Deciphering the Spatial Modular Patterns of Tissues by Integrating Spatial and Single-Cell Transcriptomic Data

• DOI: 10.1089/cmb.2021.0617
• Publication Date: 2022-06-21T13:28:29Z
• Authors: Xu Shan; Jinyu Chen; Kangning Dong; Wei Zhou; Shihua Zhang
• Keywords: Pancreatic Neoplasms; Mice; Sequence Analysis, RNA; 0206 medical engineering; Animals; 02 engineering and technology; Single-Cell Analysis; Transcriptome; Carcinoma, Pancreatic Ductal

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to analyze the expression level of tissues at cellular resolution. However, it could not capture spatial organization cells in tissue. The spatially resolved transcriptomics technologies (ST) have been developed address this issue. emerging STs are still inefficient single-cell resolution and/or fail sufficient reads. To end, we adopted partial least squares-based method (spatial modular patterns [SpaMOD]) simultaneously integrate two data modalities, as well networks related and spots, identify cell-spot comodules for deciphering SpaMOD tissues. We applied three paired scRNA-seq ST datasets, derived from mouse brain, granuloma, pancreatic ductal adenocarcinoma, respectively. identified provide detailed biological insights into relationships between cell populations their locations