
Faculty, Staff and Student Publications
Publication Date
12-18-2023
Journal
Nature Communications
Abstract
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.
Keywords
Humans, Animals, Mice, Single-Cell Gene Expression Analysis, Gene Expression Profiling, Cluster Analysis, Technology, Single-Cell Analysis, Transcriptome, Software, Cancer microenvironment, RNA sequencing, Cellular signalling networks, Computational models
DOI
10.1038/s41467-023-44206-x
PMID
38110427
PMCID
PMC10728201
PubMedCentral® Posted Date
12-18-2023
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons