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Cosmos: A Position-Resolution Causal Model for Direct and Indirect Effects in Protein Functions Multi-phenotype deep mutational scanning (DMS) experiments provide a powerful means to dissect how protein variants affect different layers of molecular function, such as abundance, surface expression...

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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04.08.2025 15:08 — 👍 8    🔁 1    💬 1    📌 1

figure 1 of Lilace paper detailing the data collection and analysis process to indicate where the Lilace model fits in

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)

30.06.2025 16:03 — 👍 3    🔁 0    💬 0    📌 0

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)

30.06.2025 16:03 — 👍 3    🔁 0    💬 1    📌 0

Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace Deep mutational scanning (DMS) experiments interrogate the effect of genetic variants on protein function, often using fluorescence-activated cell sorting (FACS) to quantitatively measure molecular ph...

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)

30.06.2025 16:03 — 👍 17    🔁 5    💬 1    📌 3