Spatial transcriptomics (ST) complements scRNA-seq by enabling in situ gene-expression profiling while preserving native tissue architecture and histomorphology. Yet ST data are inherently noisy, high-dimensional, and multimodal, complicating robust clustering. Many existing approaches define neighbor-hoods solely by spatial proximity and aggregate features via naive averaging, which blurs tissue boundaries and underrepre-sents biologically salient heterogeneity and microenvironmental interactions. In this study, we propose MuSACL, a Multimodal Spatially Adaptive Contrastive Learning model that integrates gene expression, spatial coordinates, and histological images to accurately identify spatial domains in ST data. Specifically, MuSACL introduces a Multimodal Proximity Subgraph (MPS) to construct biologically informed graph structures. Furthermore, MuSACL employs heterogeneous modalities using a Graph Attention Network (GAT) and proposes a novel Spatially Adaptive Attention Readout (SAAR) mechanism to enhance contrastive learning with spatial adaptivity. This approach overcomes the limitations of naive feature averaging, yielding more expressive, spatially aware representations. On two public ST datasets, MuSACL significantly surpasses state-of-the-art baselines. Ablations attribute the gains to MPS and SAAR, which enhance representation quality and spatial resolution.

MOTIVATION: Thanks to the development of high-throughput sequencing technologies, massive amounts of various biomolecular data have been accumulated to revolutionize the study of genomics and molecular biology. One of the main challenges in analyzing this biomolecular data is to cluster their subtypes into subpopulations to facilitate subsequent downstream analysis. Recently, many clustering methods have been developed to address the biomolecular data. However, the computational methods often suffer from many limitations such as high dimensionality, data heterogeneity and noise. RESULTS: In our study, we develop a novel Graph-based Multiple Hierarchical Consensus Clustering (GMHCC) method with an unsupervised graph-based feature ranking (FR) and a graph-based linking method to explore the multiple hierarchical information of the underlying partitions of the consensus clustering for multiple types of biomolecular data. Indeed, we first propose to use a graph-based unsupervised FR model to measure each feature by building a graph over pairwise features and then providing each feature with a rank. Subsequently, to maintain the diversity and robustness of basic partitions (BPs), we propose multiple diverse feature subsets to generate several BPs and then explore the hierarchical structures of the multiple BPs by refining the global consensus function. Finally, we develop a new graph-based linking method, which explicitly considers the relationships between clusters to generate the final partition. Experiments on multiple types of biomolecular data including 35 cancer gene expression datasets and eight single-cell RNA-seq datasets validate the effectiveness of our method over several state-of-the-art consensus clustering approaches. Furthermore, differential gene analysis, gene ontology enrichment analysis and KEGG pathway analysis are conducted, providing novel insights into cell developmental lineages and characterization mechanisms. AVAILABILITY AND IMPLEMENTATION: The source code is available at GitHub: https://github.com/yifuLu/GMHCC. The software and the supporting data can be downloaded from: https://figshare.com/articles/software/GMHCC/17111291. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.