scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network

• Published in: Briefings in bioinformatics Vol. 24; no. 1
• Main Authors: Wang, Jing, Xia, Junfeng, Wang, Haiyun, Su, Yansen, Zheng, Chun-Hou
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19.01.2023; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biological analysis; Cluster Analysis; Clustering; Coders; Gene Expression Profiling - methods; Gene sequencing; Heterogeneity; Humans; Learning; Machine learning; Modules; Noise reduction; Representations; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Single-Cell Gene Expression Analysis; contrastive learning; scRNA-seq data; deep clustering; denoising auto-encoder; ZINB model

Abstract The advances in single-cell ribonucleic acid sequencing (scRNA-seq) allow researchers to explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a prerequisite in scRNA-seq analysis since it can recognize cell identities. However, the high dimensionality, no...