DiSTect: a Bayesian model for disease-associated gene discovery and prediction in spatial transcriptomics.

ABSTRACT:

Motivation

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-associated 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 corrleation of the disease status among tissue spots.

Results

In this article, we propose DiSTect, 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

SUBMITTER: Zhao Q 

PROVIDER: S-EPMC12502917 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature