ArticleBMC bioinformatics2020
CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- STAPLE: automating spatial transcriptomics analysis and AI interpretation.bioRxiv : the preprint server for biology · 2026Article
- Cytometric Atlas of Combination Immunotherapy in Pancreatic Cancer: Blood-Based Signatures Reveal Vaccine and Checkpoint Inhibitor Responses.Cancer immunology research · 2026Article
- Comparative assessment of physiochemical metrics for the clustering of adaptive immune receptor repertoires.Nucleic acids research · 2025Article
- Integrative analysis of genomic and transcriptomic data informs precancer progression in the pancreas.bioRxiv : the preprint server for biology · 2025Article
- Spatial profiling of human pancreatic ductal adenocarcinoma reveals molecular alterations associated with venous invasion.Science translational medicine · 2025Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- SpottedPy quantifies relationships between spatial transcriptomic hotspots and uncovers environmental cues of epithelial-mesenchymal plasticity in breast cancer.Genome biology · 2024Article
- Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics.Nature methods · 2024Article
- PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration.Cell systems · 2024Article
- Integration of Clinical Trial Spatial Multiomics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery.Cancer research · 2024Article
- Transfer Learning Reveals Cancer-Associated Fibroblasts Are Associated with Epithelial-Mesenchymal Transition and Inflammation in Cancer Cells in Pancreatic Ductal Adenocarcinoma.Cancer research · 2024Article
- Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS.Nature protocols · 2023Review
- Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus.Science advances · 2023Article
- Digitize your Biology! Modeling multicellular systems through interpretable cell behavior.bioRxiv : the preprint server for biology · 2023Article
- Article
- Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial.bioRxiv : the preprint server for biology · 2023Article
- Multi-batch single-cell comparative atlas construction by deep learning disentanglement.Nature communications · 2023Article
- Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.Cell systems · 2023Article
- Integrated T cell cytometry metrics for immune-monitoring applications in immunotherapy clinical trials.JCI insight · 2022Article
- Triple-negative breast cancer metastasis involves complex epithelial-mesenchymal transition dynamics and requires vimentin.Science translational medicine · 2022Article
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3 authors.
Funding
Abstract
backgroundBayesian factorization methods, including Coordinated Gene Activity in Pattern Sets (CoGAPS), are emerging as powerful analysis tools for single cell data. However, these methods have greater computational costs than their gradient-based counterparts. These costs are often prohibitive for analysis of large single-cell datasets. Many such methods can be run in parallel which enables this limitation to be overcome by running on more powerful hardware. However, the constraints imposed by the prior distributions in CoGAPS limit the applicability of parallelization methods to enhance computational efficiency for single-cell analysis.
resultsWe developed a new software framework for parallel matrix factorization in Version 3 of the CoGAPS R/Bioconductor package to overcome the computational limitations of Bayesian matrix factorization for single cell data analysis. This parallelization framework provides asynchronous updates for sequential updating steps of the algorithm to enhance computational efficiency. These algorithmic advances were coupled with new software architecture and sparse data structures to reduce the memory overhead for single-cell data.
conclusionsAltogether our new software enhance the efficiency of the CoGAPS Bayesian matrix factorization algorithm so that it can analyze 1000 times more cells, enabling factorization of large single-cell data sets.
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