ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data
Abstract Clustering is a critical component of single-cell RNA sequencing (scRNA-seq) data analysis and can help reveal cell types and infer cell lineages. Despite considerable successes, there are few methods tailored to investigating cluster-specific genes contributing to cell heterogeneity, which...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | Genome Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13059-023-03046-0 |
| _version_ | 1797559354965098496 |
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| author | Yang Li Mingcong Wu Shuangge Ma Mengyun Wu |
| author_facet | Yang Li Mingcong Wu Shuangge Ma Mengyun Wu |
| author_sort | Yang Li |
| collection | DOAJ |
| description | Abstract Clustering is a critical component of single-cell RNA sequencing (scRNA-seq) data analysis and can help reveal cell types and infer cell lineages. Despite considerable successes, there are few methods tailored to investigating cluster-specific genes contributing to cell heterogeneity, which can promote biological understanding of cell heterogeneity. In this study, we propose a zero-inflated negative binomial mixture model (ZINBMM) that simultaneously achieves effective scRNA-seq data clustering and gene selection. ZINBMM conducts a systemic analysis on raw counts, accommodating both batch effects and dropout events. Simulations and the analysis of five scRNA-seq datasets demonstrate the practical applicability of ZINBMM. |
| first_indexed | 2024-03-10T17:44:12Z |
| format | Article |
| id | doaj.art-ba7f164345e546fba06cafda0d292351 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-03-10T17:44:12Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-ba7f164345e546fba06cafda0d2923512023-11-20T09:35:17ZengBMCGenome Biology1474-760X2023-09-0124112810.1186/s13059-023-03046-0ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic dataYang Li0Mingcong Wu1Shuangge Ma2Mengyun Wu3Center for Applied Statistics and School of Statistics, Renmin University of ChinaCenter for Applied Statistics and School of Statistics, Renmin University of ChinaDepartment of Biostatistics, Yale UniversitySchool of Statistics and Management, Shanghai University of Finance and EconomicsAbstract Clustering is a critical component of single-cell RNA sequencing (scRNA-seq) data analysis and can help reveal cell types and infer cell lineages. Despite considerable successes, there are few methods tailored to investigating cluster-specific genes contributing to cell heterogeneity, which can promote biological understanding of cell heterogeneity. In this study, we propose a zero-inflated negative binomial mixture model (ZINBMM) that simultaneously achieves effective scRNA-seq data clustering and gene selection. ZINBMM conducts a systemic analysis on raw counts, accommodating both batch effects and dropout events. Simulations and the analysis of five scRNA-seq datasets demonstrate the practical applicability of ZINBMM.https://doi.org/10.1186/s13059-023-03046-0Clustering analysisGene selectionScRNA-seq data |
| spellingShingle | Yang Li Mingcong Wu Shuangge Ma Mengyun Wu ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data Genome Biology Clustering analysis Gene selection ScRNA-seq data |
| title | ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data |
| title_full | ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data |
| title_fullStr | ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data |
| title_full_unstemmed | ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data |
| title_short | ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data |
| title_sort | zinbmm a general mixture model for simultaneous clustering and gene selection using single cell transcriptomic data |
| topic | Clustering analysis Gene selection ScRNA-seq data |
| url | https://doi.org/10.1186/s13059-023-03046-0 |
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