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

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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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