#deseq2 — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #deseq2, aggregated by home.social.
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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 📊.
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Have you ever used the inmoose package? https://inmoose.readthedocs.io/en/latest/ It has an implementation of #DESeq2 in #Python, but I am wondering about the difference compared to PyDESeq2 https://pydeseq2.readthedocs.io/en/latest/index.html. The latter is published in #Bioinformatics, but I have never seen a publication related to inmoose. The cool thing is that inmoose seems to generate the same type of graphics as the R package.
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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 😍
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@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.
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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. 😅