FlowAI: Automatic and interactive anomaly discerning tools for flow cytometry data

Bioinformatics

Volumn 32, Issue 16, 2016, Pages 2473-2480

  Monaco, Gianni ^a,b   Chen, Hao ^a   Poidinger, Michael ^a   Chen, Jinmiao ^a   De Magalhães, João Pedro ^b   Larbi, Anis ^a  

^a AGENCY FOR SCIENCE   (Singapore)

^b UNIVERSITY OF LIVERPOOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; CLUSTER ANALYSIS; FLOW CYTOMETRY; HUMAN; SOFTWARE; STATISTICAL ANALYSIS;

ALGORITHMS; CLUSTER ANALYSIS; DATA INTERPRETATION, STATISTICAL; FLOW CYTOMETRY; HUMANS; SOFTWARE;

EID: 84983315952     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw191     Document Type: Article

References (36)

1. Aghaeepour, N. et al. (2013) Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods, 10, 228-238.
2. Balcilar, M. (2007) mFilter: Miscellaneous time series filters. R Packag. version 0.1-3.
3. Barnett, D. and Reilly, J.T. (2007) Quality control in flow cytometry. In: Flow Cytometry: Principles and Applications. Humana Press, Totowa, NJ, pp. 113-131.
4. Bashashati, A. and Brinkman, R.R. (2009) A survey of flow cytometry data analysis methods. Adv. Bioinf., 2009, 584603.
5. Becher, B. et al. (2014) High-dimensional analysis of the murine myeloid cell system. Nat. Immunol., 15, 1181-1191.
6. Brown, M. and Wittwer, C. (2000) Flow cytometry: principles and clinical applications in hematology. Clin. Chem., 46, 1221-1229.
7. Chandola, V. et al. (2009) Anomaly detection: a survey. ACM Comput. Surv., 41, 1-58.
8. Chang, W. et al. (2105) shiny: Web Application Framework for R.
9. Christiano, L.J. and Fitzgerald, T.J. (2003) The band pass filter. Int. Econ. Rev. (Philadelphia), 44, 435-465.
10. Edwards, W.F. and Cavalli-Sforza, L.L. (1965) A method for cluster analysis. Biometrics, 21, 362-375.
11. Finak, G. et al. (2014) OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. PLoS Comput. Biol., 10, e1003806.
12. Fletez-Brant, K. (2014) flowClean: flowClean. R Packag. version 1.6.0.
13. Gentleman, R. et al. (2006) flowQ: Quality control for flow cytometry. R Packag. version 1.30.0.
14. Hahne, F. et al. (2009) flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics, 10, 1-8.
15. Hahne, F. et al. (2009) flowStats: Statistical methods for the analysis of flow cytometry data. R Packag. version 3.28.1.
16. Iglewicz, B. and Hoaglin, D.C. (1993) How to Detect and Handle Outliers. ASQC Basic References inQuality Control, ASQC, Milwaukee, WI, Vol. 16.
17. Jaye, D.L. et al. (2012) Translational applications of flow cytometry in clinical practice. J. Immunol., 188, 4715-4719.
18. Killick, R. and Eckley, I. (2014) changepoint: an R Package for changepoint analysis. J. Stat. Softw, 58, 1-19.
19. Leys, C. et al. (2013) Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol., 49, 764-766.
20. Maaten, L.V.D. and Hinton, G. (2008) Visualizing data using t-SNE. J. Mach. Learn. Res, 9, 2579-2605.
21. Mulley, W.R. and Kanellis, J. (2011) Understanding crossmatch testing in organ transplantation: a case-based guide for the general nephrologist. Nephrology, 16, 125-133.
22. Newell, E.W. et al. (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.
23. Novo, D. and Wood, J. (2008) Flow cytometry histograms: transformations, resolution, and display. Cytometry A, 73A, 685-692.
24. Oldaker, T.A. (2007) Quality control in clinical flow cytometry. Clin. Lab. Med., 27, 671-685.
25. Parks, D.R. et al. (2006) A new 'Logicle' display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytometry A, 69A, 541-551.
26. Perfetto, S.P. et al. (2006) Quality assurance for polychromatic flow cytometry. Nat. Protoc., 1, 1522-1530.
27. Pozarowski, P. and Darzynkiewicz, Z. (2004) Analysis of cell cycle by flow cytometry. Methods Mol. Biol., 281, 301-311.
28. Qian, Y. et al. (2012) FCSTrans: an open source software system for FCS file conversion and data transformation. Cytometry A, 81A, 353-356.
29. Qiu, P. et al. (2011) Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol., 29, 886-891.
30. Rosner, B. (1983) Percentage points for a generalized ESD many-outlier procedure. Technometrics, 25, 165-172.
31. Sarkar, D. et al. (2008) Using flowViz to visualize flow cytometry data. Bioinformatics, 24, 878-879.
32. Seamer, L.C. et al. (1997) Proposed new data file standard for flow cytometry, Version FCS 3.0. Cytometry, 28, 118-122.
33. Shekhar, K. et al. (2014) Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE).Proc. Natl.Acad. Sci. USA, 111, 202-207.
34. Spidlen, J. et al. (2012) FlowRepository: a resource of annotated flow cytometry datasets associated with peer-reviewed publications. Cytometry A, 81A, 727-731.
35. Van Gassen, S. et al. (2016) FloReMi: flow density survival regression using minimal feature redundancy. Cytometry A, 89, 22-29.
36. Vermes, I. et al. (2000) Flow cytometry of apoptotic cell death. J. Immunol. Methods, 243, 167-190.