scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and s...

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Bibliographic Details
Published in:	International journal of molecular sciences Vol. 25; no. 11; p. 5976
Main Authors:	Zhang, Tianjiao, Ren, Jixiang, Li, Liangyu, Wu, Zhenao, Zhang, Ziheng, Dong, Guanghui, Wang, Guohua
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.06.2024 MDPI
Subjects:	Accuracy Algorithms Analysis Cells Cluster Analysis Clustering Computational Biology - methods Data analysis Datasets Deep Learning Gene expression Humans Information management Neural networks RNA sequencing RNA-Seq - methods Sequence Analysis, RNA - methods Single-Cell Analysis - methods Single-Cell Gene Expression Analysis Software Sparsity Statistical analysis Teaching methods KL divergence scRNA-seq data graph contrastive learning APPNPGCN ZINB model
ISSN:	1422-0067, 1661-6596, 1422-0067
Online Access:	Get full text
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