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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.13453 |
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| _version_ | 1866912238015610880 |
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| author | Zhu, Bencong Cassese, Alberto Vannucci, Marina Guindani, Michele Li, Qiwei |
| author_facet | Zhu, Bencong Cassese, Alberto Vannucci, Marina Guindani, Michele Li, Qiwei |
| contents | 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. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes that elucidate the clustering result, referred to as spatial domain-specific discriminating genes (DGs). Existing methods for identifying these genes typically rely on a two-stage approach, which can lead to the phenomenon known as \textit{double-dipping}. To address the challenge, we propose a unified Bayesian latent block model that simultaneously detects a list of DGs contributing to spatial domain identification while clustering these DGs and spatial locations. The efficacy of our proposed method is validated through a series of simulation experiments, and its capability to identify DGs is demonstrated through applications to benchmark SRT datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13453 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BISON: Bi-clustering of spatial omics data with feature selection Zhu, Bencong Cassese, Alberto Vannucci, Marina Guindani, Michele Li, Qiwei Applications 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. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes that elucidate the clustering result, referred to as spatial domain-specific discriminating genes (DGs). Existing methods for identifying these genes typically rely on a two-stage approach, which can lead to the phenomenon known as \textit{double-dipping}. To address the challenge, we propose a unified Bayesian latent block model that simultaneously detects a list of DGs contributing to spatial domain identification while clustering these DGs and spatial locations. The efficacy of our proposed method is validated through a series of simulation experiments, and its capability to identify DGs is demonstrated through applications to benchmark SRT datasets. |
| title | BISON: Bi-clustering of spatial omics data with feature selection |
| topic | Applications |
| url | https://arxiv.org/abs/2502.13453 |