Pipeline release! nf-core/differentialabundance v2.0.0 - v2.0.0 - 2026-06-23!
Differential abundance analysis for feature/ observation matrices from platforms such as RNA-seq
Please see the changelog: https://github.com/nf-core/differentialabundance/releases/tag/2.0.0
#atacseq #chipseq #deseq2 #differentialabundance #differentialexpression #gsea #limma #microarray #rnaseq #shiny #nfcore #openscience #nextflow #bioinformatics
Learned RNA-seq workflow using C. diff data from a study 🧬. Processed raw reads thru fastp → kallisto → DESeq2 pipeline. Results matched the original paper’s findings, with clear differential expression between mucus and control conditions 📊.
Learned RNA-seq workflow using C. difficile data from a published study 🧬. Processed raw reads through fastp → kallisto → DESeq2 pipeline. Results matched the original paper's findings, with clear differential expression between mucus and control conditions 📊.
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 😍
#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. 😅
Differential expression analysis of single-cell transcriptomics allows scientists to dissect cell-type-specific responses to biological perturbations. Here, the authors show that many commonly used methods are biased and can produce false discoveries.
nf-core
Ken Koon Wong
victorjavierlo
Luca Visentin
Sioly Becerra
please 🥹
TanyaAneichyk
António Domingues
Arjan Boltjes