Login / Signup

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

Zhana DurenXi ChenMahdi ZamanighomiWanwen ZengAnsuman T SatpathyHoward Y ChangYong WangWing Hung Wong
Published 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.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • induced apoptosis
  • electronic health record
  • cell cycle arrest
  • big data
  • gene expression
  • endoplasmic reticulum stress
  • artificial intelligence
  • cell death