ArticleBriefings in bioinformatics2024
Topological and geometric analysis of cell states in single-cell transcriptomic data.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Condensates and cell states: A new paradigm for understanding tumor biology.Biophysical journal · 2026Review
- Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data.PLoS computational biology · 2026Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- Multiscale Cell-Cell Interactive Spatial Transcriptomics Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Cell and tissue reprogramming: Unlocking a new era in medical drug discovery.Pharmacological reviews · 2025Review
- Innovative strategies for bone organoid: Synergistic application and exploration of advanced technologies.Journal of orthopaedic translation · 2025Review
- FPCAM: A Weighted Dictionary-Driven Model for Single-Cell Annotation in Pulmonary Fibrosis.Biology · 2025Article
- Multiscale Differential Geometry Learning for Protein Flexibility Analysis.Journal of computational chemistry · 2025Article
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- Hodge Decomposition of Single-Cell RNA Velocity.Journal of chemical information and modeling · 2024Article
- Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data.Computers in biology and medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate the local and first-order structures of scRNA-seq data using graph representations, while scRNA-seq data frequently display complex high-dimensional structures. Here, we introduce scGeom, a tool that exploits the multiscale and multidimensional structures in scRNA-seq data by analyzing the geometry and topology through curvature and persistent homology of both cell and gene networks. We demonstrate the utility of these structural features to reflect biological properties and functions in several applications, where we show that curvatures and topological signatures of cell and gene networks can help indicate transition cells and the differentiation potential of cells. We also illustrate that structural characteristics can improve the classification of cell types.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.