Joint identification of spatially variable genes via a network-assisted Bayesian regularization approach

TL;DR

This paper introduces a Bayesian regularization method that identifies spatially variable genes while accounting for gene network interactions and confounding cellular composition in spatial transcriptomic data.

Contribution

It develops a novel network-assisted Bayesian regularization approach with thresholded graph Laplacian to improve gene identification accuracy.

Findings

Outperforms existing methods in simulations and real data.

Effectively corrects for confounding cellular variations.

Accommodates count data with zero inflation and overdispersion.

Abstract

Identifying genes that display spatial patterns is critical to investigating expression interactions within a spatial context and further dissecting biological understanding of complex mechanistic functionality. Despite the increase in statistical methods designed to identify spatially variable genes, they are mostly based on marginal analysis and share the limitation that the dependence (network) structures among genes are not well accommodated, where a biological process usually involves changes in multiple genes that interact in a complex network. Moreover, the latent cellular composition within spots may introduce confounding variations, negatively affecting identification accuracy. In this study, we develop a novel Bayesian regularization approach for spatial transcriptomic data, with the confounding variations induced by varying cellular distributions effectively corrected. Significantly advancing from the existing studies, a thresholded graph Laplacian regularization is proposed to simultaneously identify spatially variable genes and accommodate the network structure among genes. The proposed method is based on a zero-inflated negative binomial distribution, effectively accommodating the count nature, zero inflation, and overdispersion of spatial transcriptomic data. Extensive simulations and the application to real data demonstrate the competitive performance of the proposed method.