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. Author manuscript; available in PMC: 2025 Feb 1.
Published in final edited form as: Trends Genet. 2023 Nov 20;40(2):118–133. doi: 10.1016/j.tig.2023.10.012

Figure 4. Analyzing single-cell pooled perturbation screens.

Figure 4.

Single-cell perturbations (e.g. guide RNA [gRNA] or barcoded overexpression libraries) can be represented with a n x m matrix, whereby n is the number of single cells and m is the number of perturbations, and the matrix is populated with the number of UMIs per perturbation per cell. After defining UMI thresholds and assigning perturbations to cells for follow-up analyses, changes in different modalities of interest (e.g., open chromatin or gene expression changes) are measured. First, non-targeting (negative) controls should be inspected for inflation of observed test statistics. This can be performed by comparing observed significance p-values with p-values that would be expected by random chance given the number of tests performed and examining results for systematic deviations from the null expectation. Upon verifying that there is no systematic inflation, targeting gRNAs are examined for their effects on measured molecular phenotypes (e.g. gene expression changes in cis and trans or open chromatin).