ProgClust: A progressive clustering method to identify cell populations
Identifying different types of cells in scRNA-seq data is a critical task in single-cell data analysis. In this paper, we propose a method called ProgClust for the decomposition of cell populations and detection of rare cells. ProgClust represents the single-cell data with clustering trees where a p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1183099/full |
| _version_ | 1797851401691332608 |
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| author | Han Li Ying Wang Ying Wang Ying Wang Yongxuan Lai Feng Zeng Feng Zeng Feng Zeng Fan Yang Fan Yang Fan Yang |
| author_facet | Han Li Ying Wang Ying Wang Ying Wang Yongxuan Lai Feng Zeng Feng Zeng Feng Zeng Fan Yang Fan Yang Fan Yang |
| author_sort | Han Li |
| collection | DOAJ |
| description | Identifying different types of cells in scRNA-seq data is a critical task in single-cell data analysis. In this paper, we propose a method called ProgClust for the decomposition of cell populations and detection of rare cells. ProgClust represents the single-cell data with clustering trees where a progressive searching method is designed to select cell population-specific genes and cluster cells. The obtained trees reveal the structure of both abundant cell populations and rare cell populations. Additionally, it can automatically determine the number of clusters. Experimental results show that ProgClust outperforms the baseline method and is capable of accurately identifying both common and rare cells. Moreover, when applied to real unlabeled data, it reveals potential cell subpopulations which provides clues for further exploration. In summary, ProgClust shows potential in identifying subpopulations of complex single-cell data. |
| first_indexed | 2024-04-09T19:17:19Z |
| format | Article |
| id | doaj.art-e02ba0763a5649f997c721c624067099 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-09T19:17:19Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-e02ba0763a5649f997c721c6240670992023-04-06T05:02:29ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-04-011410.3389/fgene.2023.11830991183099ProgClust: A progressive clustering method to identify cell populationsHan Li0Ying Wang1Ying Wang2Ying Wang3Yongxuan Lai4Feng Zeng5Feng Zeng6Feng Zeng7Fan Yang8Fan Yang9Fan Yang10Department of Automation, Xiamen University, Xiamen, ChinaDepartment of Automation, Xiamen University, Xiamen, ChinaNational Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, ChinaXiamen Key Lab Big Data Intelligent Anal and Decis, Xiamen, ChinaSchool of Informatics, Xiamen University, Xiamen, ChinaDepartment of Automation, Xiamen University, Xiamen, ChinaNational Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, ChinaXiamen Key Lab Big Data Intelligent Anal and Decis, Xiamen, ChinaDepartment of Automation, Xiamen University, Xiamen, ChinaNational Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, ChinaXiamen Key Lab Big Data Intelligent Anal and Decis, Xiamen, ChinaIdentifying different types of cells in scRNA-seq data is a critical task in single-cell data analysis. In this paper, we propose a method called ProgClust for the decomposition of cell populations and detection of rare cells. ProgClust represents the single-cell data with clustering trees where a progressive searching method is designed to select cell population-specific genes and cluster cells. The obtained trees reveal the structure of both abundant cell populations and rare cell populations. Additionally, it can automatically determine the number of clusters. Experimental results show that ProgClust outperforms the baseline method and is capable of accurately identifying both common and rare cells. Moreover, when applied to real unlabeled data, it reveals potential cell subpopulations which provides clues for further exploration. In summary, ProgClust shows potential in identifying subpopulations of complex single-cell data.https://www.frontiersin.org/articles/10.3389/fgene.2023.1183099/fullScRNA-seqsingle-cell clusteringensemble clusteringrare cellunbalanced data |
| spellingShingle | Han Li Ying Wang Ying Wang Ying Wang Yongxuan Lai Feng Zeng Feng Zeng Feng Zeng Fan Yang Fan Yang Fan Yang ProgClust: A progressive clustering method to identify cell populations Frontiers in Genetics ScRNA-seq single-cell clustering ensemble clustering rare cell unbalanced data |
| title | ProgClust: A progressive clustering method to identify cell populations |
| title_full | ProgClust: A progressive clustering method to identify cell populations |
| title_fullStr | ProgClust: A progressive clustering method to identify cell populations |
| title_full_unstemmed | ProgClust: A progressive clustering method to identify cell populations |
| title_short | ProgClust: A progressive clustering method to identify cell populations |
| title_sort | progclust a progressive clustering method to identify cell populations |
| topic | ScRNA-seq single-cell clustering ensemble clustering rare cell unbalanced data |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1183099/full |
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