Super excited to get this out. This collab started a few years ago and is the first paper from it. Here, with experimental and computational approaches we:

1. establish that cell villages can be just as accurate (one might argue more accurate!) than arrayed-based designs

bsky.app/profile/bior...

How do we decouple the effects of two functional phenotypes in protein deep mutational scanning (DMS)?
Meet Cosmos, our new statistical framework for causal inference in multi-phenotype DMS.
www.biorxiv.org/content/10.1...
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Statistical assessment of score uncertainty is also becoming especially critical as DMS experiments scale to more conditions, phenotypes, and proteins. We hope Lilace will help address these challenges and enable more reliable functional interpretation of FACS-based DMS data! (3/3)

Coupling DMS experiments with FACS enables simultaneous measurement of a quantitative phenotype (e.g. protein abundance) across thousands of mutations in a protein. However, modeling FACS as a phenotype can be challenging due to its unique multidimensional nature and experimental noise. (2/3)

Check out our new preprint on Lilace, a statistical tool for scoring FACS-based deep mutational scanning experiments! Lilace directly models the shift between variant fluorescence distributions and provides score uncertainty estimates to better assess reliability and reproducibility. (1/3)