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Tytuł pozycji:

Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre.

Tytuł :
Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre.
Autorzy :
Ashhurst TM; Sydney Cytometry Core Research Facility, Charles Perkins Centre, Centenary Institute and The University of Sydney, Sydney, New South Wales, Australia.; Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
Marsh-Wakefield F; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Vascular Immunology Unit, Department of Pathology, The University of Sydney, Sydney, New South Wales, Australia.
Putri GH; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.; School of Computer Science, The University of Sydney, Sydney, New South Wales, Australia.
Spiteri AG; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.; Viral Immunopathology Laboratory, Discipline of Pathology, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Shinko D; Sydney Cytometry Core Research Facility, Charles Perkins Centre, Centenary Institute and The University of Sydney, Sydney, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
Read MN; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.; School of Computer Science, The University of Sydney, Sydney, New South Wales, Australia.; The Westmead Initiative, The University of Sydney, Sydney, New South Wales, Australia.
Smith AL; Sydney Cytometry Core Research Facility, Charles Perkins Centre, Centenary Institute and The University of Sydney, Sydney, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
King NJC; Sydney Cytometry Core Research Facility, Charles Perkins Centre, Centenary Institute and The University of Sydney, Sydney, New South Wales, Australia.; Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, New South Wales, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.; Viral Immunopathology Laboratory, Discipline of Pathology, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Sydney Nano, The University of Sydney, Sydney, New South Wales, Australia.
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Źródło :
Cytometry. Part A : the journal of the International Society for Analytical Cytology [Cytometry A] 2021 Apr 10. Date of Electronic Publication: 2021 Apr 10.
Publication Model :
Ahead of Print
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Original Publication: Hoboken, N.J. : Wiley-Liss, c2002-
References :
Bendall SC, Davis KL, Amir EAD, Tadmor MD, Simonds EF, Chen TJ, et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell. 2014;157(3):714-25.
Park LM, Lannigan J, Jaimes MC. OMIP-069: forty-color full spectrum flow cytometry panel for deep Immunophenotyping of major cell subsets in human peripheral blood. Cytometry A. 2020;97(10):1044-51.
Mair F, Prlic M. OMIP-044: 28-color immunophenotyping of the human dendritic cell compartment. Cytometry A. 2018;93(4):402-5.
Nettey L, Giles AJ, Chattopadhyay PK. OMIP-050: a 28-color/30-parameter fluorescence flow cytometry panel to enumerate and characterize cells expressing a wide Array of immune checkpoint molecules. Cytometry A. 2018;93(11):1094-6.
Mair F, Hartmann FJ, Mrdjen D, Tosevski V, Krieg C, Becher B. The end of gating? An introduction to automated analysis of high dimensional cytometry data. Eur J Immunol. 2016;46(1):34-43.
Brinkman RR. Improving the rigor and reproducibility of flow cytometry-based clinical research and trials through automated data analysis. Cytometry A. 2020;97(2):107-12.
Levine JH, Simonds EF, Bendall SC, Davis KL, Amir EAD, Tadmor MD, et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell. 2015;162(1):184-97.
Van Gassen S, Callebaut B, Van Helden MJ, Lambrecht BN, Demeester P, Dhaene T, et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A. 2015;87(7):636-45.
Samusik N, Good Z, Spitzer MH, Davis KL, Nolan GP. Automated mapping of phenotype space with single-cell data. Nat Methods. 2016;13(6):493-6.
van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mac Learn Res. 2008;9:2579-605.
van der Maaten L. Accelerating t-SNE using tree-based algorithms. J Mac Learn Res. 2014;15:3221-45.
McInnes L, Healy J, James Melville J. UMAP: uniform manifold approximation and projection for dimension reduction; 2018. arXiv, 2018: p. 1802.03426.
Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM, Kathail P, et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat Biotechnol. 2016;34(6):637-45.
Li H, Shaham U, Stanton KP, Yao Y, Montgomery RR, Kluger Y. Gating mass cytometry data by deep learning. Bioinformatics. 2017;33(21):3423-30.
Chen Y, Lakshmikanth T, Mikes J, Brodin P. Single-cell classification using learned cell phenotypes. 2020. bioRxiv 2020.07.22.216002.
Kaushik A, Dunham D, He Z, Manohar M, Desai M, Nadeau K, Less SA. CyAnno: a semi-automated approach for cell type annotation of mass cytometry datasets; 2020. bioRxiv. 2020.08.28.272559.
Chen H, Lau MC, Wong MT, Newell EW, Poidinger M, Chen J. Cytofkit: a bioconductor package for an integrated mass Cytometry data analysis pipeline. PLoS Comput Biol. 2016;12(9):e1005112.
Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP. Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci USA. 2014;111(26):E2770-7.
Chevrier S, Crowell HL, Zanotelli VRT, Engler S, Robinson MD, Bodenmiller B. Compensation of signal spillover in suspension and imaging mass Cytometry. Cell Syst. 2018;6(5):612-20.e5.
Nowicka M, Krieg C, Crowell HL, Weber LM, Hartmann FJ, Guglietta S, et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Res. 2017;6:748.
Weber LM, Nowicka M, Soneson C, Robinson MD. Diffcyt: differential discovery in high-dimensional cytometry via high-resolution clustering. Commun Biol. 2019;2:183.
Van P, Jiang W, Gottardo R, Finak G. ggCyto: next generation open-source visualization software for cytometry. Bioinformatics. 2018;34(22):3951-3.
Finak G, Frelinger J, Jiang W, Newell EW, Ramey J, Davis MM, et al. OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. PLoS Comput Biol. 2014;10(8):e1003806.
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-20.
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM III, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888-902.e21.
Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN, Geistlinger L, et al. Orchestrating single-cell analysis with bioconductor. Nat Methods. 2020;17(2):137-45.
Zeng C, Mulas F, Sui Y, Guan T, Miller N, Tan Y, et al. Pseudotemporal ordering of single cells reveals metabolic control of postnatal beta cell proliferation. Cell Metab. 2017;25(5):1160-75.e11.
Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS, Goh M, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 2020;21(1):12.
Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, et al. The human cell atlas. Elife. 2017;6:e27041.
Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods. 2019;16(12):1289-96.
Hao Y, Hao S, Andersen-Nissen E, Mauck III WM, Zheng S, et al. Integrated analysis of multimodal single-cell data. 2020. bioRxiv.2020.10.12.335331.
Lin Y, Ghazanfar S, Wang KYX, Gagnon-Bartsch JA, Lo KK, Su X, et al. scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Proc Natl Acad Sci USA. 2019;116(20):9775-84.
Hahne F, Khodabakhshi AH, Bashashati A, Wong CJ, Gascoyne RD, Weng AP, et al. Per-channel basis normalization methods for flow cytometry data. Cytometry A. 2010;77(2):121-31.
Schuyler RP, Jackson C, Garcia-Perez JE, Baxter RM, Ogolla S, Rochford R, et al. Minimizing batch effects in mass Cytometry data. Front Immunol. 2019;10:2367.
Van Gassen S, Gaudilliere B, Angst MS, Saeys Y, Aghaeepour N. CytoNorm: a normalization algorithm for Cytometry data. Cytometry A. 2020;97(3):268-78.
Trussart M, Teh CE, Tan T, Leong L, Gray DHD, Speed TP. Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets. Elife. 2020;9:e59630.
Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, et al. flowCore: a bioconductor package for high throughput flow cytometry. BMC Bioinformatics. 2009;10:106.
Ashhurst TM, Cox DA, Smith AL, King NJC. Analysis of the murine bone marrow hematopoietic system using mass and flow Cytometry. Methods Mol Biol. 2019;1989:159-92.
Koutsakos M, Rowntree LC, Hensen L, Chua BY, van de Sandt CE, Habel JR, et al. Integrated immune dynamics define correlates of COVID-19 severity and antibody responses. Cell Rep Med. 2021;2:100208.
Niewold P, Ashhurst TM, Smith AL, King NJC. Evaluating spectral cytometry for immune profiling in viral disease. Cytometry A. 2020;97:1165-79.
Monaco G, Chen H, Poidinger M, Chen J, de Magalhães JP, Larbi A. flowAI: automatic and interactive anomaly discerning tools for flow cytometry data. Bioinformatics. 2016;32(16):2473-80.
Dowle M, Srinivasan A. data.table: extension of data.frame. R package version 1.13.0; 2020. https://CRAN.R-project.org/package=data.table.
Parks DR, Roederer M, Moore WA. A new "Logicle" display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytometry A. 2006;69(6):541-51.
Van Gassen S, Callebaut B, Saeys Y. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data; 2020. http://bioconductor.org/packages/release/bioc/html/FlowSOM.html.
Krijthe JH. Rtsne: T-distributed stochastic neighbor embedding using a barnes-hut implementation; 2015. https://github.com/jkrijthe/Rtsne.
Linderman GC, Rachh M, Hoskins JG, Steinerberger S, Kluger Y. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nat Methods. 2019;16(3):243-5.
Konopka T. umap: uniform manifold approximation and projection. R package version 0.2.5.0; 2020. https://CRAN.R-project.org/package=umap.
Team RC. R: a language and environment for statistical computing; 2020. https://www.R-project.org/.
Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016. https://ggplot2.tidyverse.org.
Kolde R. pheatmap: pretty heatmaps. R package version 1.0.12; 2019. https://CRAN.R-project.org/package=pheatmap.
Kassambara A. ggpubr: 'ggplot2' based publication ready plots. R package version 0.4.0; 2020. https://CRAN.R-project.org/package=ggpubr.
Blighe K, Rana S, Lewis M. EnhancedVolcano: publication-ready volcano plots with enhanced colouring and labeling. R package version 1.6.0; 2020. https://github.com/kevinblighe/EnhancedVolcano.
Beygelzimer A, Kakadet S, Langford J, Arya S, Mount D, Li S. FNN: fast nearest neighbor search algorithms and applications. R package version 1.1.3; 2019. https://CRAN.R-project.org/package=FNN.
Kuhn M. caret: classification and regression training. R package version 6.0-86; 2020. https://CRAN.R-project.org/package=caret.
Moore WA, Parks DR. Update for the logicle data scale including operational code implementations. Cytometry A. 2012;81(4):273-7.
Bendall SC, Simonds EF, Qiu P, Amir EAD, Krutzik PO, Finck R, et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science. 2011;332(6030):687-96.
Weber LM, Robinson MD. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A. 2016;89(12):1084-96.
Belkina AC, Ciccolella CO, Anno R, Halpert R, Spidlen J, Snyder-Cappione JE. Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nat Commun. 2019;10(1):5415.
van Unen V, Höllt T, Pezzotti N, Li N, Reinders MJT, Eisemann E, et al. Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types. Nat Commun. 2017;8(1):1740.
Vuckovic S, Bryant CE, Lau KHA, Yang S, Favaloro J, McGuire HM, et al. Inverse relationship between oligoclonal expanded CD69- TTE and CD69+ TTE cells in bone marrow of multiple myeloma patients. Blood Adv. 2020;4(19):4593-604.
Marsh-Wakefield F, Ashhurst T, Trend S, McGuire H, Juillard P, Zinger A, et al. IgG3 (+) B cells are associated with the development of multiple sclerosis. Clin Transl Immunology. 2020;9(5):e01133.
Marsh-Wakefield F, Kruzins A, McGuire HM, Yang S, Bryant C, Fazekas de St. Groth B, et al. Mass Cytometry discovers two discrete subsets of CD39(−)Treg which discriminate MGUS from multiple myeloma. Front Immunol. 2019;10:1596.
Shinko D, McGuire HM, Diakos CI, Pavlakis N, Clarke SJ, Byrne SN, et al. Mass Cytometry reveals a sustained reduction in CD16(+) natural killer cells following chemotherapy in colorectal cancer patients. Front Immunol. 2019;10:2584.
Hayashida E, Ling ZL, Ashhurst TM, Viengkhou B, Jung SR, Songkhunawej P, et al. Zika virus encephalitis in immunocompetent mice is dominated by innate immune cells and does not require T or B cells. J Neuroinflammation. 2019;16(1):177.
Wickham H, François R, Henry L, Müller K. dplyr: a grammar of data manipulation. R package version 0.8.5; 2020. https://CRAN.R-project.org/package=dplyr.
Belson WA. Matching and prediction on the principle of biological classification. J R Stat Soc Ser C Appl Stat. 1959;8(2):65-75.
Morgan M. BiocManager: access the bioconductor project package repository. R package version 1.30.10; 2019. https://CRAN.R-project.org/package=BiocManager.
Wickham H, Hester J, Chang W. devtools: tools to make developing R packages easier. R package version 2.3.0; 2020. https://CRAN.R-project.org/package=devtools.
Felter W,Ferreira A, Rajamony R, Rubio J. An updated performance comparison of virtual machines and Linux containers. 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS); 2015. p. 171-172.
Mersmann O. Microbenchmark: accurate timing functions. R package version 1.4-7. CRAN; 2019.
Grant Information :
Marie Bashir Institute for Infectious Disease and Biosecurity; Merridew Foundation; 1088242 National Health and Medical Research Council (NH&MRC); Marie Bashir Institute, University of Sydney
Contributed Indexing :
Keywords: FlowSOM; UMAP; clustering; computational analysis; dimensionality reduction; high-dimensional cytometry; mass cytometry; spectral cytometry; t-SNE
Entry Date(s) :
Date Created: 20210411 Latest Revision: 20210426
Update Code :
20210623
DOI :
10.1002/cyto.a.24350
PMID :
33840138
Czasopismo naukowe
As the size and complexity of high-dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end-to-end integration and analysis of HD cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre-processing, batch alignment, data integration, clustering, dimensionality reduction, visualization, and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large HD datasets, generated by flow cytometry, mass cytometry, or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single-cell RNA sequencing or HD imaging technologies, such as Imaging Mass Cytometry. The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).
(© 2021 International Society for Advancement of Cytometry.)

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