Transformation of multicolour flow cytometry data with OTflow prevents misleading multivariate analysis results and incorrect immunological conclusions.
OTflow
arcsinh | cofactor
flow cytometry
flowVS
inverse hyperbolic sine
logicle transformation
multivariate analysis
preprocessing
transformation
Journal
Cytometry. Part A : the journal of the International Society for Analytical Cytology
ISSN: 1552-4930
Titre abrégé: Cytometry A
Pays: United States
ID NLM: 101235694
Informations de publication
Date de publication:
01 2022
01 2022
Historique:
revised:
28
02
2021
received:
29
09
2020
accepted:
19
07
2021
pubmed:
31
7
2021
medline:
2
2
2022
entrez:
30
7
2021
Statut:
ppublish
Résumé
The rapid evolution of the flow cytometry field, currently allowing the measurement of 30-50 parameters per cell, has led to a marked increase in deep multivariate information. Manual gating is insufficient to extract all this information. Therefore, multivariate analysis (MVA) methods have been developed to extract information and efficiently analyze the high-density multicolour flow cytometry (MFC) data. To aid interpretation, MFC data are often logarithmically transformed before MVA. We studied the consequences of different transformations of flow cytometry data in datasets containing negative intensities caused by background subtractions and spreading error, as logarithmic transformation of negative data is impossible. Transformations such as logicle or hyperbolic arcsine transformations allow linearity around zero, whereas higher (positive and negative) intensities are logarithmically transformed. To define the linear range, a parameter (or cofactor) must be chosen. We show how the chosen transformation parameter has great impact on the MVA results. In some cases, peak splitting is observed, producing two distributions around zero in an actual homogeneous population. This may be misinterpreted as the presence of multiple cell populations. Moreover, when performing arbitrary transformation before MVA analysis, biologically relevant and statistically significant information might be missed. We present a new algorithm, Optimal Transformation for flow cytometry data (OTflow), which uses various statistical methods to optimally choose the parameter of the transformation and prevent artifacts such as peak splitting. Arbitrary or unconsidered transformation can lead to wrong conclusions for the MVA cluster methods, dimensionality reduction methods, and classification methods. We recommend transformation of flow cytometry data by using OTflow-defined parameters estimated per channel, in order to prevent peak splitting and other artifacts in the data.
Identifiants
pubmed: 34327803
doi: 10.1002/cyto.a.24491
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
72-85Informations de copyright
© 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry.
Références
Perfetto SP, Chattopadhyay PK, Roederer M. Innovation: seventeen-colour flow cytometry: unravelling the immune system. Nat Rev Immunol. 2004;4:648-55. https://doi.org/10.1038/nri1416
Chattopadhyay P, Perfetto S, Gaylord B, Stall A, Duckett L, Hill J, et al. Toward 40+ parameter fluorescence flow cytometry. XXIX congress of the International Society for Advancement of cytometry. Ft. Lauderdale, FL.: International Society for Advancement of cytometry;2014. p. 215-216.
Saeys Y, Van Gassen S, Lambrecht BN. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat Rev Immunol. 2016;16:449-62. https://doi.org/10.1038/nri.2016.56
Amir ED, 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;31:545-52. https://doi.org/10.1038/nbt.2594
Jansen JJ, Hilvering B, van den Doel A, Pickkers P, Koenderman L, Buydens LMC, et al. FLOOD: FLow cytometric orthogonal orientation for diagnosis. Chemom Intel Lab Syst. 2016;151(December):126-35. https://doi.org/10.1016/j.chemolab.2015.12.001
Tinnevelt GH, Kokla M, Hilvering B, van Staveren S, Folcarelli R, Xue L, et al. Novel data analysis method for multicolour flow cytometry links variability of multiple markers on single cells to a clinical phenotype. Sci Rep. 2017;7:1-11. https://doi.org/10.1038/s41598-017-05714-1
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;8:1-18. https://doi.org/10.1038/s41598-018-29367-w
Qiu P, Simonds EF, Bendall SC, Gibbs KD Jr, Bruggner RV, Linderman MD, et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol. 2011;29:886-93. https://doi.org/10.1038/nbt.1991
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;87:636-45. https://doi.org/10.1002/cyto.a.22625
Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP. Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci. 2014;111:E2770-7. https://doi.org/10.1073/pnas.1408792111
van den Berg RA, Hoefsloot HCJ, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics. 2006;7. https://doi.org/10.1186/1471-2164-7-142
Verwer B. BD FACSDiVa option (white paper). Becton: Dickinson and Company; 2002. http://www.bdbiosciences.com/ds/is/others/23-6579.pdf
Herzenberg LA, Tung J, Moore WA, Herzenberg LA, Parks DR. Interpreting flow cytometry data: a guide for the perplexed. Nat Immunol. 2006;7:681-5. https://doi.org/10.1038/ni0706-681
Roederer M. Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry. 2001;45:194-205. http://www.ncbi.nlm.nih.gov/pubmed/11746088
Parks DR, Roederer M, Moore WA. A new “logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytom Part A. 2006;69A:541-51. https://doi.org/10.1002/cyto.a.20258
Finak G, Perez J-M, Weng A, Gottardo R. Optimizing transformations for automated, high throughput analysis of flow cytometry data. BMC Bioinformatics. 2010;11:546. https://doi.org/10.1186/1471-2105-11-546
Kotecha N, Krutzik PO, Irish JM. Web-based analysis and publication of flow cytometry experiments. Curr Protoc Cytom. 2010;Chapter 10:Unit10.17. https://doi.org/10.1002/0471142956.cy1017s53
Azad A, Rajwa B, Pothen A. flowVS: channel-specific variance stabilization in flow cytometry. BMC Bioinformatics. 2016;17:1-14. https://doi.org/10.1186/s12859-016-1083-9
Tibshirani R. Estimating transformations for regression via additivity and variance stabilization. J Am Stat Assoc. 1988;83:394-405. https://doi.org/10.1080/01621459.1988.10478610
Bartlett MS. The square root transformation in analysis of variance. R Stat Soc. 1936;3:68-78. https://doi.org/10.2307/2983678
Ray S, Pyne S. A computational framework to emulate the human perspective in flow cytometric data analysis. PLoS One. 2012;7:e35693. https://doi.org/10.1371/journal.pone.0035693
Jarque CM, Bera AK. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ Lett. 1980;6:255-9. https://doi.org/10.1016/0165-1765(80)90024-5
Jolliffe IT. Principal component analysis. Principal Component Analysis. 2nd ed. New York: Springer; 2002. p. 1-405.
Box GEP, Cox DR. An analysis of transformations (with discussion). J R Stat Soc B. 1964;77:209. https://doi.org/10.2307/2287791
Botev ZI, Grotowski JF, Kroese DP. Kernel density estimation via diffusion. Ann Stat. 2010;38:2916-2957. https://doi.org/10.1214/10-AOS799
Find local maxima - MATLAB findpeaks - MathWorks Benelux. https://nl.mathworks.com/help/signal/ref/findpeaks.html. Accessed February 14, 2021.
Jarque CM, Bera AK. A test for normality of observations and regression residuals. Int Stat Rev. 1987;55:163. https://doi.org/10.2307/1403192
Lugli E, Pinti M, Nasi M, Troiano L, Ferraresi R, Mussi C, et al. Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data. Cytom Part A. 2007;71A:334-44. https://doi.org/10.1002/cyto.a.20387
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;24:1927-33. https://doi.org/10.1038/leu.2010.160
Tung JW, Parks DR, Moore WA, Herzenberg LA, Herzenberg LA. New approaches to fluorescence compensation and visualization of FACS data. Clin Immunol. 2004;110:277-83. https://doi.org/10.1016/j.clim.2003.11.016
Bagwell CB. HyperLog - A flexible log-like transform for negative, zero, and positive valued data. Cytom Part A. 2005;64:34-42. https://doi.org/10.1002/cyto.a.20114
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. https://doi.org/10.1186/1471-2105-10-106
Bioconductor - flowCore. https://www.bioconductor.org/packages/release/bioc/html/flowCore.html. Accessed February 14, 2021.
Qian Y, Liu Y, Campbell J, Thomson E, Kong YM, Scheuermann RH. FCSTrans: an open source software system for FCS file conversion and data transformation. Cytom Part A. 2012;81:353-356. https://doi.org/10.1002/cyto.a.22037
Lemster BH, Michel JJ, Montag DT, Paat JJ, Studenski SA, Newman AB, et al. Induction of CD56 and TCR-independent activation of T cells with aging. J Immunol. 2008;180:1979-90. https://doi.org/10.4049/jimmunol.180.3.1979
Potter SJ, Lacabaratz C, Lambotte O, Perez-Patrigeon S, Vingert B̂, Sinet M, et al. Preserved central memory and activated effector memory CD4+ T-cell subsets in human immunodeficiency virus controllers: an ANRS EP36 study. J Virol. 2007;81:13904-15. https://doi.org/10.1128/JVI.01401-07
Vassena L, Giuliani E, Buonomini AR, Malagnino V, Andreoni M, Doria M. Brief report: L-selectin (CD62L) is downregulated on CD4+and CD8+T lymphocytes of HIV-1-infected individuals naive for ART. J Acquir Immune Defic Syndr. 2016;72:492-7. https://doi.org/10.1097/QAI.0000000000000999
Kononchik J, Ireland J, Zou Z, Segura J, Holzapfel G, Chastain A, et al. HIV-1 targets L-selectin for adhesion and induces its shedding for viral release. Nat Commun. 2018;9:1-15. https://doi.org/10.1038/s41467-018-05197-2
Robinson JP, Roederer M. Flow cytometry strikes gold. Science. 2015;350:739-40. https://doi.org/10.1126/science.aad6770