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
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Abstract	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.
AbstractList	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. 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.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.
Author	Zhang, Dai-Jun Zhao, Jing-Xiu Gao, Ying-Lian Zheng, Chun-Hou Liu, Jin-Xing
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Cites_doi	10.1093/bioinformatics/bty390 10.1016/j.yexcr.2020.111959 10.1016/j.cell.2018.02.001 10.1016/j.imu.2020.100330 10.1038/nature25980 10.3390/nu13092952 10.1109/TPAMI.2020.2974828 10.1109/tnnls.2021.3054635 10.1109/tnnls.2021.3069424 10.1109/tnnls.2021.3071369 10.1038/ncomms11075 10.1093/bib/bbab236 10.1038/npp.2015.235 10.1038/ni.3437 10.1109/tkde.2020.3048678 10.1109/TPAMI.2018.2879108 10.1093/bioinformatics/btab535 10.1109/ACCESS.2021.3049807 10.1093/bioinformatics/btx167 10.1186/s12885-020-07675-7 10.1016/j.cmet.2016.08.018 10.1016/j.cell.2019.05.031 10.3389/fgene.2019.00020 10.1109/TNNLS.2020.3016357 10.1109/TIP.2016.2553459 10.1242/jcs.243303 10.1038/nmeth.4207 10.1145/2623330.2623726 10.1126/science.1245316 10.1093/bioinformatics/btz139 10.1109/TKDE.2019.2911946 10.7150/thno.43865 10.1038/nature13173 10.1109/TMM.2018.2889560 10.1201/b19706-12 10.1093/bib/bbab319 10.1016/j.bcp.2020.114204 10.1093/bib/bbz063 10.1145/3132847.3132967 10.1109/TCYB.2016.2526683 10.1109/tcbb.2021.3126641 10.1038/nn.3881 10.1186/s13059-021-02356-5 10.1038/s41418-020-0578-0 10.1109/TIP.2019.2940693
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References	ref13 ref35 ref12 ref34 ref15 ref37 ref14 Huang (ref21) ref36 ref11 ref33 ref10 ref32 ref2 ref1 ref17 ref39 ref16 ref38 ref19 ref18 Wagner (ref30) 2007 Strehl (ref31) 2003; 3 ref24 ref46 ref23 ref45 ref26 ref48 ref25 ref47 ref20 ref42 ref41 ref22 ref44 ref43 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40
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Snippet	Single-cell RNA sequencing (scRNA-seq) technology is famous for providing a microscopic view to help capture cellular heterogeneity. This characteristic has...
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SubjectTerms	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
Title	A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection
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