A Hybrid Clustering Algorithm for Identifying Cell Types from Single-Cell RNA-Seq Data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) has recently brought new insight into cell differentiation processes and functional variation in cell subtypes from homogeneous cell populations. A lack of prior knowledge makes unsupervised machine learning methods, such as clustering, suitable for analyzing scRNA-seq . However, there are several limitations to overcome, including high dimensionality, clustering result instability, and parameter adjustment complexity. In this study, we propose a method by combining structure entropy and k nearest neighbor to identify cell subpopulations in scRNA-seq data. In contrast to existing clustering methods for identifying cell subtypes, minimized structure entropy results in natural communities without specifying the number of clusters. To investigate the per

SUBMITTER: Zhu X 

PROVIDER: S-EPMC6409843 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature