scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information
Abstract Background Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data. Results We deve...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-09-01
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| Series: | Cell & Bioscience |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13578-022-00886-4 |
| _version_ | 1811274863752511488 |
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| author | Jing Qi Qiongyu Sheng Yang Zhou Jiao Hua Shutong Xiao Shuilin Jin |
| author_facet | Jing Qi Qiongyu Sheng Yang Zhou Jiao Hua Shutong Xiao Shuilin Jin |
| author_sort | Jing Qi |
| collection | DOAJ |
| description | Abstract Background Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data. Results We develop a statistical model-based multidimensional imputation algorithm, scMTD, that identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells, leveraging information on cell-level, gene-level, and transcriptome dynamic to recover scRNA-seq data. Compared with the state-of-the-art imputation methods through several real-data-based analytical experiments, scMTD effectively recovers biological signals of transcriptomes and consistently outperforms the other algorithms in improving FISH validation, trajectory inference, differential expression analysis, clustering analysis, and identification of cell types. Conclusions scMTD maintains the gene expression characteristics, enhances the clustering of cell subpopulations, assists the study of gene expression dynamics, contributes to the discovery of rare cell types, and applies to both UMI-based and non-UMI-based data. Overall, scMTD’s reliability, applicability, and scalability make it a promising imputation approach for scRNA-seq data. |
| first_indexed | 2024-04-12T23:26:46Z |
| format | Article |
| id | doaj.art-1b04e9d0009e4ff78e44a821f05a4a1b |
| institution | Directory Open Access Journal |
| issn | 2045-3701 |
| language | English |
| last_indexed | 2024-04-12T23:26:46Z |
| publishDate | 2022-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Cell & Bioscience |
| spelling | doaj.art-1b04e9d0009e4ff78e44a821f05a4a1b2022-12-22T03:12:23ZengBMCCell & Bioscience2045-37012022-09-0112111510.1186/s13578-022-00886-4scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic informationJing Qi0Qiongyu Sheng1Yang Zhou2Jiao Hua3Shutong Xiao4Shuilin Jin5School of Mathematics, Harbin Institute of TechnologySchool of Mathematics, Harbin Institute of TechnologySchool of Mathematics, Harbin Institute of TechnologySchool of Mathematics, Harbin Institute of TechnologySchool of Mathematics, Harbin Institute of TechnologySchool of Mathematics, Harbin Institute of TechnologyAbstract Background Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data. Results We develop a statistical model-based multidimensional imputation algorithm, scMTD, that identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells, leveraging information on cell-level, gene-level, and transcriptome dynamic to recover scRNA-seq data. Compared with the state-of-the-art imputation methods through several real-data-based analytical experiments, scMTD effectively recovers biological signals of transcriptomes and consistently outperforms the other algorithms in improving FISH validation, trajectory inference, differential expression analysis, clustering analysis, and identification of cell types. Conclusions scMTD maintains the gene expression characteristics, enhances the clustering of cell subpopulations, assists the study of gene expression dynamics, contributes to the discovery of rare cell types, and applies to both UMI-based and non-UMI-based data. Overall, scMTD’s reliability, applicability, and scalability make it a promising imputation approach for scRNA-seq data.https://doi.org/10.1186/s13578-022-00886-4Single-cell RNA-seqMultidimensional informationTranscriptome dynamicCell-levelGene-level |
| spellingShingle | Jing Qi Qiongyu Sheng Yang Zhou Jiao Hua Shutong Xiao Shuilin Jin scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information Cell & Bioscience Single-cell RNA-seq Multidimensional information Transcriptome dynamic Cell-level Gene-level |
| title | scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information |
| title_full | scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information |
| title_fullStr | scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information |
| title_full_unstemmed | scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information |
| title_short | scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information |
| title_sort | scmtd a statistical multidimensional imputation method for single cell rna seq data leveraging transcriptome dynamic information |
| topic | Single-cell RNA-seq Multidimensional information Transcriptome dynamic Cell-level Gene-level |
| url | https://doi.org/10.1186/s13578-022-00886-4 |
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