High-dimensional Bayesian Model for Disease-Specific Gene Detection in Spatial Transcriptomics

TL;DR

This paper introduces a Bayesian spatial model for identifying disease-specific genes in spatial transcriptomics data, effectively capturing spatial correlations and handling missing data to improve gene detection accuracy.

Contribution

It presents a novel hierarchical Bayesian shrinkage model that incorporates spatial correlation and extends to large datasets, advancing gene detection in spatial transcriptomics.

Findings

Model accurately detects disease-specific genes in simulations.

Successfully applied to breast cancer spatial transcriptomics data.

Handles missing data and large sample sizes effectively.

Abstract

Identifying disease-indicative genes is critical for deciphering disease mechanisms and has attracted significant interest in biomedical research. Spatial transcriptomics offers unprecedented insights for the detection of disease-specific genes by enabling within-tissue contrasts. However, this new technology poses challenges for conventional statistical models developed for RNA-sequencing, as these models often neglect the spatial organization of tissue spots. In this article, we propose a Bayesian shrinkage model to characterize the relationship between high-dimensional gene expressions and the disease status of each tissue spot, incorporating spatial correlation among these spots through autoregressive terms. Our model adopts a hierarchical structure to facilitate the analysis of multiple correlated samples and is further extended to accommodate the missing data within tissues. To ensure the model's applicability to datasets of varying sizes, we carry out two computational frameworks for Bayesian parameter estimation, tailored to both small and large sample scenarios. Simulation studies are conducted to evaluate the performance of the proposed model. The proposed model is applied to analyze the data arising from a HER2-positive breast cancer study.