  The advent of next-generation sequencing-based spatially resolved
transcriptomics (SRT) techniques has reshaped genomic studies by enabling
high-throughput gene expression profiling while preserving spatial and
morphological context. Nevertheless, there are inherent challenges associated
with these new high-dimensional spatial data, such as zero-inflation,
over-dispersion, and heterogeneity. These challenges pose obstacles to
effective clustering, which is a fundamental problem in SRT data analysis.
Current computational approaches often rely on heuristic data preprocessing and
arbitrary cluster number prespecification, leading to considerable information
loss and consequently, suboptimal downstream analysis. In response to these
challenges, we introduce BNPSpace, a novel Bayesian nonparametric spatial
clustering framework that directly models SRT count data. BNPSpace facilitates
the partitioning of the whole spatial domain, which is characterized by
substantial heterogeneity, into homogeneous spatial domains with similar
molecular characteristics while identifying a parsimonious set of
discriminating genes among different spatial domains. Moreover, BNPSpace
incorporates spatial information through a Markov random field prior model,
encouraging a smooth and biologically meaningful partition pattern.
