Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

PeerJ

卷:5

Xinghua Pan¹  Sherman M. Weissman¹  Travers Ching²  Xun Zhu²  Lana Garmire² 

[1] Department of Genetics, Yale University, New Haven, CT, United States;

[2] Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, United States;

关键词: Single-cell;    RNA-Seq;    Heterogeneity;    Non-negative matrix factorization;    Modularity;    Clustering;   

DOI  :  10.7717/peerj.2888

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown