A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study

Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene fu...

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Autores principales:	Jujuan Zhuang, Changjing Ren, Dan Ren, Yu’ang Li, Danyang Liu, Lingyu Cui, Geng Tian, Jiasheng Yang, Jingbo Liu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	single-cell RNA sequencing GO enrichment analysis KPCA semantic similarity analysis Gene Ontology Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/4aff72a7f2aa4bb78055c14efbf49912
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spelling	oai:doaj.org-article:4aff72a7f2aa4bb78055c14efbf499122021-12-01T19:26:32ZA Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study2234-943X10.3389/fonc.2021.797057https://doaj.org/article/4aff72a7f2aa4bb78055c14efbf499122021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.797057/fullhttps://doaj.org/toc/2234-943XCritical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.Jujuan ZhuangChangjing RenDan RenYu’ang LiDanyang LiuLingyu CuiGeng TianJiasheng YangJingbo LiuFrontiers Media S.A.articlesingle-cell RNA sequencingGO enrichment analysisKPCAsemantic similarity analysisGene OntologyNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	single-cell RNA sequencing GO enrichment analysis KPCA semantic similarity analysis Gene Ontology Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	single-cell RNA sequencing GO enrichment analysis KPCA semantic similarity analysis Gene Ontology Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Jujuan Zhuang Changjing Ren Dan Ren Yu’ang Li Danyang Liu Lingyu Cui Geng Tian Jiasheng Yang Jingbo Liu A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study
description	Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.
format	article
author	Jujuan Zhuang Changjing Ren Dan Ren Yu’ang Li Danyang Liu Lingyu Cui Geng Tian Jiasheng Yang Jingbo Liu
author_facet	Jujuan Zhuang Changjing Ren Dan Ren Yu’ang Li Danyang Liu Lingyu Cui Geng Tian Jiasheng Yang Jingbo Liu
author_sort	Jujuan Zhuang
title	A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study
title_short	A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study
title_full	A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study
title_fullStr	A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study
title_full_unstemmed	A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study
title_sort	novel single-cell rna sequencing data feature extraction method based on gene function analysis and its applications in glioma study
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/4aff72a7f2aa4bb78055c14efbf49912
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A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study

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