Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis
Abstract In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous stu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-022-09027-0 |
| _version_ | 1797977601318322176 |
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| author | Juan Wang Nana Zhang Shasha Yuan Junliang Shang Lingyun Dai Feng Li Jinxing Liu |
| author_facet | Juan Wang Nana Zhang Shasha Yuan Junliang Shang Lingyun Dai Feng Li Jinxing Liu |
| author_sort | Juan Wang |
| collection | DOAJ |
| description | Abstract In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification. |
| first_indexed | 2024-04-11T05:09:47Z |
| format | Article |
| id | doaj.art-8c501013d58249ba99ac0ea819708a9e |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-11T05:09:47Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-8c501013d58249ba99ac0ea819708a9e2022-12-25T12:06:17ZengBMCBMC Genomics1471-21642022-12-0123111410.1186/s12864-022-09027-0Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysisJuan Wang0Nana Zhang1Shasha Yuan2Junliang Shang3Lingyun Dai4Feng Li5Jinxing Liu6School of Computer Science, Qufu Normal UniversitySchool of Computer Science, Qufu Normal UniversitySchool of Computer Science, Qufu Normal UniversitySchool of Computer Science, Qufu Normal UniversitySchool of Computer Science, Qufu Normal UniversitySchool of Computer Science, Qufu Normal UniversitySchool of Computer Science, Qufu Normal UniversityAbstract In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.https://doi.org/10.1186/s12864-022-09027-0Dictionary learningLow-rank representationscRNA-seq data analysisSubspace clusteringCell type identification |
| spellingShingle | Juan Wang Nana Zhang Shasha Yuan Junliang Shang Lingyun Dai Feng Li Jinxing Liu Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis BMC Genomics Dictionary learning Low-rank representation scRNA-seq data analysis Subspace clustering Cell type identification |
| title | Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis |
| title_full | Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis |
| title_fullStr | Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis |
| title_full_unstemmed | Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis |
| title_short | Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis |
| title_sort | non negative low rank representation based on dictionary learning for single cell rna sequencing data analysis |
| topic | Dictionary learning Low-rank representation scRNA-seq data analysis Subspace clustering Cell type identification |
| url | https://doi.org/10.1186/s12864-022-09027-0 |
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