Authors
Peijun Wu, Xiang Zhou
Published in
Proceedings of the National Academy of Sciences of the United States of America. Volume 123. Issue 33. Pages e2525359123. Aug 18, 2026. Epub Aug 12, 2026.
Abstract
A critical analytical task in sc-CRISPR screening is identifying downstream genes influenced by perturbed target genes. Existing methods for this task primarily rely on traditional association-based analyses, which not only fall short in establishing causal relationships between genes but also suffer from high false positive rates and limited statistical power. To overcome these limitations, we introduce a causal inference-based framework that leverages the perturbation status of gRNAs in single cells as instrumental variables (IVs) to infer causal gene relationship via IV analysis. Building upon this framework, we further present Canon, a one-sample IV analysis method specifically tailored to systematically identify genes that are potentially causally influenced by perturbed target genes across diverse sc-CRISPR platforms. Canon ensures robust type I error control while maintaining high statistical power. We evaluated its performance through comprehensive simulations and real data applications. The gene-gene relationships identified by Canon provide valuable insights into the causal gene regulatory network, uncovering candidate therapeutic targets with potential relevance for cancer biology and demonstrating the transformative potential of sc-CRISPR screening to resolve causal regulatory networks at an unprecedented scale.
PMID:
42584997
Bibliographic data and abstract were imported from PubMed on 13 Aug 2026.
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