A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection

Single-cell RNA sequencing (scRNA-seq) technology is famous for providing a microscopic view to help capture cellular heterogeneity. This characteristic has advanced the field of genomics by enabling the delicate differentiation of cell types. However, the properties of single-cell datasets, such as...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 35; H. 2; S. 2473 - 2483
Hauptverfasser:	Zhang, Dai-Jun, Gao, Ying-Lian, Zhao, Jing-Xiu, Zheng, Chun-Hou, Liu, Jin-Xing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Cell differentiation Cluster Analysis Consensus Data models Datasets Distance fusion Feature extraction Gene sequencing Genetic algorithms Genomics graph autoencoder (GAE) Heterogeneity integration algorithm Learning Mathematical models Neural Networks, Computer Optimization Representation learning Sequential analysis Similarity Single-Cell Gene Expression Analysis single-cell RNA-seq
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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