摘要
Dimension reduction is a cornerstone of exploratory data analysis; however, traditional methods fail to preserve the spatial context of spatial genomics data. In this work, we develop a nonnegative spatial factorization (NSF) model that allows interpretable, parts-based decomposition of spatial single-cell count data. NSF allows label-free annotation of regions of interest in spatial genomics data and identifies genes and cells that can be used to define those regions.
摘要译文
降低是探索性数据分析的基石；但是，传统方法无法保留空间基因组学数据的空间环境。在这项工作中，我们开发了一个非负空间分解（NSF）模型，该模型允许可解释的，基于零件的空间单细胞计数数据。NSF允许对空间基因组数据中感兴趣的区域的无标签注释，并识别可用于定义这些区域的基因和细胞。
Parts-based decomposition of spatial genomics data finds distinct tissue regions[J]. Nature Methods, 2023,20(2): 187-188