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CoCoA-diff: counterfactual inference for single-cell gene expression analysis.

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.

SUBMITTER: Park YP

PROVIDER: S-EPMC8369635 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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CoCoA-diff: counterfactual inference for single-cell gene expression analysis.

Park Yongjin P YP Kellis Manolis M

Genome biology 20210817 1

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 cel ...[more]

PMID: 34404460

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