Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Epublish 40 citations
PubMed: 38217002 DOI: 10.1186/s13059-023-03159-6
Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions.
Genome Biol 25(1):14
Authors: Kaishu Mason, Anuja Sathe, Paul R Hess, Jiazhen Rong, Chi-Yun Wu, Emma Furth, Katalin Susztak, Jonathan Levinsohn, Hanlee P Ji, Nancy Zhang
Affiliations
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA.
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Genomics and Computational Biology Graduate Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
- The Gladstone Institute, San Francisco, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA.
Abstract
Existing methods for analysis of spatial transcriptomic data focus on delineating the global gene expression variations of cell types across the tissue, rather than local gene expression changes driven by cell-cell interactions. We propose a new statistical procedure called niche-differential expression (niche-DE) analysis that identifies cell-type-specific niche-associated genes, which are differentially expressed within a specific cell type in the context of specific spatial niches. We further develop niche-LR, a method to reveal ligand-receptor signaling mechanisms that underlie niche-differential gene expression patterns. Niche-DE and niche-LR are applicable to low-resolution spot-based spatial transcriptomics data and data that is single-cell or subcellular in resolution.
MeSH
Cells, Genes, Cell Communication, Gene Expression, Spatial Transcriptomics
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