Causal differential expression analysis under unmeasured confounders with causarray

Abstract

Advances in single-cell sequencing and CRISPR technologies have enabled detailed case-control comparisons and experimental perturbations at single-cell resolution. However, uncovering causal relationships in observational genomic data remains challenging due to selection bias and inadequate adjustment for unmeasured confounders, particularly in heterogeneous datasets. To address these challenges, we introduce causarray, a robust causal inference framework for analyzing array-based genomic data at both pseudo-bulk and single-cell levels under unmeasured confounding. causarray integrates a generalized confounder adjustment method to account for unmeasured confounders and employs semiparametric inference with flexible machine learning techniques to ensure robust statistical estimation of treatment effects. Benchmarking results show that causarray robustly separates treatment effects from confounders while preserving biological signals across diverse settings. We also apply causarray to two single-cell genomic studies: (1) an in vivo Perturb-seq study of autism risk genes in developing mouse brains and (2) a case-control study of Alzheimers disease using three human brain transcriptomic datasets. In these applications, causarray identifies clustered causal effects of multiple autism risk genes and consistent causally affected genes across Alzheimers disease datasets, uncovering biologically relevant pathways directly linked to neuronal development and synaptic functions that are critical for understanding disease pathology.