A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering

Background Single-cell RNA sequencing (scRNA-seq) allows the exploration of biological heterogeneity among different cell types within tissues at a single-cell resolution. Cell clustering serves as a foundation for scRNA-seq data analysis and provides new insights into the heterogeneity of cells wit...

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Veröffentlicht in:	BMC genomics Jg. 26; H. 1; S. 749 - 13
Hauptverfasser:	Tang, Binhua, Feng, Yingying, Gao, Xinyu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 18.08.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Ablation Adversarial AE Algorithms Animal Genetics and Genomics Artificial neural networks Autoencoder Biomedical and Life Sciences Cluster Analysis Clustering Cross-attention mechanism Data analysis Data integration Datasets Electronic data processing GCN Gene expression Gene sequencing Genomics of isolated human populations Graphical representations Heterogeneity Humans Learning Life Sciences Machine learning Methods Microarrays Microbial Genetics and Genomics Neural networks Normal distribution Plant Genetics and Genomics Proteomics RNA sequencing RNA-Seq - methods ScRNA-seq Self-supervised clustering Sensory integration Single-Cell Gene Expression Analysis - methods China ScRNA-seq GCN Self-supervised clustering Adversarial AE Cross-attention mechanism
ISSN:	1471-2164, 1471-2164
Online-Zugang:	Volltext
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