PMID- 34404460 OWN - NLM STAT- MEDLINE VI - 22 IP - 1 TI - CoCoA-diff: counterfactual inference for single-cell gene expression analysis. PG - 228 CI - © 2021. The Author(s). LA - eng PT - Journal Article PT - Research Support, N.I.H., Extramural PT - Research Support, Non-U.S. Gov't PL - England TA - Genome Biol JT - Genome biology JID - 100960660 IS - 1474-760X (Electronic) LID - 10.1186/s13059-021-02438-4 [doi] FAU - Park, Yongjin P AU - Park YP AUID- ORCID: 0000-0001-8915-2876 AD - Department of Pathology and Laboratory Medicine, Department of Statistics, University of British Columbia, Vancouver, BC, Canada. ypp@stat.ubc.ca. AD - Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada. ypp@stat.ubc.ca. FAU - Kellis, Manolis AU - Kellis M AD - Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. AD - Broad Institute of MIT and Harvard, Cambridge, MA, USA. IS - 1474-7596 (Linking) SB - IM MH - Alzheimer Disease/genetics MH - Brain MH - Causality MH - *Gene Expression MH - *Genetic Techniques MH - Genomic Medicine MH - Humans MH - Models, Statistical MH - RNA-Seq MH - *Single-Cell Analysis MH - Transcriptome OTO - NOTNLM OT - Alzheimer’s disease OT - Causal inference OT - Counterfactual inference OT - Single-cell RNA-seq PMC - PMC8369635 DCOM- 20220119 LR - 20221203 DP - 20210817 DEP - 20210817 AB - 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.