scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data
Cell type identification using single-cell RNA sequencing data is critical for understanding disease mechanisms and drug discovery. Cell clustering analysis has been widely studied in health research for rare tumor cell detection. In this study, we propose a Gaussian mixture model-based variational...
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| Vydané v: | Machine learning: science and technology Ročník 4; číslo 3; s. 35013 - 35024 |
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| Hlavní autori: | , , , , , |
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| Jazyk: | English |
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Bristol
IOP Publishing
01.09.2023
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| ISSN: | 2632-2153, 2632-2153 |
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| Abstract | Cell type identification using single-cell RNA sequencing data is critical for understanding disease mechanisms and drug discovery. Cell clustering analysis has been widely studied in health research for rare tumor cell detection. In this study, we propose a Gaussian mixture model-based variational graph autoencoder on scRNA-seq data (scGMM-VGAE) that integrates a statistical clustering model to a deep learning algorithm to significantly improve the cell clustering performance. This model feeds a cell-cell graph adjacency matrix and a gene feature matrix into a graph variational autoencoder (VGAE) to generate latent data. These data are then used for cell clustering by the Gaussian mixture model (GMM) module. To optimize the algorithm, a designed loss function is derived by combining parameter estimates from the GMM and VGAE. We test the proposed method on four publicly available and three simulated datasets which contain many biological and technical zeros. The scGMM-VGAE outperforms four selected baseline methods on three evaluation metrics in cell clustering. By successfully incorporating GMM into deep learning VGAE on scRNA-seq data, the proposed method shows higher accuracy in cell clustering on scRNA-seq data. This improvement has a significant impact on detecting rare cell types in health research. All source codes used in this study can be found at
https://github.com/ericlin1230/scGMM-VGAE
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| AbstractList | Cell type identification using single-cell RNA sequencing data is critical for understanding disease mechanisms and drug discovery. Cell clustering analysis has been widely studied in health research for rare tumor cell detection. In this study, we propose a Gaussian mixture model-based variational graph autoencoder on scRNA-seq data (scGMM-VGAE) that integrates a statistical clustering model to a deep learning algorithm to significantly improve the cell clustering performance. This model feeds a cell-cell graph adjacency matrix and a gene feature matrix into a graph variational autoencoder (VGAE) to generate latent data. These data are then used for cell clustering by the Gaussian mixture model (GMM) module. To optimize the algorithm, a designed loss function is derived by combining parameter estimates from the GMM and VGAE. We test the proposed method on four publicly available and three simulated datasets which contain many biological and technical zeros. The scGMM-VGAE outperforms four selected baseline methods on three evaluation metrics in cell clustering. By successfully incorporating GMM into deep learning VGAE on scRNA-seq data, the proposed method shows higher accuracy in cell clustering on scRNA-seq data. This improvement has a significant impact on detecting rare cell types in health research. All source codes used in this study can be found at https://github.com/ericlin1230/scGMM-VGAE. Cell type identification using single-cell RNA sequencing data is critical for understanding disease mechanisms and drug discovery. Cell clustering analysis has been widely studied in health research for rare tumor cell detection. In this study, we propose a Gaussian mixture model-based variational graph autoencoder on scRNA-seq data (scGMM-VGAE) that integrates a statistical clustering model to a deep learning algorithm to significantly improve the cell clustering performance. This model feeds a cell-cell graph adjacency matrix and a gene feature matrix into a graph variational autoencoder (VGAE) to generate latent data. These data are then used for cell clustering by the Gaussian mixture model (GMM) module. To optimize the algorithm, a designed loss function is derived by combining parameter estimates from the GMM and VGAE. We test the proposed method on four publicly available and three simulated datasets which contain many biological and technical zeros. The scGMM-VGAE outperforms four selected baseline methods on three evaluation metrics in cell clustering. By successfully incorporating GMM into deep learning VGAE on scRNA-seq data, the proposed method shows higher accuracy in cell clustering on scRNA-seq data. This improvement has a significant impact on detecting rare cell types in health research. All source codes used in this study can be found at https://github.com/ericlin1230/scGMM-VGAE . |
| Author | Hu, Pingzhao Fung, Daryl L X Liu, Boyuan Leung, Carson K Lac, Leann Lin, Eric |
| Author_xml | – sequence: 1 givenname: Eric surname: Lin fullname: Lin, Eric organization: Dalla Lana School of Public Health, University of Toronto , Toronto, Ontario, Canada – sequence: 2 givenname: Boyuan surname: Liu fullname: Liu, Boyuan organization: Dalla Lana School of Public Health, University of Toronto , Toronto, Ontario, Canada – sequence: 3 givenname: Leann surname: Lac fullname: Lac, Leann organization: University of Manitoba Department of Statistics, Winnipeg, Manitoba, Canada – sequence: 4 givenname: Daryl L X surname: Fung fullname: Fung, Daryl L X organization: University of Manitoba Department of Computer Science, Winnipeg, Manitoba, Canada – sequence: 5 givenname: Carson K orcidid: 0000-0002-7541-9127 surname: Leung fullname: Leung, Carson K organization: University of Manitoba Department of Computer Science, Winnipeg, Manitoba, Canada – sequence: 6 givenname: Pingzhao orcidid: 0000-0002-9546-2245 surname: Hu fullname: Hu, Pingzhao organization: Western University Department of Biochemistry, London, Ontario N6A 5C1, Canada |
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| SubjectTerms | Algorithms cell type Cluster analysis Clustering Deep learning Gaussian mixture model Gene sequencing Graph neural networks Machine learning Medical research model integration Parameter estimation Probabilistic models Ribonucleic acid RNA scGMM-VGAE single cell RNA-seq variational graph autoencoder |
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| Title | scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data |
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