Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

• Published in: Nature communications Vol. 14; no. 1; p. 8416
• Main Authors: Liang, Qingnan, Huang, Yuefan, He, Shan, Chen, Ken
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18-12-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 45/91; 631/114/2391; 631/114/2397; 631/114/794; 631/61/514/1949; 631/67/327; Animals; Cancer; Cell division; Cluster Analysis; Clustering; Data analysis; Datasets; Dissection; Gene Expression Profiling; Genes; Humanities and Social Sciences; Humans; Mice; multidisciplinary; Nominations; Science; Science (multidisciplinary); Single-Cell Analysis; Single-Cell Gene Expression Analysis; Spatial data; Technology; Trajectory analysis; Transcriptome; Transcriptomics; Tumors
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-44206-x

Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.
Clustering-based analysis has limited power in highly dynamic single-cell data, which is a common situation in tumour samples. Here, authors introduce GSDensity, enabling pathway-centric analysis for the direct integration of data with their domain knowledge.