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  1. ; ; ; (, Genome Biology)
    Abstract Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method — SCEPTRE low-MOI — that resolves these analysis challenges and demonstrates improved calibration and power. 
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  2. ; ; ; ; ; ; ; ; ; ; et al (, Nature Biotechnology)
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  3. ; ; ; (, The Annals of Statistics)
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