Deep soft K-means clustering with self-training for single-cell RNA sequence data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) allows researchers to study cell heterogeneity at the cellular level. A crucial step in analyzing scRNA-seq data is to cluster cells into subpopulations to facilitate subsequent downstream analysis. However, frequent dropout events and increasing size of scRNA-seq data make clustering such high-dimensional, sparse and massive transcriptional expression profiles challenging. Although some existing deep learning-based clustering algorithms for single cells combine dimensionality reduction with clustering, they either ignore the distance and affinity constraints between similar cells or make some additional latent space assumptions like mixture Gaussian distribution, failing to learn cluster-friendly low-dimensional space. Therefore, in this paper, we co

SUBMITTER: Chen L 

PROVIDER: S-EPMC7671315 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature