  The location, timing, and abundance of gene expression (both mRNA and
proteins) within a tissue define the molecular mechanisms of cell functions.
Recent technology breakthroughs in spatial molecular profiling, including
imaging-based technologies and sequencing-based technologies, have enabled the
comprehensive molecular characterization of single cells while preserving their
spatial and morphological contexts. This new bioinformatics scenario calls for
effective and robust computational methods to identify genes with spatial
patterns. We represent a novel Bayesian hierarchical model to analyze spatial
transcriptomics data, with several unique characteristics. It models the
zero-inflated and over-dispersed counts by deploying a zero-inflated negative
binomial model that greatly increases model stability and robustness. Besides,
the Bayesian inference framework allows us to borrow strength in parameter
estimation in a de novo fashion. As a result, the proposed model shows
competitive performances in accuracy and robustness over existing methods in
both simulation studies and two real data applications. The related R/C++
source code is available at https://github.com/Minzhe/BOOST-GP.
