Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations.
Zhana DurenXi ChenMahdi ZamanighomiWanwen ZengAnsuman T SatpathyHoward Y ChangYong WangWing Hung WongPublished in: Proceedings of the National Academy of Sciences of the United States of America (2018)
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.