Want to learn how transformers and large-language models can help to organize&understand single cell profiles?
Led by @arturszalata, we review current such approaches in single cell genomics and give perspectives towards foundation models etc. nature.com/articles/s4159…
Very helpful! But consider colorblind-friendly color palettes 🎨
ggpubfigs only supports colorblind-friendly color palettes, so it is something users don't even need to think about :D
github.com/JLSteenwyk/ggp…x.com/tangming2005/s…
Very helpful! But consider colorblind-friendly color palettes 🎨
ggpubfigs only supports colorblind-friendly color palettes, so it is something users don't even need to think about :D
github.com/JLSteenwyk/ggp…x.com/tangming2005/s… https://t.co/LRMDbTCyTG
This simple pytorch trick will cut in half your GPU memory use / double your batch size (for real). Instead of adding losses and then computing backward, it's better to compute the backward on each loss (which frees the computational graph). Results will be exactly identical
Excited to share two preprints in my lab on computational lineage tracing (LT) of single cells.
Preprint #1 - Benchmarking of mtDNA lineage tracing: biorxiv.org/content/10.110…
Preprint #2 - A phylodynamics model framework (scPhylox) with single-cell LT data: biorxiv.org/content/10.110…
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