Classifying immunophenotypes with templates from flow cytometry

2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013

Volumn , Issue , 2013, Pages 256-265

  Azad, Ariful ^a   Khan, Arif ^a   Rajwa, Bartek ^a   Pyne, Saumyadipta ^b   Pothen, Alex ^a  

^a PURDUE UNIVERSITY   (United States)

^b UNIVERSITY OF HYDERABAD   (India)

Author keywords

Classification; Clustering; Flow cytometry; Immunology; Template tree; Templates

Indexed keywords

CLUSTERING; DISSIMILARITY MEASURES; DYNAMIC CLASSIFICATION; FLUORESCENCE MEASUREMENTS; SIGNALING PROTEINS; STATISTICAL PARAMETERS; TEMPLATE TREE; TEMPLATES;

ALGORITHMS; CELL CULTURE; CELL PROLIFERATION; CLASSIFICATION (OF INFORMATION); FLOW CYTOMETRY; IMMUNOLOGY; PROTEINS;

BIOINFORMATICS;

EID: 84888182236     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2506583.2506627     Document Type: Conference Paper

References (23)

1. N. Aghaeepour, G. Finak, H. Hoos, et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Meth., 10(3):228-238, 2013.
2. A. Azad, J. Langguth, Y. Fang, A. Qi, and A. Pothen. Identifying rare cell populations in comparative flow cytometry. LNCS, 6293:162-175, 2010, (WABI 2010).
3. A. Azad, S. Pyne, and A. Pothen. Matching phosphorylation response patterns of antigen-receptor stimulated T cells via flow cytometry. BMC Bioinformatics, 13(Suppl 2):S10, 2012.
4. A. Azad, B. Rajwa, and A. Pothen. Homogeneous meta-clustering in flow cytometry by variance stabilization. In preparation, 2013.
5. C. Brockmeyer, W. Paster, D. Pepper, et al. T Cell Receptor (TCR)-induced Tyrosine phosphorylation dynamics identifies THEMIS as a new TCR signalosome component. J. Biol. Chem., 286(9):7535-7547, 2011.
6. R. Brunelli and T. Poggio. Face recognition: Features versus templates. IEEE Trans. Patt. Anal. Mach. Intell., 15(10):1042-1052, 1993.
7. S. D. Connell and A. K. Jain. Template-based online character recognition. Patt. Recog., 34(1):1-14, 2001.
8. M. De Wachter, M. Matton, K. Demuynck, P. Wambacq, R. Cools, and D. Van Compernolle. Template-based continuous speech recognition. IEEE Trans. Audio Speech Lang. Proc., 15(4):1377-1390, 2007.
9. L. Deng, H. Strik, et al. Structure-based and template-based automatic speech recognition: comparing parametric and nonparametric approaches. In Proc. Interspeech, pp. 898-901, 2007.
10. A. Dress, K. T. Huber, J. Koolen, V. Moulton, and A. Spillner. Basic Phylogenetic Combinatorics. Cambridge University Press, 2012.
11. G. Finak, J. Perez, A. Weng, and R. Gottardo. Optimizing transformations for automated, high throughput analysis of flow cytometry data. BMC Bioinformatics, 11(1):546, 2010.
12. D. Gusfield. Partition-distance: A problem and class of perfect graphs arising in clustering. Inf. Proc. Lett., 82(3):159-164, 2002.
13. M. Halkidi and M. Vazirgiannis. Clustering validity assessment: Finding the optimal partitioning of a data set. In ICDM 2001, pp. 187-194, 2001.
14. T. Lin. Robust mixture modeling using multivariate skew t distributions. Stat. Comput., 20(3):343-356, 2010.
15. K. Lo, R. Brinkman, and R. Gottardo. Automated gating of flow cytometry data via robust model-based clustering. Cytometry Part A, 73(4):321-332, 2008.
16. E. Lugli, M. Roederer, and A. Cossarizza. Data analysis in flow cytometry: The future just started. Cytometry Part A, 77(7):705-713, 2010.
17. L. Maier, D. Anderson, P. DeJager, L. Wicker, and D. Hafler. Allelic variant in ctla4 alters T cell phosphorylation patterns. PNAS, 104(47):18607-18612, 2007.
18. D. Novo, G. Gregori, and B. Rajwa. Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices. Cytometry Part A, 83:508-520, 2013.
19. S. P. Perfetto, P. K. Chattopadhyay, and M. Roederer. Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Imm., 4(8):648-655, 2004.
20. J. M. Peters and M. Q. Ansari. Multiparameter flow cytometry in the diagnosis and management of acute leukemia. Arch. Path. Lab. Med., 135(1):44-54, 2011.
21. S. Pyne, X. Hu, K. Wang, et al. Automated high-dimensional flow cytometric data analysis. PNAS, 106(21):8519-8524, 2009.
22. R. A. Seder, P. A. Darrah, and M. Roederer. T-cell quality in memory and protection: implications for vaccine design. Nat. Rev. Imm., 8(4):247-258, 2008.
23. H. M. Shapiro. Practical Flow Cytometry. Wiley-Liss, 2005.