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BayesTME: A unified statistical framework for spatial transcriptomics

Haoran Zhang, View ORCID ProfileMiranda V. Hunter, Jacqueline Chou, Jeffrey F. Quinn, View ORCID ProfileMingyuan Zhou, Richard White, View ORCID ProfileWesley Tansey
doi: https://doi.org/10.1101/2022.07.08.499377
Haoran Zhang
1Dept. of Computer Science, University of Texas at Austin
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Miranda V. Hunter
2Sloan Kettering Institute
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Jacqueline Chou
3Dept. of Physiology, Biophysics, & Systems Biology, Weill Cornell Medical College
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Jeffrey F. Quinn
5Computational Oncology, Memorial Sloan Kettering Cancer Center
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Mingyuan Zhou
4McCombs School of Business, University of Texas at Austin
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Richard White
2Sloan Kettering Institute
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Wesley Tansey
5Computational Oncology, Memorial Sloan Kettering Cancer Center
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  • For correspondence: tanseyw@mskcc.org
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Abstract

Spatial variation in cellular phenotypes underlies heterogeneity in immune recognition and response to therapy in cancer and many other diseases. Spatial transcriptomics (ST) holds the potential to quantify such variation, but existing analysis methods address only a small part of the analysis challenge, such as spot deconvolution or spatial differential expression. 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 (i) be entirely reference-free without any need for paired scRNA-seq, (ii) outperform a large suite of methods in quantitative benchmarks, and (iii) uncover a new type of ST signal: spatial differential expression within individual cell types. To achieve the latter, BayesTME models each phenotype as spatially adaptive and discovers statistically significant spatial patterns amongst coordinated subsets of genes within phenotypes, which we term spatial transcriptional programs. On human and zebrafish melanoma tissues, BayesTME identifies spatial transcriptional programs that capture fundamental biological phenomena like bilateral symmetry, differential expression between interior and surface tumor cells, and tumor-associated fibroblast and macrophage reprogramming. Our results demonstrate BayesTME’s power in unlocking a new level of insight from spatial transcriptomics data and fostering a deeper understanding of the spatial architecture of the tumor microenvironment. BayesTME is open source and publicly available (https://github.com/tansey-lab/bayestme).

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/tansey-lab/bayestme

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 10, 2022.
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BayesTME: A unified statistical framework for spatial transcriptomics
Haoran Zhang, Miranda V. Hunter, Jacqueline Chou, Jeffrey F. Quinn, Mingyuan Zhou, Richard White, Wesley Tansey
bioRxiv 2022.07.08.499377; doi: https://doi.org/10.1101/2022.07.08.499377
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BayesTME: A unified statistical framework for spatial transcriptomics
Haoran Zhang, Miranda V. Hunter, Jacqueline Chou, Jeffrey F. Quinn, Mingyuan Zhou, Richard White, Wesley Tansey
bioRxiv 2022.07.08.499377; doi: https://doi.org/10.1101/2022.07.08.499377

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