Journal
PEERJ
Volume 5, Issue -, Pages -Publisher
PEERJ INC
DOI: 10.7717/peerj.2888
Keywords
Single-cell; RNA-Seq; Heterogeneity; Non-negative matrix factorization; Modularity; Clustering; Subpopulation; Single cell sequencing; Single cell; Feature gene
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Funding
- NIEHS through the trans-NIH Big Data to Knowledge (BD2K) initiative [K01ES025434]
- NIH/NIGMS [P20 COBRE GM103457]
- NICHD [R01 HD084633]
- NLM [R01LM012373]
- Medical Research Grant from Hawaii Community Foundation [14ADVC-64566]
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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 (D-scores) in separating these groups, and discovered that NMF uniquely identifies genes expressed at intermediate levels as top-ranked genes. Finally, we show that in conjugation with the modularity detection method FEM, NMF reveals meaningful protein-protein interaction modules. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data. The NMF based subpopulation detection package is available at: https://github.com/lanagarmire/NMFEM.
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