Confounder-robust causal discovery and inference in Perturb-seq using proxy and instrumental variables

TL;DR

This paper introduces a new method for causal discovery in Perturb-seq data that effectively accounts for unobserved confounders, improving the accuracy of gene network inference using proxy and instrumental variables.

Contribution

The paper presents a novel confounder-robust causal discovery framework tailored for Perturb-seq data, leveraging proxy and instrumental variables to unbiasedly infer gene causal networks.

Findings

Outperforms baseline methods ignoring confounders

More accurate recovery of true gene causal DAGs

Demonstrated effectiveness on K562 CRISPR data

Abstract

Emerging single-cell technologies that integrate CRISPR-based genetic perturbations with single-cell RNA sequencing, such as Perturb-seq, have substantially advanced our understanding of gene regulation and causal influence of genes. While Perturb-seq data provide valuable causal insights into gene-gene interactions, statistical concerns remain regarding unobserved confounders that may bias inference. These latent factors may arise not only from intrinsic molecular features of regulatory elements encoded in Perturb-seq experiments, but also from unobserved genes arising from cost-constrained experimental designs. Although methods for analyzing large-scale Perturb-seq data are rapidly maturing, approaches that explicitly account for such unobserved confounders in learning the causal gene networks are still lacking. Here, we propose a novel method to recover causal gene networks from Perturb-seq experiments with robustness to arbitrarily omitted confounders. Our framework leverages proxy and instrumental variable strategies to exploit the rich information embedded in perturbations, enabling unbiased estimation of the underlying directed acyclic graph (DAG) of gene expressions. Simulation studies and analyses of CRISPR interference experiments of K562 cells demonstrate that our method outperforms baseline approaches that ignore unmeasured confounding, yielding more accurate and biologically relevant recovery of the true gene causal DAGs.