ArticleCell systems2023
BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment.
Article in Cell systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 23 citations in OpenAlex.
- Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.Cell reports methods · 2026Article
- RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.Nature communications · 2026Article
- STAPLE: automating spatial transcriptomics analysis and AI interpretation.bioRxiv : the preprint server for biology · 2026Article
- Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications.Cancer cell · 2026Review
- Bayesian multi-cell type models for the analysis of complex immune cell populations with application to ovarian cancer.Briefings in bioinformatics · 2026Article
- Spatially resolving cancer: from cell states to therapy.Trends in cancer · 2026Review
- Multi-omics to study chronic respiratory diseases and viral infections.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- SURF: A Self-Supervised Deep Learning Method for Reference-Free Deconvolution in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs.Nature biotechnology · 2025Article
- Acquisition of discrete immune suppressive barriers contributes to the initiation and progression of preinvasive to invasive human lung cancer.bioRxiv : the preprint server for biology · 2025Article
- Deep learning in integrating spatial transcriptomics with other modalities.Briefings in bioinformatics · 2024Review
- An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.Briefings in bioinformatics · 2024Article
- Machine learning integrative approaches to advance computational immunology.Genome medicine · 2024Review
- SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics.Communications biology · 2024Article
- MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance.Patterns (New York, N.Y.) · 2024Article
- Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future.Future oncology (London, England) · 2024Review
- Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data.Genome biology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.