CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data
Abstract Background The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge. Results Here, we p...
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| Format: | Article |
| Language: | English |
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BMC
2021-03-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-021-04054-2 |
| _version_ | 1818426211514187776 |
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| author | Yuting Dai Aining Xu Jianfeng Li Liang Wu Shanhe Yu Jun Chen Weili Zhao Xiao-Jian Sun Jinyan Huang |
| author_facet | Yuting Dai Aining Xu Jianfeng Li Liang Wu Shanhe Yu Jun Chen Weili Zhao Xiao-Jian Sun Jinyan Huang |
| author_sort | Yuting Dai |
| collection | DOAJ |
| description | Abstract Background The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge. Results Here, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner. Conclusions CytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow. |
| first_indexed | 2024-12-14T14:26:14Z |
| format | Article |
| id | doaj.art-ff8ba4a7e2bf471bb17b266ef27514a7 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T14:26:14Z |
| publishDate | 2021-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-ff8ba4a7e2bf471bb17b266ef27514a72022-12-21T22:57:57ZengBMCBMC Bioinformatics1471-21052021-03-0122112010.1186/s12859-021-04054-2CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry dataYuting Dai0Aining Xu1Jianfeng Li2Liang Wu3Shanhe Yu4Jun Chen5Weili Zhao6Xiao-Jian Sun7Jinyan Huang8Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research and Center for Individualized Medicine, Mayo ClinicShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityAbstract Background The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge. Results Here, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner. Conclusions CytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow.https://doi.org/10.1186/s12859-021-04054-2Flow cytometryMass cytometrySingle-cellTreePseudotime |
| spellingShingle | Yuting Dai Aining Xu Jianfeng Li Liang Wu Shanhe Yu Jun Chen Weili Zhao Xiao-Jian Sun Jinyan Huang CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data BMC Bioinformatics Flow cytometry Mass cytometry Single-cell Tree Pseudotime |
| title | CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data |
| title_full | CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data |
| title_fullStr | CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data |
| title_full_unstemmed | CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data |
| title_short | CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data |
| title_sort | cytotree an r bioconductor package for analysis and visualization of flow and mass cytometry data |
| topic | Flow cytometry Mass cytometry Single-cell Tree Pseudotime |
| url | https://doi.org/10.1186/s12859-021-04054-2 |
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