Machine learning and statistical methods for clustering single-cell RNA-sequencing data

• Published in: Briefings in bioinformatics Vol. 21; no. 4; pp. 1209 - 1223
• Main Authors: Petegrosso, Raphael, Li, Zhuliu, Kuang, Rui
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 15.07.2020; Oxford Publishing Limited (England)
• Subjects: Cluster analysis; Clustering; Core analysis; Data analysis; Gene expression; Gene sequencing; Learning algorithms; Machine learning; Mathematical analysis; Neural networks; Optimization; Probabilistic models; Ribonucleic acid; RNA; Source code; Statistical methods; Statistics; clustering; scRNA sequencing; machine learning; single-cell technology

Abstract   Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell...