Robust classification of single-cell transcriptome data by nonnegative matrix factorization

• Published in: Bioinformatics (Oxford, England) England), 2017-01, Vol.33 (2), p.235-242
• Main Authors: Shao, Chunxuan, Höfer, Thomas
• Format: Article
• Language: English
• Subjects: Animals; Cerebral Cortex - cytology; Cerebral Cortex - metabolism; Computational Biology - methods; Gene Expression Profiling - methods; Mice; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Software; Statistics as Topic - methods
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btw607

Single-cell transcriptome data provide unprecedented resolution to study heterogeneity in cell populations and present a challenge for unsupervised classification. Popular methods, like principal component analysis (PCA), often suffer from the high level of noise in the data. Here we adapt Nonnegative Matrix Factorization (NMF) to study the problem of identifying subpopulations in single-cell transcriptome data. In contrast to the conventional gene-centered view of NMF, identifying metagenes, we used NMF in a cell-centered direction, identifying cell subtypes ('metacells'). Using three different datasets (based on RT-qPCR and single cell RNA-seq data, respectively), we show that NMF outperforms PCA in identifying subpopulations in an accurate and robust way, without the need for prior feature selection; moreover, NMF successfully recovered the broad classes on a large dataset (thousands of single-cell transcriptomes), as identified by a computationally sophisticated method. NMF allows to identify feature genes in a direct, unbiased manner. We propose novel approaches for determining a biologically meaningful number of subpopulations based on minimizing the ambiguity of classification. In conclusion, our study shows that NMF is a robust, informative and simple method for the unsupervised learning of cell subtypes from single-cell gene expression data. https://github.com/ccshao/nimfa CONTACTS: [e-mail] or [e-mail] information: Supplementary data are available at Bioinformatics online.