ArticleGenome research2020
netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.
Article in Genome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed, 119 citations in OpenAlex.
- scCADI: Cell-Aware Diffusion Model for Single-Cell RNA-seq Imputation.Interdisciplinary sciences, computational life sciences · 2026Article
- Clustering Single-Cell RNA-Seq Data with Low-Rank Matrix Factorization and Local Graph Regularization.Interdisciplinary sciences, computational life sciences · 2026Article
- Joint Low Rank Representation with Symmetric Orthogonal Decomposition for Clustering of scRNA-seq Data.Interdisciplinary sciences, computational life sciences · 2026Article
- scZiva: imputation method for single-cell RNA-seq data with zero-inflated variational autoencoder.BMC bioinformatics · 2026Article
- Patches: A Representation Learning Framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing.bioRxiv : the preprint server for biology · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- scIRT: Imputation and Dimensionality Reduction for Single-Cell RNA-Seq Data by Combining NMF with SMOTE.International journal of molecular sciences · 2026Article
- SPP1 in notochord cells modulates intervertebral disc degeneration through CD44 recognition by macrophages based on single-cell transcriptome analysis.International journal of surgery (London, England) · 2026Article
- JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.The annals of applied statistics · 2025Article
- Beyond single biomarkers: multi-omics strategies to predict immunotherapy outcomes in blood cancers.Clinical and experimental medicine · 2025Review
- Deciphering splicing heterogeneity at single-cell resolution by SCSES.Nature communications · 2025Article
- Knowledge graph-aided Bayesian active learning for top-K genetic interaction discovery.Scientific reports · 2025Article
- GRACKLE: an interpretable matrix factorization approach for biomedical representation learning.Bioinformatics (Oxford, England) · 2025Article
- scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein-protein interactions.Nature methods · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection.Life science alliance · 2024Article
- Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-Seq data.BMC genomics · 2024Article
- Graph Fourier transform for spatial omics representation and analyses of complex organs.Nature communications · 2024Article
- A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data.Genome research · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Single-cell RNA-sequencing (scRNA-seq) enables high-throughput measurement of RNA expression in single cells. However, because of technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells in a lower-dimensional space, leveraging the observation that cells generally occupy a small number of RNA expression states. We introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc learns a low-dimensional representation of scRNA-seq transcript counts using network-regularized non-negative matrix factorization. The network regularization takes advantage of prior knowledge of gene-gene interactions, encouraging pairs of genes with known interactions to be nearby each other in the low-dimensional representation. The resulting matrix factorization imputes gene abundance for both zero and nonzero counts and can be used to cluster cells into meaningful subpopulations. We show that netNMF-sc outperforms existing methods at clustering cells and estimating gene-gene covariance using both simulated and real scRNA-seq data, with increasing advantages at higher dropout rates (e.g., >60%). We also show that the results from netNMF-sc are robust to variation in the input network, with more representative networks leading to greater performance gains.
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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.