ReviewNature protocols2023
Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS.
Review in Nature protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Cancer-Associated Fibroblasts Promote Epithelial-Mesenchymal Transition and Classical to Basal Subtype Shift in Pancreatic Cancer.Cancer research · 2026Article
- The B7 family subgroup reflects tumor cell heterogeneity and patient post-operative prognosis in gallbladder cancer.Biology direct · 2026Article
- Blocking apoptosis promotes survival and alters developmental dynamics of human retinal ganglion cells in retinal organoids.Cell reports · 2026Article
- Valeric acid from oral microbiome suppresses esophageal cancer growth by disrupting eEF1A1 -mediated translational output.Nature communications · 2026Article
- A cell fate specification and transition mechanism for human foveolar cone subtype patterning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- In-memory analog computing for non-negative matrix factorization.Nature communications · 2026Article
- Multimodal-based analysis of single-cell ATAC-seq data enables highly accurate delineation of clinically relevant tumor cell subpopulations.Genome medicine · 2026Article
- DisCP-Atlas: a comprehensive resource mapping cellular processes to complex diseases.Nucleic acids research · 2026Article
- Immunoregulatory Roles of Tumor-Originated Pericytes Identified by Single-Cell Analysis in Glioblastoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Detecting signatures underlying the composition of biological data.Nucleic acids research · 2025Article
- Integrative analysis of genomic and transcriptomic data informs precancer progression in the pancreas.bioRxiv : the preprint server for biology · 2025Article
- Revealing a coherent cell state landscape across single cell datasets with CONCORD.bioRxiv : the preprint server for biology · 2025Article
- Article
- The Key Role of COA6 in Pancreatic Ductal Adenocarcinoma: Metabolic Reprogramming and Regulation of the Immune Microenvironment.Journal of cellular and molecular medicine · 2025Article
- GRACKLE: an interpretable matrix factorization approach for biomedical representation learning.Bioinformatics (Oxford, England) · 2025Article
- An unbiased tissue transcriptome analysis identifies potential markers for skin phenotypes and therapeutic responses in atopic dermatitis.Nature communications · 2025Article
- Role of artificial intelligence in advancing immunology.Immunologic research · 2025Review
- Bridging inflammation and proliferation: scRNA-seq analysis of chemotactic and growth factor signaling in mouse skin wound repair.Frontiers in immunology · 2025Article
- Intrinsic GATA4 expression sensitizes the aortic root to dilation in a Loeys-Dietz syndrome mouse model.Nature cardiovascular research · 2024Article
- Spatiotemporal transcriptomic mapping of regenerative inflammation in skeletal muscle reveals a dynamic multilayered tissue architecture.The Journal of clinical investigation · 2024Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.
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Registered trials
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