Cell population identification using fluorescence-minus-one controls with a one-class classifying algorithm

Bioinformatics

Volumn 30, Issue 23, 2014, Pages 3372-3378

  Feher, Kristen ^a   Kirsch, Jenny ^a   Radbruch, Andreas ^a   Chang, Hyun Dong ^a   Kaiser, Toralf ^a  

^a DEUTSCHES RHEUMA FORSCHUNGSZENTRUM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER;

ALGORITHM; CLUSTER ANALYSIS; FLOW CYTOMETRY; FLUORESCENCE; HUMAN; PROCEDURES; SUPPORT VECTOR MACHINE;

ALGORITHMS; BIOLOGICAL MARKERS; CLUSTER ANALYSIS; FLOW CYTOMETRY; FLUORESCENCE; HUMANS; SUPPORT VECTOR MACHINES;

EID: 84925550970     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu575     Document Type: Article

References (22)

1. Aghaeepour, N. et al. (2011) Rapid cell population identification in flow cytometry data. Cytometry A, 79, 6-13.
2. Aghaeeour, N. et al. (2013) Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods, 10, 228-238.
3. Aubin, J.E. (1979) Autofluorescence of viable cultured mammalian cells. J. Histochem. Cytochem., 27, 36-43.
4. Bacher, P. et al. (2013) Antigen-reactive T cell enrichment for direct, high-resolution analysis of the human naive and memory Th cell repertoire. J. Immunol., 190, 3967-3976.
5. Baumgarth, N. and Roederer, M. (2000) A practical approach to multicolor flow cytometry for immunophenotyping. J. Immunol. Meth., 243, 77-97.
6. Chapelle, O. et al. (2002) Choosing multiple parameters for support vector machines. Mach. Learn., 46, 131-159.
7. Chattopadhyay, P.K. et al. (2008) A chromatic explosion: the development and future of multiparamter flow cytometry. Immunology, 125, 441-449.
8. Fawcett, T. (2006) An introduction to ROC analysis. Patt. Recog. Lett, 27, 861-874.
9. Flury, B. (1983) Some relations between the comparison of covariance matrices and principal component analysis. Comp. Stat. Data Anal., 1, 97-109.
10. Fraley, C. et al. (2012) mclust version 4 for R: normal mixture modeling for model-based clustering, classification, and density estimation. Technical Report No. 597. Department of Statistics, University of Washington.
11. Hahne, F. et al. (2009) flowCore: a Bioconductor package for high-throughput flow cytometry. BMC Bioinformatics, 10, 106.
12. Hastie, T. et al. (2009) Unsupervised learning. Elements of Statistical Learning. 2nd edn. Springer Verlag.
13. Lo, K. et al. (2009) flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics, 10, 145.
14. Novo, D. et al. (2013) Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices. Cytometry A, 83A, 508-520.
15. Pyne, S. et al. (2009) Automated high-dimensional flow cytometric data analysis. Proc. Natl Acad. Sci. USA, 106, 8519-8524.
16. Qiu, P. et al. (2011) Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotech., 29, 886-891.
17. R Core Team. (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing.
18. Roederer, M. (2001) Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry, 45, 194-205.
19. Roederer, M. (2002) Compensation in flow cytometry. Curr. Prot. Cytometry, 221.14.1-1.14.20.
20. Roederer, M. (2008) How many events is enough? Are you positive? Cytometry A, 73, 384-385.
21. Schölkopf, B. et al. (2000) Estimating the support of a high-dimensional distribution. Technical Report MSR-TR-99-87. Microsoft Research.
22. Tax, D.M.J. (2001) One-class classification (concept-learning in the absence of counter-examples). PhD Thesis. Technische Universiteit Delft.