BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment Journal Article

   

Authors:	Zhang, H.; Hunter, M. V.; Chou, J.; Quinn, J. F.; Zhou, M.; White, R. M.; Tansey, W.
Article Title:	BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment
Abstract:	Spatial variation in cellular phenotypes underlies heterogeneity in immune recognition and response to therapy in cancer and many other diseases. Spatial transcriptomics holds the potential to quantify such variation, but existing analysis methods are limited by their focus on individual tasks such as spot deconvolution. We present BayesTME, an end-to-end Bayesian method for analyzing spatial transcriptomics data. BayesTME unifies several previously distinct analysis goals under a single, holistic generative model. This unified approach enables BayesTME to deconvolve spots into cell phenotypes without any need for paired single-cell RNA-seq. BayesTME then goes beyond spot deconvolution to uncover spatial expression patterns among coordinated subsets of genes within phenotypes, which we term spatial transcriptional programs. BayesTME achieves state-of-the-art performance across myriad benchmarks. On human and zebrafish melanoma tissues, BayesTME identifies spatial transcriptional programs that capture fundamental biological phenomena such as bilateral symmetry and tumor-associated fibroblast and macrophage reprogramming. BayesTME is open source. © 2023 The Authors
Keywords:	genetics; animal; animals; bayes theorem; gene expression profiling; benchmarking; macrophage; macrophages; zebra fish; zebrafish; tumor microenvironment; bayesian methods; machine learning; humans; human; spatial transcriptomics; spatial gene expression; spatial modeling
Journal Title:	Cell Systems
Volume:	14
Issue:	7
ISSN:	2405-4712
Publisher:	Cell Press
Date Published:	2023-07-19
Start Page:	605
End Page:	619.e7
Language:	English
DOI:	10.1016/j.cels.2023.06.003
PUBMED:	37473731
PROVIDER:	scopus
PMCID:	PMC10368078
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165252378&doi=10.1016%2fj.cels.2023.06.003&partnerID=40&md5=700fc2c791d286d4e9f0715b42f46a1b
Notes:	The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Corresponding author is MSK author: Wesley Tansey -- Source: Scopus

https://synapse.mskcc.org/synapse/works/has_related_data_chk/207981