PREDICTING CELLULAR RESPONSES WITH VARIATIONAL CAUSAL INFERENCE AND REFINED RELATIONAL INFORMATION

Abstract

Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variational Bayesian causal inference framework to predict a cell's gene expressions under counterfactual perturbations (perturbations that this cell did not factually receive), leveraging information representing biological knowledge in the form of gene regulatory networks (GRNs) to aid individualized cellular response predictions. Aiming at a data-adaptive GRN, we also developed an adjacency matrix updating technique for graph convolutional networks and used it to refine GRNs during pre-training, which generated more insights on gene relations and enhanced model performance. Additionally, we propose a robust estimator within our framework for the asymptotically efficient estimation of marginal perturbation effect, which is yet to be carried out in previous works. With extensive experiments, we exhibited the advantage of our approach over state-of-the-art deep learning models for individual response prediction.

1. INTRODUCTION

Studying a cell's response to genetic, chemical, and physical perturbations is fundamental in understanding various biological processes and can lead to important applications such as drug discovery and personalized therapies. Cells respond to exogenous perturbations at different levels, including epigenetic (DNA methylation and histone modifications), transcriptional (RNA expression), translational (protein expression), and post-translational (chemical modifications on proteins). The availability of single-cell RNA sequencing (scRNA-seq) datasets has led to the development of several methods for predicting single-cell transcriptional responses (Ji et al., 2021) . These methods fall into two broad categories. The first category (Lotfollahi et al., 2019; 2020; Rampášek et al., 2019; Russkikh et al., 2020; Lotfollahi et al., 2021a) approaches the problem of predicting single cell gene expression response without explicitly modeling the gene regulatory network (GRN), which is widely hypothesized to be the structural causal model governing transcriptional responses of cells (Emmert-Streib et al., 2014) . Notably among those studies, CPA (Lotfollahi et al., 2021a) uses an adversarial autoencoder framework designed to decompose the cellular gene expression response to latent components representing perturbations, covariates and basal cellular states. CPA extends the classic idea of decomposing high-dimensional gene expression response into perturbation vectors (Clark et al., 2014; 2015) , which can be used for finding connections among perturbations (Subramanian et al., 2017) . However, while CPA's adversarial approach encourages latent indepen-

