Advertisement

Computational Analysis of High-Dimensional Flow Cytometric Data for Diagnosis and Discovery

Chapter
First Online:
Part of the Current Topics in Microbiology and Immunology book series (CT MICROBIOLOGY, volume 377)

Abstract

Recent technological advancements have enabled the flow cytometric measurement of tens of parameters on millions of cells. Conventional manual data analysis and bioinformatics tools cannot provide a complete analysis of these datasets due to this complexity. In this chapter we will provide an overview of a general data analysis pipeline both for automatic identification of cell populations of known importance (e.g., diagnosis by identification of predefined cell population) and for exploratory analysis of cohorts of flow cytometry assays (e.g., discovery of new correlates of a malignancy). We provide three real-world examples of how unsupervised discovery has been used in basic and clinical research. We also discuss challenges for evaluation of the algorithms developed for (1) identification of cell populations using clustering, (2) identification of specific cell populations, and (3) supervised analysis for discriminating between patient subgroups.

Keywords

Acute Myeloid Leukemia Mean Fluorescence Intensity Manual Analysis Computational Pipeline Data Analysis Pipeline 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

References

  1. Aghaeepour N, Chattopadhyay PK, Ganesan A, O’Neill K, Zare H, Jalali A, Hoos HH, Roederer M, Brinkman RR (2012a) Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays. Bioinformatics 28:1009–1016. doi:  10.1093/bioinformatics/bts082 Google Scholar
  2. Aghaeepour N, Finak G, Consortium The FlowCAP, Dougall D, Khodabakhshi AH, Mah P, Obermoser G, Spidlen J, Taylor I, Wuensch SA, Bramson J, Eaves C, Weng AP, Iii ES, Ho K, Kollmann T, Rogers W, De Rosa S, Dalal B, Azad A, Pothen A, Brandes A, Bretschneider H, Bruggner R, Finck R, Jia R, Zimmerman N, Linderman M, Dill D, Nolan G, Chan C, Khettabi FE, O’Neill K, Chikina M, Ge Y, Sealfon S, Sugar I, Gupta A, Shooshtari P, Zare H, De Jager PL, Jiang M, Keilwagen J, Maisog JM, Luta G, Barbo AA, Majek P, Vilcek J, Manninen T, Huttunen H, Ruusuvuori P, Nykter M, McLachlan GJ, Wang K, Naim I, Sharma G, Nikolic R, Pyne S, Qian Y, Qiu P, Quinn J, Roth A, The DREAM, Consortium Meyer P, Stolovitzky G, Saez-Rodriguez J, Norel R, Bhattacharjee M, Biehl M, Bucher P, Bunte K, Di Camillo B, Sambo F, Sanavia T, Trifoglio E, Toffolo G, Dimitrieva S, Dreos R, Ambrosini G, Grau J, Grosse I, Posch S, Guex N, Keilwagen J, Kursa M, Rudnicki W, Liu B, Maienschein-Cline M, Manninen T, Huttunen H, Ruusuvuori P, Nykter M, Schneider P, Seifert M, Strickert M, Vilar JM, Hoos H, Mosmann TR, Brinkman R, Gottardo R, Scheuermann RH (2013) Critical assessment of automated flow cytometry data analysis techniques. Nat Methods. doi: 10.1038/nmeth.2365 PubMedCentralPubMedGoogle Scholar
  3. Aghaeepour N, Jalali A, O’Neill K, Chattopadhyay PK, Roederer M, Hoos HH, Brinkman RR (2012b) RchyOptimyx: cellular hierarchy optimization for flow cytometry. Cytometry A 81:1022–1030. doi: 10.1002/cyto.a.22209 PubMedCentralPubMedCrossRefGoogle Scholar
  4. Aghaeepour N, Nikolic R, Hoos HH, Brinkman RR (2011) Rapid cell population identification in flow cytometry data. Cytometry A 79:6–13. doi: 10.1002/cyto.a.21007 PubMedCentralPubMedCrossRefGoogle Scholar
  5. Bagwell CB (2004) DNA histogram analysis for node-negative breast cancer. Cytometry 58A:76–78CrossRefGoogle Scholar
  6. Bendall SC, Simonds EF, Qiu P, Amir eD, Krutzik PO, Finck R, Bruggner RV, Melamed R, Trejo A, Ornatsky OI, Balderas RS, Plevritis SK, Sachs K, Pe’er D, Tanner SD, Nolan GP (2011) Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332:687–696. doi: 10.1126/science.1198704
  7. Bioconductor (2013) Bioconductor—Flow Cytometry. http://www.bioconductor.org/packages/2.12/BiocViews.html#–FlowCytometry. Accessed June
  8. Boddy L, Wilkins MF, Morris CW (2001) Pattern recognition in flow cytometry. Cytometry 44:195–209PubMedCrossRefGoogle Scholar
  9. Diehl AD, Augustine AD, Blake JA, Cowell LG, Gold ES, Gondre-Lewis TA, Masci AM, Meehan TF, Morel PA, Nijnik A, Peters B, Pulendran B, Scheuermann RH, Yao QA, Zand MS, Mungall CJ (2011) Hematopoietic cell types: prototype for a revised cell ontology. J Biomed Inform 44:75–79. doi: 10.1016/j.jbi.2010.01.006 PubMedCentralPubMedCrossRefGoogle Scholar
  10. Finak G, Perez JM, Weng A, Gottardo R (2010) Optimizing transformations for automated, high throughput analysis of flow cytometry data. BMC Bioinform 11:546-2105-11-546. doi: 10.1186/1471-2105-11-546
  11. Ge Y, Sealfon SC (2012) flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding. Bioinformatics 28:2052–2058. doi: 10.1093/bioinformatics/bts300 PubMedCentralPubMedCrossRefGoogle Scholar
  12. Gene Ontology Consortium (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32:D258–D261CrossRefGoogle Scholar
  13. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genom biol 5(10):R80Google Scholar
  14. Hahne F, LeMeur N, Brinkman R, Ellis B, Haaland P, Sarkar D, Spidlen J, Strain E, Gentleman R (2009a) flowCore: a bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10(1):106Google Scholar
  15. Hahne F, Meur NL, Brinkman R, Ellis B, Haaland P, Sarkar D, Spidlen J, Strain E, Gentleman R (2009b) FlowCore: a bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10:106PubMedCentralPubMedCrossRefGoogle Scholar
  16. Herzenberg LA, Parks D, Sahaf B, Perez O, Roederer M, Herzenberg LA (2002) The history and future of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin Chem 48:1819–1827PubMedGoogle Scholar
  17. Jelizarow M, Guillemot V, Tenenhaus A, Strimmer K, Boulesteix A (2010) Over-optimism in bioinformatics: an illustration. Bioinformatics 26:1990–1998PubMedCrossRefGoogle Scholar
  18. Kalina T, Flores-Montero J, van der Velden VH, Martin-Ayuso M, Bottcher S, Ritgen M, Almeida J, Lhermitte L, Asnafi V, Mendonca A, de Tute R, Cullen M, Sedek L, Vidriales MB, Perez JJ, te Marvelde JG, Mejstrikova E, Hrusak O, Szczepanski T, van Dongen JJ, Orfao A, EuroFlow Consortium (EU-FP6, LSHB-CT-2006-018708) (2012) EuroFlow standardization of flow cytometer instrument settings and immunophenotyping protocols. Leukemia 26:1986-2010. doi:  10.1038/leu.2012.122
  19. Keeney M, Barnett D, Gratama J (2004) Impact of standardization on clinical cell analysis by flow cytometry. J Biol Regul Homeost Agents 18:305–312PubMedGoogle Scholar
  20. Le Meur N (2013) Computational methods for evaluation of cell-based data assessment–Bioconductor. Curr Opin Biotechnol 24:105–111. doi: 10.1016/j.copbio.2012.09.003 PubMedCrossRefGoogle Scholar
  21. Levin E, Serrano K, Devine DV (2013) Biomedical Excellence for Safer Transfusion (BEST) Collaborative (2013) Standardization of CD62P measurement: results of an international comparative study, Vox Sang. doi:  10.1111/vox.12023
  22. Lizard G (2007) Flow cytometry analyses and bioinformatics: interest in new softwares to optimize novel technologies and to favor the emergence of innovative concepts in cell research. Cytometry A 71:646–647. doi: 10.1002/cyto.a.20444 PubMedCrossRefGoogle Scholar
  23. Lo K, Brinkman RR, Gottardo R (2008) Automated gating of flow cytometry data via robust model-based clustering. Cytometry A. doi: 10.1002/cyto.a.20531 PubMedGoogle Scholar
  24. Lugli E, Roederer M, Cossarizza A (2010) Data analysis in flow cytometry: the future just started. Cytometry A 77:705–713. doi: 10.1002/cyto.a.20901 PubMedCentralPubMedCrossRefGoogle Scholar
  25. Maecker HT, McCoy JP, Nussenblatt R (2012) Standardizing immunophenotyping for the human immunology project. Nat Rev Immunol 12:191–200. doi: 10.1038/nri3158 PubMedCentralPubMedGoogle Scholar
  26. Maecker HT, Rinfret A, D’Souza P, Darden J, Roig E, Landry C, Hayes P, Birungi J, Anzala O, Garcia M, Harari A, Frank I, Baydo R, Baker M, Holbrook J, Ottinger J, Lamoreaux L, Epling CL, Sinclair E, Suni MA, Punt K, Calarota S, El-Bahi S, Alter G, Maila H, Kuta E, Cox J, Gray C, Altfeld M, Nougarede N, Boyer J, Tussey L, Tobery T, Bredt B, Roederer M, Koup R, Maino VC, Weinhold K, Pantaleo G, Gilmour J, Horton H, Sekaly RP (2005) Standardization of cytokine flow cytometry assays. BMC Immunol 6:13. doi:  10.1186/1471-2172-6-13
  27. Maino VC, Maecker HT (2004) Cytokine flow cytometry: a multiparametric approach for assessing cellular immune responses to viral antigens. Clin Immunol 110:222–231. doi: 10.1016/j.clim.2003.11.018 PubMedCrossRefGoogle Scholar
  28. Overton WR (1988) Modified histogram subtraction technique for analysis of flow cytometry data. Cytometry 9:619–626PubMedCrossRefGoogle Scholar
  29. Pyne S, Hu X, Wang K, Rossin E, Lin TI, Maier LM, Baecher-Allan C, McLachlan GJ, Tamayo P, Hafler DA, De Jager PL, Mesirov JP (2009) Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci U S A. doi: 10.1073/pnas.0903028106 PubMedCentralPubMedGoogle Scholar
  30. Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP (2006) GenePattern 2.0. Nat Genet 38:500–501. doi: 10.1038/ng0506-500 PubMedCrossRefGoogle Scholar
  31. Robinson JP, Rajwa B, Patsekin V, Davisson VJ (2012) Computational analysis of high-throughput flow cytometry data. Expert Opin Drug Discov 7:679–693. doi: 10.1517/17460441.2012.693475 PubMedCrossRefGoogle Scholar
  32. Roederer M (2001) Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry 45:194–205PubMedCrossRefGoogle Scholar
  33. Roederer M, Hardy RR (2001) Frequency difference gating: a multivariate method for identifying subsets that differ between samples. Cytometry 45:56–64. doi: 10.1002/1097-0320(20010901) 45:1 < 56:AID-CYTO1144 > 3.0.CO;2–9 [pii]PubMedCrossRefGoogle Scholar
  34. Roederer M, Moore W, Treister A, Hardy RR, Herzenberg LA (2001) Probability binning comparison: a metric for quantitating multivariate distribution differences. Cytometry 45:47–55. doi: 10.1002/1097-0320(20010901) 45:1 < 47:AID-CYTO1143 > 3.0.CO;2-A [pii]PubMedCrossRefGoogle Scholar
  35. Sarkar D, Le Meur N, Gentleman R (2008) Using flowViz to Visualize Flow Cytometry Data. BioinformaticsGoogle Scholar
  36. Spidlen J, Breuer K, Brinkman R (2012a) Preparing a Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) compliant manuscript using the International Society for Advancement of Cytometry (ISAC) FCS file repository (FlowRepository.org). Curr Protoc Cytom Chapter 10:Unit 10.18. doi:  10.1002/0471142956.cy1018s61
  37. Spidlen J, Breuer K, Rosenberg C, Kotecha N, Brinkman RR (2012b) FlowRepository: a resource of annotated flow cytometry datasets associated with peer-reviewed publications. Cytometry A 81:727–731. doi: 10.1002/cyto.a.22106 PubMedCrossRefGoogle Scholar
  38. Suni MA, Dunn HS, Orr PL, de Laat R, Sinclair E, Ghanekar SA, Bredt BM, Dunne JF, Maino VC, Maecker HT (2004) Performance of plate-based cytokine flow cytometry with automated data analysis. feedbackGoogle Scholar
  39. Zare H, Bashashati A, Kridel R, Aghaeepour N, Haffari G, Connors JM, Gascoyne RD, Gupta A, Brinkman RR, Weng AP (2012) Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma. Am J Clin Pathol 137:75–85. doi:  10.1309/AJCPMMLQ67YOMGEW Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Terry Fox LaboratoryBC Cancer AgencyVancouver BCCanada

Personalised recommendations

Cite chapter

Buy options