Pathway Centric Analysis for Single-Cell RNA-Seq and Spatial Transcriptomics Data With GSDensity

Authors

Qingnan Liang
Yuefan Huang
Shan He
Ken Chen

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