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.
Genome Biol. 2024 Jan 12;25(1):14. doi: 10.1186/s13059-023-03159-6.
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.
现有的空间转录组数据分析方法主要侧重于描绘细胞类型在整个组织中的全局基因表达变化，而不是由细胞间相互作用驱动的局部基因表达变化。我们提出了一种新的统计程序，称为生态位差异表达（niche-DE）分析，该程序可识别细胞类型特异性的生态位相关基因，这些基因在特定细胞类型的特定空间生态位中表现出差异表达。我们进一步开发了 niche-LR 方法，以揭示潜在的配体-受体信号机制，这些机制是生态位差异基因表达模式的基础。niche-DE 和 niche-LR 适用于低分辨率基于点的空间转录组学数据以及单细胞或亚细胞分辨率的数据。
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