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CoCoA-diff: counterfactual inference for single-cell gene expression analysis
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13059_2021_Article_2438.pdf
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3.03 MB
Format
Adobe PDF
Checksum (MD5)
6abb2414239a79c0384d11dde2ea8e54
Author(s)
Park, Yongjin P.
Kellis, Manolis
Date Issued
August 17, 2021
Publisher
BioMed Central
Citation
Genome Biology. 2021 Aug 17;22(1):228
Version
Final published version
Abstract
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.
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/s13059-021-02438-4
