A New Graph Autoencoder-Based Multi-Level Kernel Subspace Fusion Framework for Single-Cell Type Identification

The advent of single-cell RNA sequencing (scRNA-seq) technology offers the opportunity to conduct biological research at the cellular level. Single-cell type identification based on unsupervised clustering is one of the fundamental tasks of scRNA-seq data analysis. Although many single-cell clusteri...

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Vydáno v:	IEEE/ACM transactions on computational biology and bioinformatics Ročník 21; číslo 6; s. 2292 - 2303
Hlavní autoři:	Wang, Juan, Qiao, Tian-Jing, Zheng, Chun-Hou, Liu, Jin-Xing, Shang, Jun-Liang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.11.2024
Témata:	Accuracy Adaptation models Algorithms Animals Autoencoder Cluster Analysis Clustering methods Computational Biology - methods Consensus learning Data models Deep Learning Feature extraction graph autoencoder Humans Kernel kernel self-expression model multi-level kernel space fusion multiple top fea-ture sets RNA-Seq - methods Sequence Analysis, RNA - methods Sequential analysis Single-Cell Analysis - methods single-cell type identification
ISSN:	1545-5963, 1557-9964, 1557-9964
On-line přístup:	Získat plný text
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