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 that prioritizes disease genes by adjusting confounders without prior knowledge of control variables. We demonstrate that our method substantially improves statistical power in simulations and real-world data analysis of 70k brain cells collected for dissecting Alzheimers disease. We identified that 215 causal genes are differentially regulated by the disease 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.