Current trends in flow cytometry automated data analysis software.

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Abstrakt

Tytuł:: Current trends in flow cytometry automated data analysis software.
Autorzy:: Cheung M; Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom.
Campbell JJ; National Measurement Laboratory, LGC, Teddington, United Kingdom.
Whitby L; UK NEQAS for Leucocyte Immunophenotyping, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.
Thomas RJ; Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom.
Braybrook J; National Measurement Laboratory, LGC, Teddington, United Kingdom.
Petzing J; Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom.
Źródło:: Cytometry. Part A : the journal of the International Society for Analytical Cytology [Cytometry A] 2021 Oct; Vol. 99 (10), pp. 1007-1021. Date of Electronic Publication: 2021 Feb 19.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't; Review
Język:: English
Imprint Name(s):: Original Publication: Hoboken, N.J. : Wiley-Liss, c2002-
MeSH Terms:: Data Analysis*
Software*
Algorithms ; Cluster Analysis ; Flow Cytometry ; Immunophenotyping ; Reproducibility of Results
References:: Maecker HT, Rinfret A, D'Souza P, Darden J, Roig E, Landry C, et al. Standardization of cytokine flow cytometry assays. BMC Immunol. 2005;6:1-18.
Grant R, Coopman K, Medcalf N, Silva-Gomes S, Campbell JJ, Kara B, et al. Understanding the contribution of operator measurement variability within flow cytometry data analysis for quality control of cell and gene therapy manufacturing. Measurement. 2020;150:106998.
Grant R, Coopman K, Medcalf N, Silva-Gomes S, Kara B, Campbell JJ, et al. Quantifying operator subjectivity within flow cytometry data analysis as a source of measurement uncertainty and the impact of experience on results. PDA J Pharm Sci Technol. 2020;pdajpst.2019.011213. https://doi.org/10.5731/pdajpst.2019.011213.
Bendall SC, Nolan GP, Roederer M, Chattopadhyay PK. A deep profiler's guide to cytometry. Trends Immunol. 2012;33(7):323-32.
Bendall SC, Simonds EF, Qiu P, El-ad DA, Krutzik PO, Finck R, et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science (80-). 2011;332(6030):687-96.
Mair F, Prlic M. OMIP-044: 28-color immunophenotyping of the human dendritic cell compartment. Cytom Part A. 2018;93(4):402-5.
O'Neill K, Aghaeepour N, Špidlen J, Brinkman R. Flow cytometry bioinformatics. PLoS Comput Biol. 2013;9(12).
Bashashati A, Brinkman RRA. Survey of flow cytometry data analysis methods. Adv Bioinforma. 2009;2009:1-19.
Finak G, Langweiler M, Jaimes M, Malek M, Taghiyar J, Korin Y, et al. Standardizing flow cytometry Immunophenotyping analysis from the human ImmunoPhenotyping consortium. Sci Rep. 2016;6:1-11.
Aghaeepour N, Finak G, Hoos H, Mosmann TR, Brinkman R, Gottardo R, et al. Critical assessment of automated flow cytometry data analysis techniques. Nat Methods. 2013;10(3):228-38.
Aghaeepour N, Chattopadhyay P, Chikina M, Dhaene T, Van Gassen S, Kursa M, et al. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytom Part A. 2016;89(1):16-21.
Weber LM, Nowicka M, Soneson C, Robinson MD. diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering.. Commun Biol. 2019;2(183):1-11.
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. Cytom Part 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.
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.
Aghaeepour N, Nikolic R, Hoos HH, Brinkman RR. Rapid cell population identification in flow cytometry data. Cytom Part A. 2011;79 A(1):6-13.
Pedersen NW, Chandran PA, Qian Y, Rebhahn J, Petersen NV, Hoff MD, et al. Automated analysis of flow cytometry data to reduce inter-lab variation in the detection of major histocompatibility complex multimer-binding T cells. Front Immunol. 2017;8:1-12.
Melchiotti R, Gracio F, Kordasti S, Todd AK, de Rinaldis E. Cluster stability in the analysis of mass cytometry data. Cytom Part A. 2017;91(1):73-84.
Czechowska K, Lannigan J, Aghaeepour N, Back JB, Begum J, Behbehani G, et al. Cyt-Geist: current and future challenges in cytometry: reports of the CYTO 2019 conference workshops. Cytom Part A. 2019;95(12):1236-74.
Saeys Y, Van Gassen S, Lambrecht BN. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat Rev Immunol. 2016;16(7):449-62.
Zunder ER, Lujan E, Goltsev Y, Wernig M, Nolan GP. A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell. 2015;16(3):323-37.
Spitzer MH, Gherardini PF, Fragiadakis GK, Bhattacharya N, Yuan RT, Hotson AN, et al. An interactive reference framework for modeling a dynamic immune system. Science (80-). 2015;349(6244).
Maaten L, Hinton G. Visualizing Data using t-SNE. J Mach Learn Res. 2008;9(Nov):2579-605.
Kotecha N, Krutzik PO, Irish JM. Web-based analysis and publication of flow cytometry experiments. Curr Protoc Cytom. 2010;53(1):10-17.
Becher B, Schlitzer A, Chen J, Mair F, Sumatoh HR, Teng KWW, et al. High-dimensional analysis of the murine myeloid cell system. Nat Immunol. 2014;15(12):1181-9.
Malek M, Taghiyar MJ, Chong L, Finak G, Gottardo R, Brinkman RR. FlowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification. Bioinformatics. 2015;31(4):606-7.
Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP. Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci. 2014;111(26):E2770-7.
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):1-12.
Lin L, Finak G, Ushey K, Seshadri C, Hawn TR, Frahm N, et al. COMPASS identifies T-cell subsets correlated with clinical outcomes. Nat Biotechnol. 2015;33(6):610-6.
O'Neill K, Jalali A, Aghaeepour N, Hoos H, Brinkman RR. Enhanced flowType/RchyOptimyx: a bioconductor pipeline for discovery in high-dimensional cytometry data. Bioinformatics. 2014;30(9):1329-30.
Dundar M, Akova F, Yerebakan HZ, Rajwa B. A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects. BMC Bioinformat. 2014;15(1):1-15.
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.
Van Gassen S, Vens C, Dhaene T, Lambrecht BN, Saeys Y. FloReMi: flow density survival regression using minimal feature redundancy. Cytom Part A. 2016;89(1):22-9.
Lux M, Brinkman RR, Chauve C, Laing A, Lorenc A, Abeler-Dörner L, et al. FlowLearn: fast and precise identification and quality checking of cell populations in flow cytometry. Bioinformatics. 2018;34(13):2245-53.
Lee HC, Kosoy R, Becker CE, Dudley JT, Kidd BA. Automated cell type discovery and classification through knowledge transfer. Bioinformatics. 2017;33(11):1689-95.
Rebhahn JA, Roumanes DR, Qi Y, Khan A, Thakar J, Rosenberg A, et al. Competitive SWIFT cluster templates enhance detection of aging changes. Cytom Part A. 2016;89(1):59-70.
Lee AJ, Chang I, Burel JG, Lindestam Arlehamn CS, Mandava A, Weiskopf D, et al. DAFi: a directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data. Cytom Part A. 2018;93(6):597-610.
Weber LM, Nowicka M, Soneson C, Robinson MD. diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering. Commun. Biol. 2019;2(183):1-11.
Reiter M, Rota P, Kleber F, Diem M, Groeneveld-Krentz S, Dworzak M. Clustering of cell populations in flow cytometry data using a combination of Gaussian mixtures. Pattern Recogn. 2016;60:1029-40.
Abdelaal T, van Unen V, Höllt T, Koning F, Reinders MJT, Mahfouz A. Predicting cell populations in single cell mass cytometry data. Cytom Part A. 2019;95(7):769-81.
Wong L, Hill BL, Hunsberger BC, Bagwell CB, Curtis AD, Davis BH. Automated analysis of flow cytometric data for measuring neutrophil CD64 expression using a multi-instrument compatible probability state model. Cytom Part B - Clin Cytom. 2015;88(4):227-35.
Xu D, Tian Y. A comprehensive survey of clustering algorithms. Ann Data Sci. 2015;2(2):165-93.
Kaufman L, Rousseeuw PJ. In: Kaufman L, Rousseeuw PJ, editors. Finding groups in data: an introduction to cluster analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 1990 (Wiley Series in Probability and Statistics).
Qiu P, Simonds EF, Bendall SC, Gibbs KD, Bruggner RV, Linderman MD, et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol. 2011;29(10):886-93.
Qiu P. Toward deterministic and semiautomated SPADE analysis. Cytom Part A. 2017;91(3):281-9.
Jain AK. Data clustering: 50 years beyond K-means. Pattern Recogn Lett. 2010 Jun 1;31(8):651-66.
Ge Y, Sealfon SC. Flowpeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding. Bioinformatics. 2012;28(15):2052-8.
Pouyan MB, Nourani M. Identifying cell populations in flow cytometry data using phenotypic signatures. IEEE/ACM Trans Comput Biol Bioinforma. 2017;14(4):880-91.
Ester M, Kriegel H-P, Sander J, Xu X. A Density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd international conference on knowledge discovery and data mining. Vol. 96, 34th ed. Menlo Park, California: AAAI Press; 1996. p. 226-31.
Ankerst M, Breunig MM, Kriegel HP, Sander J. OPTICS: ordering points to identify the clustering structure. SIGMOD Rec (ACM Spec Interes Gr Manag Data). 1999;28(2):49-60.
Shekhar K, Brodin P, Davis MM, Chakraborty AK. Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). Proc Natl Acad Sci. 2014;111(1):202-7.
Qian Y, Wei C, Eun-Hyung Lee F, Campbell J, Halliley J, Lee JA, et al. Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data. Cytom Part B Clin Cytom. 2010;78B(S1):S69-82.
Sugar IP, Sealfon SC. Misty Mountain clustering: application to fast unsupervised flow cytometry gating. BMC Bioinformat. 2010;11(1):502.
Folcarelli R, van Staveren S, Bouman R, Hilvering B, Tinnevelt GH, Postma G, et al. Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm. Sci Rep. 2018;8(1):1-18.
Zaunders J, Jing J, Leipold M, Maecker H, Kelleher AD, Koch I. Computationally efficient multidimensional analysis of complex flow cytometry data using second order polynomial histograms. Cytom Part A. 2016;89(1):44-58.
Meehan S, Kolyagin GA, Parks D, Youngyunpipatkul J, Herzenberg LA, Walther G, et al. Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization. Commun Biol. 2019;2(1):1-12.
Fraley C, Raftery AE. Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc. 2002;97(458):611-31.
Pyne S, Hu X, Wang K, Rossin E, Lin T-I, Maier LM, et al. Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci. 2009;106(21):8519-24.
Lo K, Hahne F, Brinkman RR, Gottardo R. flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinformat. 2009;10(1):145.
Finak G, Bashashati A, Brinkman R, Gottardo R. Merging mixture components for cell population identification in flow cytometry. Adv Bioinforma. 2009;2009:1-12.
Naim I, Datta S, Rebhahn J, Cavenaugh JS, Mosmann TR, Sharma G. SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design. Cytom Part A. 2014;85(5):408-21.
Mosmann TR, Naim I, Rebhahn J, Datta S, Cavenaugh JS, Weaver JM, et al. SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation. Cytom Part A. 2014;85(5):422-33.
Boedigheimer MJ, Ferbas J. Mixture modeling approach to flow cytometry data. Cytom Part A. 2008;73(5):421-9.
Cron A, Gouttefangeas C, Frelinger J, Lin L, Singh SK, Britten CM, et al. Hierarchical Modeling for rare event detection and cell subset alignment across flow cytometry samples. Altan-bonnet G. PLoS Comput Biol. 2013 Jul 11;9(7):e1003130.
Rogers WT, Moser AR, Holyst HA, Bantly A, Mohler ER, Scangas G, et al. Cytometric fingerprinting: quantitative characterization of multivariate distributions. Cytom Part A. 2008;73(5):430-41.
Sörensen T, Baumgart S, Durek P, Grützkau A, Häupl T. immunoClust-an automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytom Part A. 2015;87(7):603-15.
Pyne S, Lee SX, Wang K, Irish J, Tamayo P, Nazaire MD, et al. Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. PLoS One. 2014;9(7):e100334.
Anchang B, Do MT, Zhao X, Plevritis SK. CCAST: a model-based gating strategy to isolate homogeneous subpopulations in a heterogeneous population of single cells. PLoS Comput Biol. 2014;10(7):13-7.
Commenges D, Alkhassim C, Gottardo R, Hejblum B, Thiébaut R. Cytometree: a binary tree algorithm for automatic gating in cytometry analysis. Cytom Part A. 2018;93(11):1132-40.
Hejblum BP, Alkhassim C, Gottardo R, Caron F, Thiébaut R. Sequential Dirichlet process mixtures of multivariate skew t-distributions for model-based clustering of flow cytometry data. Ann Appl Stat. 2019;13(1):638-60.
Von Luxburg U. A tutorial on spectral clustering. Stat Comput. 2007 Dec 22;17(4):395-416.
Zare H, Shooshtari P, Gupta A, Brinkman RR. Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformat. 2010;11:403.
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.
Kohonen T. The self-organizing map. Neurocomputing. 1998;21(1-3):1-6.
Kohonen T. Essentials of the self-organizing map. Neural Netw. 2013 Jan 1;37:52-65.
Vesanto J, Alhoniemi E. Clustering of the self-organizing map. IEEE Trans Neural Netw. 2000;11:586-600.
Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2019;37(1):38-47.
Amir EAD, Davis KL, Tadmor MD, Simonds EF, Levine JH, Bendall SC, et al. ViSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol. 2013;31(6):545-52.
Diggins KE, Brent Ferrell P, Irish JM. Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data. Methods. 2015;82:55-63.
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):1-10.
Moon KR, van Dijk D, Wang Z, Gigante S, Burkhardt DB, Chen WS, et al. Visualizing structure and transitions in high-dimensional biological data. Nat Biotechnol. 2019;37(12):1482-92.
Fletez-Brant K, Špidlen J, Brinkman RR, Roederer M, Chattopadhyay PK. flowClean: automated identification and removal of fluorescence anomalies in flow cytometry data. Cytom Part A. 2016;89(5):461-71.
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.
Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, et al. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformat. 2009;10(1):1-8.
Hahne F, Gopalakrishnan N, Khodabakhshi AH, Wong C, Lee K. flowStats: statistical methods for the analysis of flow cytometry data. R Package version. 2009;3(1).
Finak G, Jiang M. flowWorkspace: infrastructure for representing and interacting with the gated cytometry. R Package version. 2011;3(3).
Pedersen MJ, Nielsen CV. Improving survey response rates in online panels: effects of Low-cost incentives and cost-free text appeal interventions. Soc Sci Comput Rev. 2016;34(2):229-43.
Meehan S, Walther G, Moore W, Orlova D, Meehan C, Parks D, Ghosn E, Philips M, Mitsunaga E, Waters J, Kantor A, Okamura R, Owumi S, Yang Y, Herzenberg LA, Herzenberg LA. AutoGate: automating analysis of flow cytometry data. Immunol Res. 2014;58(2-3):218-223.
Brooke J. SUS-A quick and dirty usability scale. Usability Eval Ind. 1996;189(194):4-7.
Bangor A, Kortum PT, Miller JT. An empirical evaluation of the system usability scale. Int J Hum Comput Interact. 2008;24(6):574-94.
Kalina T, Flores-Montero J, Van Der Velden VHJ, Martin-Ayuso M, Böttcher S, Ritgen M, et al. EuroFlow standardization of flow cytometer instrument settings and immunophenotyping protocols. Leukemia. 2012;26(9):1986-2010.
Costa ES, Pedreira CE, Barrena S, Lecrevisse Q, Flores J, Quijano S, et al. Automated pattern-guided principal component analysis vs expert-based immunophenotypic classification of B-cell chronic lymphoproliferative disorders: a step forward in the standardization of clinical immunophenotyping. Leukemia. 2010;24(11):1927-33.
Lhermitte L, Mejstrikova E, Van Der Sluijs-Gelling AJ, Grigore GE, Sedek L, Bras AE, et al. Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia. Leukemia. 2018;32(4):874-81.
Van Dongen JJM, Lhermitte L, Böttcher S, Almeida J, Van Der Velden VHJ, Flores-Montero J, et al. EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes. Leukemia. 2012;26(9):1908-75.
Sutherland DR, Anderson L, Keeney M, Nayar R, Chin-Yee I. The ISHAGE guidelines for CD34+ cell determination by flow cytometry. J Hematother. 1996;5(3):213-26.
ISO 15189:2012 Medical laboratories - Requirements for quality and competence.
Grant Information:: MR/R015724/1 United Kingdom MRC_ Medical Research Council; EP/L105072/1 EPSRC/MRC Doctoral Training Centre for Regenerative Medicine at Loughborough University; UK NEQAS; LGC
Contributed Indexing:: Keywords: automation; cell therapy; data analysis; flow cytometry; gating; software
Entry Date(s):: Date Created: 20210219 Date Completed: 20211028 Latest Revision: 20220317
Update Code:: 20240105
DOI:: 10.1002/cyto.a.24320
PMID:: 33606354
: Czasopismo naukowe

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Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take-up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t-distributed stochastic neighbor embedding (t-SNE) and its initial Matlab-based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K-Means, hierarchical, density-based, model-based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.
(© 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.)

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