A0749
Title: Bayesian model for disease-specific gene detection in high-dimensional spatially resolved transcriptomics
Authors: Qihuang Zhang - McGill University (Canada) [presenting]
Abstract: Identifying disease-indicative genes is critical for deciphering disease mechanisms and continues to attract significant interest. 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-seq, as these models often neglect the spatial organization of tissue spots. A new Bayesian shrinkage model is discussed to characterize the relationship between high-dimensional gene expressions and the disease status of tissue spots, incorporating spatial correlation among these spots through autoregressive terms. The model adopts a hierarchical structure to accommodate for the missing data within tissues and is further extended to facilitate the analysis of multiple correlated samples. To ensure the model's applicability to datasets of varying sizes, two computational frameworks are carried out for Bayesian parameter estimation, tailored to both small and large sample scenarios. Simulation studies are conducted to evaluate the performance of the proposed model, and the model is also applied to analyze the data arising from a HER2-positive breast cancer study.
