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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
Anuja Sathe
2Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
Paul Hess
1Department of Statistics and Data Science, The Wharton School, University of Pennsylvania
Jiazhen Rong
3Genomics and Computational Biology Graduate Program, Perelman School of Medicine, University of Pennsylvania
Chi-Yun Wu
4The Gladstone Institute
Emma Furth
5Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania
Hanlee P. Ji
2Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
Nancy Zhang
1Department of Statistics and Data Science, The Wharton School, University of Pennsylvania

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Posted January 04, 2023.
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
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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