Spectral clustering based on learning similarity matrix.

ABSTRACT:

Motivation

Single-cell RNA-sequencing (scRNA-seq) technology can generate genome-wide expression data at the single-cell levels. One important objective in scRNA-seq analysis is to cluster cells where each cluster consists of cells belonging to the same cell type based on gene expression patterns.

Results

We introduce a novel spectral clustering framework that imposes sparse structures on a target matrix. Specifically, we utilize multiple doubly stochastic similarity matrices to learn a similarity matrix, motivated by the observation that each similarity matrix can be a different informative representation of the data. We impose a sparse structure on the target matrix followed by shrinking pairwise differences of the rows in the target matrix, motivated by the fact that the

SUBMITTER: Park S 

PROVIDER: S-EPMC6454479 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature