Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View

TL;DR

This paper introduces a causal graphical model to improve gene regulatory network inference from single-cell RNA sequencing data with dropouts, avoiding imputation and providing a principled framework that handles zeros effectively.

Contribution

The authors propose a causal dropout model that characterizes dropout mechanisms and enables accurate network inference without imputation, integrating with existing methods.

Findings

Conditional independence relations are preserved after test-wise deletion of zero-valued samples.

The causal dropout model can be validated from data and encompasses existing dropout models.

Empirical results show improved network inference accuracy on synthetic and real data.

Abstract

Gene regulatory network inference (GRNI) is a challenging problem, particularly owing to the presence of zeros in single-cell RNA sequencing data: some are biological zeros representing no gene expression, while some others are technical zeros arising from the sequencing procedure (aka dropouts), which may bias GRNI by distorting the joint distribution of the measured gene expressions. Existing approaches typically handle dropout error via imputation, which may introduce spurious relations as the true joint distribution is generally unidentifiable. To tackle this issue, we introduce a causal graphical model to characterize the dropout mechanism, namely, Causal Dropout Model. We provide a simple yet effective theoretical result: interestingly, the conditional independence (CI) relations in the data with dropouts, after deleting the samples with zero values (regardless if technical or not) for the conditioned variables, are asymptotically identical to the CI relations in the original data without dropouts. This particular test-wise deletion procedure, in which we perform CI tests on the samples without zeros for the conditioned variables, can be seamlessly integrated with existing structure learning approaches including constraint-based and greedy score-based methods, thus giving rise to a principled framework for GRNI in the presence of dropouts. We further show that the causal dropout model can be validated from data, and many existing statistical models to handle dropouts fit into our model as specific parametric instances. Empirical evaluation on synthetic, curated, and real-world experimental transcriptomic data comprehensively demonstrate the efficacy of our method.