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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Vydané v:IEEE transaction on neural networks and learning systems Ročník 35; číslo 2; s. 2473 - 2483
Hlavní autori: Zhang, Dai-Jun, Gao, Ying-Lian, Zhao, Jing-Xiu, Zheng, Chun-Hou, Liu, Jin-Xing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Popis
Shrnutí: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 high dropout events, noise, and high dimensionality, are still a research challenge in the single-cell field. To utilize single-cell data more efficiently and to better explore the heterogeneity among cells, a new graph autoencoder (GAE)-based consensus-guided model (scGAC) is proposed in this article. The data are preprocessed into multiple top-level feature datasets. Then, feature learning is performed by using GAEs to generate new feature matrices, followed by similarity learning based on distance fusion methods. The learned similarity matrices are fed back to the GAEs to guide their feature learning process. Finally, the abovementioned steps are iterated continuously to integrate the final consistent similarity matrix and perform other related downstream analyses. The scGAC model can accurately identify critical features and effectively preserve the internal structure of the data. This can further improve the accuracy of cell type identification.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3190289