SPACEL: deep learning-based characterization of spatial transcriptome architectures

• Published in: Nature communications Vol. 14; no. 1; pp. 7603 - 18
• Main Authors: Xu, Hao, Wang, Shuyan, Fang, Minghao, Luo, Songwen, Chen, Chunpeng, Wan, Siyuan, Wang, Rirui, Tang, Meifang, Xue, Tian, Li, Bin, Lin, Jun, Qu, Kun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.11.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 38; 631/114; 631/114/1314; 631/114/2397; 631/1647/48; 631/61/514; Algorithms; Alignment; Artificial neural networks; Coordinates; Data analysis; Data processing; Deconvolution; Deep Learning; Gene expression; Gene Expression Profiling; Humanities and Social Sciences; Information processing; Machine learning; Models, Statistical; multidisciplinary; Probabilistic models; Science; Science (multidisciplinary); Spatial analysis; Spatial discrimination learning; Spatial distribution; Toolkits; Transcriptome - genetics; Transcriptomes; Transcriptomics

Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, joint analysis of multiple ST slices and aligning t...