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
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Checksum (MD5)
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Author(s)
Park, Yongjin P.
Kellis, Manolis
Date Issued
August 2021
Journal
Genome Biology
Publisher
Springer Science and Business Media LLC
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s13059-021-02438-4
