ArticleNature communications2023
Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Depletion of Effector Regulatory T Cells Associates with Major Response to Induction Dual Immune Checkpoint Blockade.Cancer discovery · 2025Trial
- Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture.bioRxiv : the preprint server for biology · 2026Article
- Multi-task spatial distillation reveals cell-type-resolved programmed cell death landscapes in the human kidney.Briefings in bioinformatics · 2026Article
- Agent-based modeling of cellular dynamics in adoptive cell therapy.Communications biology · 2026Article
- Inferring pathway activity from single-cell and spatial transcriptomics data with PaaSc.PLoS computational biology · 2025Article
- Spatiotemporal analysis of Crohn's disease reveals PECAM2 signaling at the basis of the inflammation-to-fibrosis transition.Journal of Crohn's & colitis · 2025Article
- TrimNN: characterizing cellular community motifs for studying multicellular topological organization in complex tissues.Nature communications · 2025Article
- LSGI: interpretable spatial gradient analysis for spatial transcriptomics data.Genome biology · 2025Article
- The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.Journal of mass spectrometry : JMS · 2025Article
- Pan-cancer analysis shapes the understanding of cancer biology and medicine.Cancer communications (London, England) · 2025Review
- A unified analysis of atlas single-cell data.Genome research · 2025Article
- Cancer stem cells and tumor-associated macrophages as mates in tumor progression: mechanisms of crosstalk and advanced bioinformatic tools to dissect their phenotypes and interaction.Frontiers in immunology · 2025Review
- Article
- Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles.iScience · 2024Article
- Interpretable Spatial Gradient Analysis for Spatial Transcriptomics Data.bioRxiv : the preprint server for biology · 2024Article
- Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review.Global challenges (Hoboken, NJ) · 2024Review
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4 authors.
Funding
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.
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