Algorithmic Tools for Mining High-Dimensional Cytometry Data

Overview

Journal J Immunol

Specialty Allergy & Immunology

Date 2015 Jul 19

PMID 26188071

Citations 49

Authors

Cariad Chester

Holden T Maecker

Affiliations

Soon will be listed here.

Abstract

The advent of mass cytometry has led to an unprecedented increase in the number of analytes measured in individual cells, thereby increasing the complexity and information content of cytometric data. Although this technology is ideally suited to the detailed examination of the immune system, the applicability of the different methods for analyzing such complex data is less clear. Conventional data analysis by manual gating of cells in biaxial dot plots is often subjective, time consuming, and neglectful of much of the information contained in a highly dimensional cytometric dataset. Algorithmic data mining has the promise to eliminate these concerns, and several such tools have been applied recently to mass cytometry data. We review computational data mining tools that have been used to analyze mass cytometry data, outline their differences, and comment on their strengths and limitations. This review will help immunologists to identify suitable algorithmic tools for their particular projects.

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References

Newell E, Sigal N, Bendall S, Nolan G, Davis M . Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity. 2012; 36(1):142-52. PMC: 3752833. DOI: 10.1016/j.immuni.2012.01.002. View

Leipold M, Maecker H . Mass cytometry: protocol for daily tuning and running cell samples on a CyTOF mass cytometer. J Vis Exp. 2012; (69):e4398. PMC: 3499083. DOI: 10.3791/4398. View

Lugli E, Roederer M, Cossarizza A . Data analysis in flow cytometry: the future just started. Cytometry A. 2010; 77(7):705-13. PMC: 2909632. DOI: 10.1002/cyto.a.20901. View

Bendall S, Davis K, Amir E, Tadmor M, Simonds E, Chen T . Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell. 2014; 157(3):714-25. PMC: 4045247. DOI: 10.1016/j.cell.2014.04.005. View

Aghaeepour N, Finak G, Hoos H, Mosmann T, Brinkman R, Gottardo R . Critical assessment of automated flow cytometry data analysis techniques. Nat Methods. 2013; 10(3):228-38. PMC: 3906045. DOI: 10.1038/nmeth.2365. View

Roederer M, Nozzi J, Nason M . SPICE: exploration and analysis of post-cytometric complex multivariate datasets. Cytometry A. 2011; 79(2):167-74. PMC: 3072288. DOI: 10.1002/cyto.a.21015. View

Pyne S, Hu X, Wang K, Rossin E, Lin T, Maier L . Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci U S A. 2009; 106(21):8519-24. PMC: 2682540. DOI: 10.1073/pnas.0903028106. View

Aghaeepour N, Jalali A, ONeill K, Chattopadhyay P, Roederer M, Hoos H . RchyOptimyx: cellular hierarchy optimization for flow cytometry. Cytometry A. 2012; 81(12):1022-30. PMC: 3726344. DOI: 10.1002/cyto.a.22209. View

Finck R, Simonds E, Jager A, Krishnaswamy S, Sachs K, Fantl W . Normalization of mass cytometry data with bead standards. Cytometry A. 2013; 83(5):483-94. PMC: 3688049. DOI: 10.1002/cyto.a.22271. View

10.

Mei H, Leipold M, Schulz A, Chester C, Maecker H . Barcoding of live human peripheral blood mononuclear cells for multiplexed mass cytometry. J Immunol. 2015; 194(4):2022-31. PMC: 4323739. DOI: 10.4049/jimmunol.1402661. View

11.

Zunder E, Lujan E, Goltsev Y, Wernig M, Nolan G . A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell. 2015; 16(3):323-37. PMC: 4401090. DOI: 10.1016/j.stem.2015.01.015. View

12.

Inokuma M, Maino V, Bagwell C . Probability state modeling of memory CD8⁺ T-cell differentiation. J Immunol Methods. 2013; 397(1-2):8-17. DOI: 10.1016/j.jim.2013.08.003. View

13.

Maecker H, McCoy J, Nussenblatt R . Standardizing immunophenotyping for the Human Immunology Project. Nat Rev Immunol. 2012; 12(3):191-200. PMC: 3409649. DOI: 10.1038/nri3158. View

14.

Gaudilliere B, Fragiadakis G, Bruggner R, Nicolau M, Finck R, Tingle M . Clinical recovery from surgery correlates with single-cell immune signatures. Sci Transl Med. 2014; 6(255):255ra131. PMC: 4334126. DOI: 10.1126/scitranslmed.3009701. View

15.

Naim I, Datta S, Rebhahn J, Cavenaugh J, Mosmann T, Sharma G . SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design. Cytometry A. 2014; 85(5):408-21. PMC: 4238829. DOI: 10.1002/cyto.a.22446. View

16.

Amir E, Davis K, Tadmor M, Simonds E, Levine J, Bendall S . viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol. 2013; 31(6):545-52. PMC: 4076922. DOI: 10.1038/nbt.2594. View

17.

Shekhar K, Brodin P, Davis M, Chakraborty A . Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE). Proc Natl Acad Sci U S A. 2013; 111(1):202-7. PMC: 3890841. DOI: 10.1073/pnas.1321405111. View

18.

Qiu P, Simonds E, Bendall S, Gibbs Jr K, Bruggner R, Linderman M . Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol. 2011; 29(10):886-91. PMC: 3196363. DOI: 10.1038/nbt.1991. View

19.

Linderman M, Bjornson Z, Simonds E, Qiu P, Bruggner R, Sheode K . CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data. Bioinformatics. 2012; 28(18):2400-1. PMC: 3436846. DOI: 10.1093/bioinformatics/bts425. View

20.

Zunder E, Finck R, Behbehani G, Amir E, Krishnaswamy S, Gonzalez V . Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat Protoc. 2015; 10(2):316-33. PMC: 4347881. DOI: 10.1038/nprot.2015.020. View