BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data.

Autor: Guo Y; Department of Mathematical Sciences, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, United States., Zhu B; Department of Mathematical Sciences, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, United States.; Department of Statistics, The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong, China., Tang C; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States., Rong R; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States., Ma Y; Department of Biostatistics, Brown University, 69 Brown Street, Providence, RI 02912, United States., Xiao G; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States., Xu L; Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States., Li Q; Department of Mathematical Sciences, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, United States.
Jazyk: angličtina
Zdroj: Briefings in bioinformatics [Brief Bioinform] 2024 Sep 23; Vol. 25 (6).
DOI: 10.1093/bib/bbae524
Abstrakt: The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.
(© The Author(s) 2024. Published by Oxford University Press.)
Databáze: MEDLINE
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