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Niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions

Kaishu Mason, Anuja Sathe, Paul Hess, Jiazhen Rong, Chi-Yun Wu, Emma Furth, Hanlee P. Ji, Nancy Zhang
doi: https://doi.org/10.1101/2023.01.03.522646
Kaishu Mason
1Department of Statistics and Data Science, The Wharton School, University of Pennsylvania
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Anuja Sathe
2Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
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Paul Hess
1Department of Statistics and Data Science, The Wharton School, University of Pennsylvania
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Jiazhen Rong
3Genomics and Computational Biology Graduate Program, Perelman School of Medicine, University of Pennsylvania
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Chi-Yun Wu
4The Gladstone Institute
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Emma Furth
5Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania
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Hanlee P. Ji
2Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
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Nancy Zhang
1Department of Statistics and Data Science, The Wharton School, University of Pennsylvania
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  • For correspondence: nzh@wharton.upenn.edu
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Posted January 04, 2023.
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Niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
Kaishu Mason, Anuja Sathe, Paul Hess, Jiazhen Rong, Chi-Yun Wu, Emma Furth, Hanlee P. Ji, Nancy Zhang
bioRxiv 2023.01.03.522646; doi: https://doi.org/10.1101/2023.01.03.522646
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Niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
Kaishu Mason, Anuja Sathe, Paul Hess, Jiazhen Rong, Chi-Yun Wu, Emma Furth, Hanlee P. Ji, Nancy Zhang
bioRxiv 2023.01.03.522646; doi: https://doi.org/10.1101/2023.01.03.522646

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