Modeling Heterogenous Outcomes in Functional Genetic Screens Using Single-cell Genomics as Readouts This supplementary request seeks to advance the analysis of single-cell CRISPR screening datasets by developing novel computational models to address the challenge of heterogeneous transcriptome responses upon perturbing the same gene. It falls within the scope of the parent award to develop algorithms for functional genetic screens, but proposes to follow up on unanticipated results (single-cell CRISPR screen) and enhance this direction, which is unexpectedly productive, as is demonstrated in preliminary results. Specifically, we aim to: (1) build a model to detect heterogeneous expression patterns in Perturb-seq assays, l, and (2) extend the model to other dataset types, including pooled single-cell and bulk transcriptomics datasets from various gene perturbations. This work will significantly enhance our understanding of genotype-phenotype relationships at the single-cell level, with broad applications in cancer research, virology, and gene regulation.

Project Narrative This supplementary request aims to develop computational models for single-cell CRISPR screening analysis, focusing on addressing heterogeneous transcriptome responses. We will build, benchmark, and extend models for Perturb-seq assays and other dataset types, enhancing our understanding of genotype-phenotype relationships at the single-cell level.