Flow Cytometry Bioinformatics

PLoS Computational Biology

Volumn 9, Issue 12, 2013, Pages

  O'Neill, Kieran ^a,b   Aghaeepour, Nima ^a,b   Špidlen, Josef ^a   Brinkman, Ryan ^a,b  

^a BRITISH COLUMBIA CANCER AGENCY   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; CELL PROLIFERATION; DATA VISUALIZATION; FLOW CYTOMETRY; METADATA; POPULATION STATISTICS; QUALITY CONTROL; SUPERVISED LEARNING;

CELL POPULATIONS; COMPUTATIONAL RESOURCES; COMPUTATIONAL STATISTICS; COMPUTATIONAL TOOLS; CYTOMETRY; DATA STANDARDS; INTERNATIONAL SOCIETY; OPEN DATUM; OPEN SOFTWARE; STATISTICS LEARNING;

CELL CULTURE;

ARTICLE; AUTOANALYSIS; BIOINFORMATICS; CELL TYPE; DATA ANALYSIS; DATA ANALYSIS SOFTWARE; DATA BASE; FLOW CYTOMETRY; HIGH THROUGHPUT SCREENING; HUMAN; INFORMATION STORAGE; MACHINE LEARNING; MATHEMATICAL COMPUTING; NONHUMAN; WEB BROWSER;

CELL SEPARATION; COMPUTATIONAL BIOLOGY; FLOW CYTOMETRY;

EID: 84892763223     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003365     Document Type: Article

References (78)

1. Brando B, Barnett D, Janossy G, Mandy F, Autran B, et al. (2000) Cytofluorometric methods for assessing absolute numbers of cell subsets in blood. European Working Group on Clinical Cell Analysis. Cytometry 42: 327-346.
2. Ferreira-Facio CS, Milito C, Botafogo V, Fontana M, Thiago LS, et al. (2013) Contribution of multiparameter flow cytometry immunophenotyping to the diagnostic screening and classification of pediatric cancer. PLoS ONE 8: e55534 doi:10.1371/journal.pone.0055534.
3. Wu D, Wood BL, Fromm JR, (2013) Flow cytometry for non-Hodgkin and classical Hodgkin lymphoma. Methods Mol Biol 971: 27-47.
4. Wang Y, Hammes F, De Roy K, Verstraete W, Boon N, (2010) Past, present and future applications of flow cytometry in aquatic microbiology. Trends Biotechnol 28: 416-424.
5. Johnson LA, Flook JP, Look MV, Pinkel D, (1987) Flow sorting of X and Y chromosome-bearing spermatozoa into two populations. Gamete Res 16: 1-9.
6. Baerlocher GM, Vulto I, de Jong G, Lansdorp PM, (2006) Flow cytometry and FISH to measure the average length of telomeres (flow FISH). Nat Protoc 1: 2365-2376.
7. Chattopadhyay PK, Hogerkorp C-M, Roederer M, (2008) A chromatic explosion: the development and future of multiparameter flow cytometry. Immunology 125: 441-449.
8. Behbehani GK, Bendall SC, Clutter MR, Fantl WJ, Nolan GP, (2012) Single-cell mass cytometry adapted to measurements of the cell cycle. Cytometry A 81: 552-566.
9. White AK, VanInsberghe M, Petriv OI, Hamidi M, Sikorski D, et al. (2011) High-throughput microfluidic single-cell RT-qPCR. Proc Natl Acad Sci U S A 108: 13999-14004.
10. Spidlen J, Leif RC, Moore W, Roederer M, (2008) International Society for the Advancement of Cytometry Data Standards Task Force, (2008) et al. Gating-ML: XML-based gating descriptions in flow cytometry. Cytometry A 73A: 1151-1157.
11. International Society for Advancement of Cytometry (2013) Gating-ML 2.0 (Report). Available: http://flowcyt.sourceforge.net/gating/latest.pdf. Accessed 8 November 2013.
12. Roederer M, (2002) Compensation in flow cytometry. Curr Protoc Cytom Chapter 1: Unit 1.14.
13. Bagwell CB, Adams EG, (1993) Fluorescence spectral overlap compensation for any number of flow cytometry parameters. Ann N Y Acad Sci 677: 167-184.
14. Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, et al. (2009) flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10: 106.
15. Shapiro HM (2003) Practical flow cytometry. New York: Wiley-Liss. pp. 235.
16. Parks DR, Roederer M, Moore WA, (2006) A new "Logicle" display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytometry A 69: 541-551.
17. Finak G, Perez J-M, Weng A, Gottardo R, (2010) Optimizing transformations for automated, high throughput analysis of flow cytometry data. BMC Bioinformatics 11: 546.
18. Moore WA, Parks DR, (2012) Update for the logicle data scale including operational code implementations. Cytometry A 81: 273-277.
19. Bagwell CB, (2005) Hyperlog-a flexible log-like transform for negative, zero, and positive valued data. Cytometry A 64: 34-42.
20. Lo K, Brinkman RR, Gottardo R, (2008) Automated gating of flow cytometry data via robust model-based clustering. Cytometry A 73: 321-332.
21. Qian Y, Liu Y, Campbell J, Thomson E, Kong YM, et al. (2012) FCSTrans: an open source software system for FCS file conversion and data transformation. Cytometry A 81: 353-356.
22. Le Meur N, Rossini A, Gasparetto M, Smith C, Brinkman RR, et al. (2007) Data quality assessment of ungated flow cytometry data in high throughput experiments. Cytometry A 71: 393-403.
23. Rogers WT, Moser AR, Holyst HA, Bantly A, Mohler ER, et al. (2008) Cytometric fingerprinting: quantitative characterization of multivariate distributions. Cytometry A 73: 430-441.
24. Hahne F, Khodabakhshi AH, Bashashati A, Wong C-J, Gascoyne RD, et al. (2010) Per-channel basis normalization methods for flow cytometry data. Cytometry A 77: 121-131.
25. Hornik K, Böhm W (2013) clue: Cluster ensembles, version 0.3-47. Available: http://cran.r-project.org/web/packages/clue/index.html. Accessed 15 February 2013.
26. Aghaeepour N, Finak G, (2013) FlowCAP Consortium DREAM Consortium (2013) Hoos H, et al. (2013) Critical assessment of automated flow cytometry data analysis techniques. Nat Methods 10: 228-238.
27. Lugli E, Roederer M, Cossarizza A, (2010) Data analysis in flow cytometry: the future just started. Cytometry A 77: 705-713.
28. Tree Star, Inc (2013) FlowJo. Available: http://www.flowjo.com/vX/en/ws.boolean.html. Accessed 5 April 2013.
29. De Novo Software (2013) FCS Express. Available: http://www.denovosoftware.com/site/Flow-RUO-Overview.shtml. Accessed 3 April 2013.
30. Saluk A (2013) TSRI Cytometry Software Page. Available: http://facs.scripps.edu/software.html. Accessed September 3 2009.
31. Leukobyte (2013) CytoPaint Classic. Available: http://leukobyte.com Accessed 5 April 2013.
32. Purdue University Cytometry Laboratories (2013) PUCL Cytometry Software Page. Available: http://www.cyto.purdue.edu/Purdue_software. Accessed 7 July 2011.
33. Beckman Coulter (2013) Kaluza. Available: http://www.beckmancoulter.com. Accessed 10 February 2013.
34. Bendall SC, Nolan GP, (2012) From single cells to deep phenotypes in cancer. Nat Biotechnol 30: 639-647.
35. Maecker HT, Rinfret A, D'Souza P, Darden J, Roig E, et al. (2005) Standardization of cytokine flow cytometry assays. BMC Immunol 6: 13.
36. Virgo PF, Gibbs GJ, (2012) Flow cytometry in clinical pathology. Ann Clin Biochem 49: 17-28.
37. Costa ES, Pedreira CE, Barrena S, Lecrevisse Q, Flores J, et al. (2010) 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 24: 1927-1933.
38. Qiu P, Simonds EF, Bendall SC, Gibbs KD, Bruggner RV, et al. (2011) Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 29: 886-891.
39. van der Maaten L (2013) Matlab Toolbox for Dimensionality Reduction. Available: http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html. Accessed 10 February 2013.
40. Bendall SC, Simonds EF, Qiu P, Amir E-aD, Krutzik PO, et al. (2011) Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332: 687-696.
41. Lo K, Hahne F, Brinkman RR, Gottardo R, (2009) flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics 10: 145.
42. Pyne S, Hu X, Wang K, Rossin E, Lin T-I, et al. (2009) Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci U S A 106: 8519-8524.
43. Qian Y, Wei C, Eun-Hyung Lee F, Campbell J, Halliley J, et al. (2010) 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. Cytometry B Clin Cytom 78 Suppl 1: S69-82.
44. Zare H, Shooshtari P, Gupta A, Brinkman RR, (2010) Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics 11: 403.
45. Aghaeepour N, Nikolic R, Hoos HH, Brinkman RR, (2011) Rapid cell population identification in flow cytometry data. Cytometry A 79: 6-13.
46. 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.
47. Roederer M, Treister A, Moore W, Herzenberg LA, (2001) Probability binning comparison: a metric for quantitating univariate distribution differences. Cytometry 45: 37-46.
48. Roederer M, Hardy RR, (2001) Frequency difference gating: a multivariate method for identifying subsets that differ between samples. Cytometry 45: 56-64.
49. Aghaeepour N, Chattopadhyay PK, Ganesan A, O'Neill K, Zare H, et al. (2012) Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays. Bioinformatics 28: 1009-1016.
50. Aghaeepour N, Jalali A, O'Neill K, Chattopadhyay PK, Roederer M, et al. (2012) RchyOptimyx: cellular hierarchy optimization for flow cytometry. Cytometry A 81: 1022-1030.
51. Zare H, Bashashati A, Kridel R, Aghaeepour N, Haffari G, et al. (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.
52. Qiu P, (2012) Inferring phenotypic properties from single-cell characteristics. PLoS ONE 7: e37038 doi:10.1371/journal.pone.0037038.
53. Bodenmiller B, Zunder ER, Finck R, Chen TJ, Savig ES, et al. (2012) Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat Biotechnol 30: 858-867.
54. Bashashati A, Johnson NA, Khodabakhshi AH, Whiteside MD, Zare H, et al. (2012) B cells with high side scatter parameter by flow cytometry correlate with inferior survival in diffuse large B-cell lymphoma. Am J Clin Pathol 137: 805-814.
55. Murphy RF, Chused TM, (1984) A proposal for a flow cytometric data file standard. Cytometry 5: 553-555.
56. International Society for Advancement of Cytometry (2013) International Society for Advancement of Cytometry. Available: http://isac-net.org/. Accessed 5 March 2013.
57. Dean PN, Bagwell CB, Lindmo T, Murphy RF, Salzman GC, (1990) Introduction to flow cytometry data file standard. Cytometry 11: 321-322.
58. Seamer LC, Bagwell CB, Barden L, Redelman D, Salzman GC, et al. (1997) Proposed new data file standard for flow cytometry, version FCS 3.0. Cytometry 28: 118-122.
59. Spidlen J, Moore W, Parks D, Goldberg M, Bray C, et al. (2010) Data File Standard for Flow Cytometry, version FCS 3.1. Cytometry A 77: 97-100.
60. Leif RC, Spidlen J, Brinkman RR (2009) Cytometry Standards Continuum [7264-16]. In: SPIE BIOS Proc. Vol. 6859. 8-12 February 2009. Available: http://newportinstrument.com/cytometryml/pdf/Cytometry_Stds_Continuum08.pdf. Accessed 5 March 2013.
61. International Society for Advancement of Cytometry (2008) Analytical Cytometry Standard NetCDF Conventions for List Mode Binary Data File Component. Available: http://flowcyt.sourceforge.net/netcdf/latest.pdf. Accessed 5 March 2013.
62. Spidlen J, Shooshtari P, Kollmann TR, Brinkman RR, (2011) Flow cytometry data standards. BMC Res Notes 4: 50.
63. International Society for Advancement of Cytometry (2012) Classification Results File Format (Report). Available: http://flowcyt.sourceforge.net/clr/latest.pdf. Accessed 5 March 2013.
64. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80.
65. Bioconductor (2013) Bioconductor FlowCytometry view. Available: http://www.bioconductor.org/packages/release/BiocViews.html#___FlowCytometry. Accessed 11 July 2013.
66. Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, et al. (2006) GenePattern 2.0. Nat Genet 38: 500-501.
67. Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, et al. (2012) GenePattern Flow Cytometry Suite. Available: http://www.broadinstitute.org/cancer/software/genepattern/modules/flow_cytometry. Accessed 11 July 2013.
68. Lee JA, Spidlen J, Boyce K, Cai J, Crosbie N, et al. (2008) MIFlowCyt: the minimum information about a Flow Cytometry Experiment. Cytometry A 73: 926-930.
69. Kotecha N, Krutzik PO, Irish JM, (2010) Web-based analysis and publication of flow cytometry experiments. Curr Protoc Cytom Chapter 10: Unit 10.17.
70. Spidlen J, Breuer K, Rosenberg C, Kotecha N, Brinkman RR, (2012) FlowRepository: a resource of annotated flow cytometry datasets associated with peer-reviewed publications. Cytometry A 81: 727-731.
71. Spidlen J, Breuer K, Brinkman R, (2012) 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.
72. Spidlen J (2013) Listing of FlowRepository public data sets. Available: http://flowrepository.org/public_experiment_representations. Accessed 5 July 2013.
73. FlowCAP- Flow Cytometry: Critical Assessment of Population Identification Methods. http://flowcap.flowsite.org. Accessed 15 March 2013.
74. Aghaeepour N (2012) IDCRP's HIV Natural History Study data set. Available: https://flowrepository.org/id/FR-FCM-ZZZK. Accessed 3 March 2013.
75. Craig FE, Brinkman RR, Eyck ST, Aghaeepour N, (2013) Computational analysis optimizes the flow cytometric evaluation for lymphoma. Cytometry B Clin Cytom E-pub ahead of print. doi:10.1002/cyto.b.21115.
76. Villanova F, Di Meglio P, Inokuma M, Aghaeepour N, Perucha E, et al. (2013) Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery. PLoS ONE 8: e65485 doi:10.1371/journal.pone.0065485.
77. Maecker HT, McCoy JP, Nussenblatt R, (2012) Standardizing immunophenotyping for the Human Immunology Project. Nat Rev Immunol 12: 191-200.
78. Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP, (2010) Computational solutions to large-scale data management and analysis. Nat Rev Genet 11: 647-657.