ArticleJournal of computational and applied mathematics2024
Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization.
Article in Journal of computational and applied mathematics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Identification of ATF3 as a Potential Key Biomarker for Hypertensive Nephropathy via Single-Cell Transcriptome Sequencing.Inflammation · 2026Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- How large is the universe of RNA-like motifs? A clustering analysis of RNA graph motifs using topological descriptors.PLoS computational biology · 2025Article
- Research Trends and Dynamics in Single-cell RNA Sequencing for Musculoskeletal Diseases: A Scientometric and Visualization Study.International journal of medical sciences · 2025Review
- Cauchy hyper-graph Laplacian nonnegative matrix factorization for single-cell RNA-sequencing data analysis.BMC bioinformatics · 2024Article
- 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
- Analyzing scRNA-seq data by CCP-assisted UMAP and tSNE.PloS one · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) is a relatively new technology that has stimulated enormous interest in statistics, data science, and computational biology due to the high dimensionality, complexity, and large scale associated with scRNA-seq data. Nonnegative matrix factorization (NMF) offers a unique approach due to its meta-gene interpretation of resulting low-dimensional components. However, NMF approaches suffer from the lack of multiscale analysis. This work introduces two persistent Laplacian regularized NMF methods, namely, topological NMF (TNMF) and robust topological NMF (rTNMF). By employing a total of 12 datasets, we demonstrate that the proposed TNMF and rTNMF significantly outperform all other NMF-based methods. We have also utilized TNMF and rTNMF for the visualization of popular Uniform Manifold Approximation and Projection (UMAP) and
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Registered trials
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