ArticlePeerJ2017
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization.
Article in PeerJ, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Identification of differentially expressed genes in RNA-seq data via semi-rigid orthogonal sparse KL-NMTF.BMC bioinformatics · 2026Article
- Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions.Journal of machine learning research : JMLR · 2025Article
- Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- Interface-guided phenotyping of coding variants in the transcription factor RUNX1.Cell reports · 2024Article
- Interpretable online network dictionary learning for inferring long-range chromatin interactions.PLoS computational biology · 2024Article
- Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data.Computers in biology and medicine · 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
- A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening.Nature methods · 2023Article
- Learning Individual Survival Models from PanCancer Whole Transcriptome Data.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- scGEM: Unveiling the Nested Tree-Structured Gene Co-Expressing Modules in Single Cell Transcriptome Data.Cancers · 2023Article
- ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs.Nature communications · 2023Article
- Improved downstream functional analysis of single-cell RNA-sequence data using DGAN.Scientific reports · 2023Article
- Non-negative Independent Factor Analysis disentangles discrete and continuous sources of variation in scRNA-seq data.Bioinformatics (Oxford, England) · 2022Article
- Alignment and integration of spatial transcriptomics data.Nature methods · 2022Article
- Similarity and Dissimilarity Regularized Nonnegative Matrix Factorization for Single-Cell RNA-seq Analysis.Interdisciplinary sciences, computational life sciences · 2022Article
- Article
- From bench to bedside: Single-cell analysis for cancer immunotherapy.Cancer cell · 2021Review
- scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.Bioinformatics (Oxford, England) · 2021Article
- The triumphs and limitations of computational methods for scRNA-seq.Nature methods · 2021Review
- Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data.Genomics, proteomics & bioinformatics · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq datasets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various datasets. We ranked genes by their importance scores (
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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.