Generalized Bayesian nonparametric clustering framework for high-dimensional spatial omics data

TL;DR

This paper introduces a Bayesian nonparametric clustering method for high-dimensional spatial transcriptomics data that automatically infers the number of spatial domains while preserving spatial information.

Contribution

It presents a novel BNPMFA model that integrates spatial constraints and infers the optimal number of clusters, overcoming limitations of existing multi-stage approaches.

Findings

Outperforms state-of-the-art methods in clustering accuracy

Accurately estimates the number of spatial domains

Provides new insights into cellular region identification

Abstract

The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has transformed genomic research by enabling high-throughput gene expression profiling while preserving spatial context. Identifying spatial domains within SRT data is a critical task, with numerous computational approaches currently available. However, most existing methods rely on a multi-stage process that involves ad-hoc dimension reduction techniques to manage the high dimensionality of SRT data. These low-dimensional embeddings are then subjected to model-based or distance-based clustering methods. Additionally, many approaches depend on arbitrarily specifying the number of clusters (i.e., spatial domains), which can result in information loss and suboptimal downstream analysis. To address these limitations, we propose a novel Bayesian nonparametric mixture of factor analysis (BNPMFA) model, which incorporates a Markov random field-constrained Gibbs-type prior for partitioning high-dimensional spatial omics data. This new prior effectively integrates the spatial constraints inherent in SRT data while simultaneously inferring cluster membership and determining the optimal number of spatial domains. We have established the theoretical identifiability of cluster membership within this framework. The efficacy of our proposed approach is demonstrated through realistic simulations and applications to two SRT datasets. Our results show that the BNPMFA model not only surpasses state-of-the-art methods in clustering accuracy and estimating the number of clusters but also offers novel insights for identifying cellular regions within tissue samples.