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

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  1. Department of Statistics
  2. CoCoA-diff: counterfactual inference for single-cell gene expression analysis

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

TitleCoCoA-diff: counterfactual inference for single-cell gene expression analysis
Publication TypeJournal Article
Year of Publication2021
AuthorsPark, YP, Kellis, M
JournalGenome Biol.
Volume22
Pagination1–23
AbstractFinding 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.