Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection

• Published in: Genome Biology Vol. 25; no. 1; pp. 124 - 30
• Main Authors: Barry, Timothy, Mason, Kaishu, Roeder, Kathryn, Katsevich, Eugene
• Format: Journal Article
• Language: English
• Published: England BioMed Central 17-05-2024; BMC
• Subjects: Calibration; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; CRISPR; CRISPR-Cas Systems; Datasets; Gene expression; Genes; Humans; Libraries; Method; Methods; Multiplicity of infection; Single-Cell Analysis - methods
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-024-03254-2

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.