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

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

SUBMITTER: Liang Q 

PROVIDER: S-EPMC10728201 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature