Algorithmic tools for mining high-dimensional cytometry data

Journal of Immunology

Volumn 195, Issue 3, 2015, Pages 773-779

  Chester, Cariad ^a   Maecker, Holden T ^a  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; CALCULATION; CLUSTER ANALYSIS; CYTOMETRY; DATA ANALYSIS; DATA MINING; FLOW CYTOMETRY; HUMAN; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NONHUMAN; NONLINEAR SYSTEM; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; REGRESSION ANALYSIS; REPRODUCIBILITY; REVIEW; STATISTICAL DISTRIBUTION; STOCHASTIC NEIGHBOR EMBEDDING; B LYMPHOCYTE; BIOLOGY; IMMUNOLOGY; PROCEDURES; T LYMPHOCYTE;

CYTOKINE RECEPTOR;

ALGORITHMS; B-LYMPHOCYTES; COMPUTATIONAL BIOLOGY; DATA MINING; FLOW CYTOMETRY; HUMANS; RECEPTORS, CYTOKINE; T-LYMPHOCYTES;

EID: 84937685382     PISSN: 00221767     EISSN: 15506606     Source Type: Journal    
DOI: 10.4049/jimmunol.1500633     Document Type: Review

References (30)

1. Maecker, H. T., J. P. McCoy, and R. Nussenblatt. 2012. Standardizing immunophenotyping for the Human Immunology Project. Nat. Rev. Immunol. 12: 191-200.
2. Lugli, E., M. Roederer, and A. Cossarizza. 2010. Data analysis in flow cytometry: the future just started. Cytometry A 77: 705-713.
3. Ornatsky, O. I.,R. Kinach,D.R. Bandura,X. Lou, S. D.Tanner, V. I. Baranov, M.Nitz, andM. A.Winnik. 2008. Development of analytical methods for multiplex bio-Assay with inductively coupled plasma mass spectrometry. J. Anal. At. Spectrom. 23: 463-469.
4. Bendall, S. C., G. P. Nolan, M. Roederer, and P. K. Chattopadhyay. 2012. A deep profiler's guide to cytometry. Trends Immunol. 33: 323-332.
5. Aghaeepour, N., G. Finak, The FlowCAP Consortium, , The DREAM Consortium, H. Hoos, T. R. Mosmann, R. Brinkman, R. Gottardo, and R. H. Scheuermann. 2013. Critical assessment of automated flow cytometry data analysis techniques. [Published erratum appears in 2013Nat. Methods 10: 445.] Nat. Methods 10: 228-238.
6. Pyne, S., X. Hu, K. Wang, E. Rossin, T. I. Lin, L. M. Maier, C. Baecher-Allan, G. J. McLachlan, P. Tamayo, D. A. Hafler, et al. 2009. Automated high-dimensional flow cytometric data analysis. Proc. Natl. Acad. Sci. USA 106: 8519-8524.
7. Roederer, M., J. L. Nozzi, and M. C. Nason. 2011. SPICE: exploration and analysis of post-cytometric complex multivariate datasets. Cytometry A 79: 167-174.
8. Aghaeepour, N., A. Jalali, K. O'Neill, P. K. Chattopadhyay, M. Roederer, H. H. Hoos, and R. R. Brinkman. 2012. RchyOptimyx: cellular hierarchy optimization for flow cytometry. Cytometry A 81: 1022-1030.
9. Hotelling, H. 1933. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24: 417-441.
10. Newell, E. W., N. Sigal, S. C. Bendall, G. P. Nolan, and M. M. Davis. 2012. Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 36: 142-152.
11. Jolliffe, I. T. 2002. Principal Component Analysis. Springer-Verlag, New York.
12. Lee, J. A., and M. Verleysen. 2007. Nonlinear Dimensionality Reduction. Springer-Verlag, New York.
13. Van der Maaten, L., and G. Hinton. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9: 85.
14. Van der Maaten, L. 2013. Barnes-Hut-SNE. Proceedings of the International Conference on Learning Representations.
15. Amir, A. D., K. L. Davis, M. D. Tadmor, E. F. Simonds, J. H. Levine, S. C. Bendall, D. K. Shenfeld, S. Krishnaswamy, G. P. Nolan, and D. Pe'er. 2013. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31: 545-552.
16. Shekhar, K., P. Brodin, M. M. Davis, and A. K. Chakraborty. 2014. Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE). Proc. Natl. Acad. Sci. USA 111: 202-207.
17. Qiu, P., E. F. Simonds, S. C. Bendall, K. D. Gibbs, Jr., R. V. Bruggner, M. D. Linderman, K. Sachs, G. P. Nolan, and S. K. Plevritis. 2011. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29: 886-891.
18. Linderman, M. D., Z. Bjornson, E. F. Simonds, P. Qiu, R. V. Bruggner, K. Sheode, T. H. Meng, S. K. Plevritis, and G. P. Nolan. 2012. CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data. Bioinformatics 28: 2400-2401.
19. Naim, I., S. Datta, J. Rebhahn, J. S. Cavenaugh, T. R. Mosmann, and G. Sharma. 2014. SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design. Cytometry A 85: 408-421.
20. Zunder, E. R., E. Lujan, Y. Goltsev, M. Wernig, and G. P. Nolan. 2015. A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell 16: 323-337.
21. Van Gassen, S., B. Callebaut, M. J. Van Helden, B. N. Lambrecht, P. Demeester, T. Dhaene, and Y. Saeys. 2015. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A. DOI: 10.1002/cyto.a.22625
22. Tusher, V. G., R. Tibshirani, and G. Chu. 2001. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98: 5116-5121.
23. Gaudillière, B., G. K. Fragiadakis, R. V. Bruggner, M. Nicolau, R. Finck, M. Tingle, J. Silva, E. A. Ganio, C. G. Yeh, W. J. Maloney, et al. 2014. Clinical recovery from surgery correlates with single-cell immune signatures. Sci. Transl. Med. 6: 255ra131.
24. Inokuma, M. S., V. C. Maino, and C. B. Bagwell. 2013. Probability state modeling of memory CD8+ T-cell differentiation. J. Immunol. Methods 397: 8-17.
25. Bendall, S. C., K. L. Davis, A. D. Amir, M. D. Tadmor, E. F. Simonds, T. J. Chen, D. K. Shenfeld, G. P. Nolan, and D. Pe'er. 2014. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157: 714-725.
26. Mei, H. E., M. D. Leipold, A. R. Schulz, C. Chester, and H. T. Maecker. 2015. Barcoding of live human peripheral blood mononuclear cells for multiplexed mass cytometry. J. Immunol. 194: 2022-2031.
27. Bodenmiller, B., E. R. Zunder, R. Finck, T. J. Chen, E. S. Savig, R. V. Bruggner, E. F. Simonds, S. C. Bendall, K. Sachs, P. O. Krutzik, and G. P. Nolan. 2012. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30: 858-867.
28. Zunder, E. R., R. Finck, G. K. Behbehani, A. D. Amir, S. Krishnaswamy, V. D. Gonzalez, C. G. Lorang, Z. Bjornson, M. H. Spitzer, B. Bodenmiller, et al. 2015. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10: 316-333.
29. Finck, R., E. F. Simonds, A. Jager, S. Krishnaswamy, K. Sachs, W. Fantl, D. Pe'er, G. P. Nolan, and S. C. Bendall. 2013. Normalization of mass cytometry data with bead standards. Cytometry A 83: 483-494.
30. Leipold, M. D., and H. T. Maecker. 2012. Mass cytometry: protocol for daily tuning and running cell samples on a CyTOF mass cytometer. J. Vis. Exp. 69: e4398.