CoupledClustering – integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations

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. Stanford University researchers 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.

Overview of the coupled-clustering method

rna-seq

(A) Single-cell gene expression and single-cell chromatin accessibility data. (B) Learning coupling matrix from public data. (C) Coupled clustering model. (D) Cluster-specific gene expression and chromatin accessibility.

Availability – Software and simulation data are available at http://web.stanford.edu/∼zduren/CoupledNMF/

Duren Z, Chen X, Zamanighomi M, Zeng W, Satpathy AT, Chang HY, Wang Y, Wong WH. (2018) Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proc Natl Acad Sci USA 115(30):7723-7728. [article]

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