CoCoA-diff: counterfactual inference for single-cell gene expression analysis
Author(s)Park, Yongjin P.; Kellis, Manolis
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Abstract Finding a causal gene is a fundamental problem in genomic medicine. We present a causal inference framework, CoCoA-diff, that prioritizes disease genes by adjusting confounders without prior knowledge of control variables in single-cell RNA-seq data. We demonstrate that our method substantially improves statistical power in simulations and real-world data analysis of 70k brain cells collected for dissecting Alzheimer’s disease. We identify 215 differentially regulated causal genes in various cell types, including highly relevant genes with a proper cell type context. Genes found in different types enrich distinctive pathways, implicating the importance of cell types in understanding multifaceted disease mechanisms.
Springer Science and Business Media LLC
Genome Biology. 2021 Aug 17;22(1):228
Final published version