Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders

TL;DR

This paper introduces GISL, a novel method for gene regulatory network inference that accounts for selection bias and latent confounders by leveraging perturbation data to distinguish causal regulation from spurious dependencies.

Contribution

The paper presents GISL, a new algorithm that effectively separates true gene regulation from confounding and selection effects using perturbation data.

Findings

GISL accurately infers gene regulatory networks in synthetic data.

GISL outperforms existing methods on real-world gene expression datasets.

Selection bias significantly impacts causal inference in gene networks.

Abstract

Gene regulatory network inference (GRNI) aims to discover how genes causally regulate each other from gene expression data. It is well-known that statistical dependencies in observed data do not necessarily imply causation, as spurious dependencies may arise from latent confounders, such as non-coding RNAs. Numerous GRNI methods have thus been proposed to address this confounding issue. However, dependencies may also result from selection--only cells satisfying certain survival or inclusion criteria are observed--while these selection-induced spurious dependencies are frequently overlooked in gene expression data analyses. In this work, we show that such selection is ubiquitous and, when ignored or conflated with true regulations, can lead to flawed causal interpretation and misguided intervention recommendations. To address this challenge, a fundamental question arises: can we distinguish dependencies due to regulation, confounding, and crucially, selection? We show that gene perturbations offer a simple yet effective answer: selection-induced dependencies are symmetric under perturbation, while those from regulation or confounding are not. Building on this motivation, we propose GISL (Gene regulatory network Inference in the presence of Selection bias and Latent confounders), a principled algorithm that leverages perturbation data to uncover both true gene regulatory relations and non-regulatory mechanisms of selection and confounding up to the equivalence class. Experiments on synthetic and real-world gene expression data demonstrate the effectiveness of our method.