scDA: Single cell discriminant analysis for single-cell RNA sequencing data
Single-cell RNA-sequencing (scRNA-seq) techniques provide unprecedented opportunities to investigate phenotypic and molecular heterogeneity in complex biological systems. However, profiling massive amounts of cells brings great computational challenges to accurately and efficiently characterize dive...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037021002270 |
| _version_ | 1818871970902573056 |
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| author | Qianqian Shi Xinxing Li Qirui Peng Chuanchao Zhang Luonan Chen |
| author_facet | Qianqian Shi Xinxing Li Qirui Peng Chuanchao Zhang Luonan Chen |
| author_sort | Qianqian Shi |
| collection | DOAJ |
| description | Single-cell RNA-sequencing (scRNA-seq) techniques provide unprecedented opportunities to investigate phenotypic and molecular heterogeneity in complex biological systems. However, profiling massive amounts of cells brings great computational challenges to accurately and efficiently characterize diverse cell populations. Single cell discriminant analysis (scDA) solves this problem by simultaneously identifying cell groups and discriminant metagenes based on the construction of cell-by-cell representation graph, and then using them to annotate unlabeled cells in data. We demonstrate scDA is effective to determine cell types, revealing the overall variabilities between cells from eleven data sets. scDA also outperforms several state-of-the-art methods when inferring the labels of new samples. In particular, we found scDA less sensitive to drop-out events and capable to label a mass of cells within or across datasets after learning even from a small set of data. The scDA approach offers a new way to efficiently analyze scRNA-seq profiles of large size or from different batches. scDA was implemented and freely available at https://github.com/ZCCQQWork/scDA. |
| first_indexed | 2024-12-19T12:31:23Z |
| format | Article |
| id | doaj.art-a418e188bb4a4d26a2af9e70235076a0 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-12-19T12:31:23Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-a418e188bb4a4d26a2af9e70235076a02022-12-21T20:21:23ZengElsevierComputational and Structural Biotechnology Journal2001-03702021-01-011932343244scDA: Single cell discriminant analysis for single-cell RNA sequencing dataQianqian Shi0Xinxing Li1Qirui Peng2Chuanchao Zhang3Luonan Chen4Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaAgricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaAgricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaKey Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China; Corresponding authors at: Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China (L. Chen).Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China; State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China; Corresponding authors at: Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China (L. Chen).Single-cell RNA-sequencing (scRNA-seq) techniques provide unprecedented opportunities to investigate phenotypic and molecular heterogeneity in complex biological systems. However, profiling massive amounts of cells brings great computational challenges to accurately and efficiently characterize diverse cell populations. Single cell discriminant analysis (scDA) solves this problem by simultaneously identifying cell groups and discriminant metagenes based on the construction of cell-by-cell representation graph, and then using them to annotate unlabeled cells in data. We demonstrate scDA is effective to determine cell types, revealing the overall variabilities between cells from eleven data sets. scDA also outperforms several state-of-the-art methods when inferring the labels of new samples. In particular, we found scDA less sensitive to drop-out events and capable to label a mass of cells within or across datasets after learning even from a small set of data. The scDA approach offers a new way to efficiently analyze scRNA-seq profiles of large size or from different batches. scDA was implemented and freely available at https://github.com/ZCCQQWork/scDA.http://www.sciencedirect.com/science/article/pii/S2001037021002270Single-cell RNA-sequencingDiscriminant analysisDiscriminant featuresCell-by-cell representation graphCell annotation |
| spellingShingle | Qianqian Shi Xinxing Li Qirui Peng Chuanchao Zhang Luonan Chen scDA: Single cell discriminant analysis for single-cell RNA sequencing data Computational and Structural Biotechnology Journal Single-cell RNA-sequencing Discriminant analysis Discriminant features Cell-by-cell representation graph Cell annotation |
| title | scDA: Single cell discriminant analysis for single-cell RNA sequencing data |
| title_full | scDA: Single cell discriminant analysis for single-cell RNA sequencing data |
| title_fullStr | scDA: Single cell discriminant analysis for single-cell RNA sequencing data |
| title_full_unstemmed | scDA: Single cell discriminant analysis for single-cell RNA sequencing data |
| title_short | scDA: Single cell discriminant analysis for single-cell RNA sequencing data |
| title_sort | scda single cell discriminant analysis for single cell rna sequencing data |
| topic | Single-cell RNA-sequencing Discriminant analysis Discriminant features Cell-by-cell representation graph Cell annotation |
| url | http://www.sciencedirect.com/science/article/pii/S2001037021002270 |
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