A-T.28: Joint inference of guide assignment and perturbation effects in heterogeneous single-cell CRISPR screens
Pooled single-cell CRISPR screens have become an important resource in computational biology for linking the effect of genetic perturbations to molecular phenotypes and thereby advancing our understanding of gene regulation. Since many perturbations are profiled simultaneously in such pooled screens, a key challenge is the assignment of individual cells to the perturbation they received based on the guide RNAs (gRNAs) detected in each cell. Despite its central role, this step lacks systematic benchmarks and accessible tools for comparing assignment strategies. To address this gap, we developed crispat, a Python package that helps researchers evaluate and select appropriate gRNA-cell assignment methods. Applying crispat across multiple published datasets revealed substantial differences across methods in both the number and quality of assigned cells highlighting that assignment choice can substantially influence downstream analyses. Moreover, no single method consistently performed best across data sets or metrics. Building on these insights, we introduce crispero, a probabilistic factor model, that avoids committing to a fixed assignment altogether. Our model jointly integrates gRNA counts with molecular readouts to infer perturbation effects while estimating the probability that each cell received a functional perturbation, thereby capturing assignment uncertainty and identifying non-responding cells. crispero further separates general sources of variation - such as cell state, cell cycle, and batch - from perturbation-specific effects that capture gene modules co-regulated by targeted genes. Finally, interaction terms enable detection of perturbation responses restricted to specific cellular subpopulations and help dissect heterogeneous responses in complex tissues.
Co-authors: Britta Velten
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