Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations

Zhana Duren; Xi Chen; Mahdi Zamanighomi; Wanwen Zeng; Ansuman T Satpathy; Howard Y Chang; Yong Wang; Wing Hung Wong

doi:10.1073/pnas.1805681115

Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations

Proc Natl Acad Sci U S A. 2018 Jul 24;115(30):7723-7728. doi: 10.1073/pnas.1805681115. Epub 2018 Jul 9.

Authors

Zhana Duren^{1

2}, Xi Chen^{1

2}, Mahdi Zamanighomi^{1

2

3}, Wanwen Zeng^{1

2

4}, Ansuman T Satpathy³, Howard Y Chang³, Yong Wang^{5

6}, Wing Hung Wong^{7

2

3}

Affiliations

¹ Department of Statistics, Stanford University, Stanford, CA 94305.
² Department of Biomedical Data Science, Stanford University, Stanford, CA 94305.
³ Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305.
⁴ Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, Department of Automation, Tsinghua University, 100084 Beijing, China.
⁵ Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100080 Beijing, China.
⁶ Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, 650223 Kunming, China.
⁷ Department of Statistics, Stanford University, Stanford, CA 94305; whwong@stanford.edu.

Abstract

When different types of functional genomics data are generated on single cells from different samples of cells from the same heterogeneous population, the clustering of cells in the different samples should be coupled. We formulate this "coupled clustering" problem as an optimization problem and propose the method of coupled nonnegative matrix factorizations (coupled NMF) for its solution. The method is illustrated by the integrative analysis of single-cell RNA-sequencing (RNA-seq) and single-cell ATAC-sequencing (ATAC-seq) data.

Keywords: NMF; coupled clustering; single-cell genomic data.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Animals
Databases, Genetic*
Humans
Models, Genetic*
Sequence Analysis, RNA / methods*

Abstract

Publication types

MeSH terms

Grants and funding