published. Authors of #deseq2 pointed out they were not part of pydeseq2 publication, and pointed that they find it inappropriate (intellectually and practically).

What do you think, #bioinformatics community?

I personally think it's within the spirit of open science. Deseq2 authors are cited in the 1st paragraph, the package is reimplemented in python and has a chance to evolve to be even better. It's not about the credits, it's about the progress.

@haojiawu @biorxivpreprint this is very interesting. I have been telling people that one of the main reasons for using #RStats instead of #python for gene expression analysis, is the lack of methods such as #DESeq2 or #limma as python libraries. This might tip the balance for a lot of people.

Of course there are many reasons to prefer R, the excellent #Bioconductor ecosystem one of them, and in fairness, for #scRNA analysis python has very strong ecosystem and community.

Wow, this paper on #scRNAseq and differential expression methods is an eye opener. https://www.nature.com/articles/s41467-021-25960-2

Nice overview of methods and relatively easy to understand explanation of what is wrong with certain, especially single-cell-specific, methods.

Of great help in my own scRNA-seq efforts. Should probably have read this earlier. Now, back to R I go. 😅

#statistics #rnaseq #limma #edgeR #DESeq2