Hierarchical Bayesian mixture modelling for antigenspecific T-cell subtyping in combinatorially encoded flow cytometry studies

Statistical Applications in Genetics and Molecular Biology

Volumn 12, Issue 3, 2013, Pages 309-331

  Lin, Lin ^a   Chan, Cliburn ^b   Hadrup, Sine R ^c   Froesig, Thomas M ^c   Wang, Quanli ^a   West, Mike ^a  

^a Duke University   (United States)

^b DUKE UNIVERSITY MEDICAL CENTER   (United States)

^c GENTOFTE UNIVERSITY HOSPITAL   (Denmark)

Author keywords

Dirichlet process mixtures; GPU computing; Hierarchical model; Immune profiling; Immune response biomarkers; Large data sets; Markov chain Monte Carlo; Massive mixture models; Multimers; Posterior simulation; Relabeling; T cell subtyping

Indexed keywords

BIOLOGICAL MARKER; CD4 ANTIGEN; CD8 ANTIGEN; POLYMER; PROSTATE SPECIFIC ANTIGEN; DIFFERENTIATION ANTIGEN; KALLIKREIN; KALLIKREIN-RELATED PEPTIDASE 3, HUMAN;

ANTIGEN SPECIFICITY; ARTICLE; AUTOANALYSIS; BAYES THEOREM; BIOTECHNOLOGICAL PROCEDURES; BLOOD SAMPLING; CELL LABELING; CENTRAL PROCESSING UNIT; CLINICAL IMMUNOLOGY; COMPUTER; COMPUTER ANALYSIS; COMPUTER GRAPHICS; FLOW CYTOMETRY; FLUORESCENCE; GRAPHICS PROCESSING UNIT; HUMAN; HUMAN CELL; IMMUNE RESPONSE; INFORMATION PROCESSING DEVICE; MEDICAL RESEARCH; MEDICAL TECHNOLOGY; MONTE CARLO METHOD; PERIPHERAL BLOOD MONONUCLEAR CELL; PROBABILITY; PROCESS MODEL; PROSTATE CANCER; STATISTICAL MODEL; T LYMPHOCYTE; T LYMPHOCYTE SUBPOPULATION; VALIDATION STUDY; ALGORITHM; AMINO ACID SEQUENCE; BIOLOGICAL MODEL; CHEMISTRY; COMPUTER SIMULATION; IMMUNOPHENOTYPING; METABOLISM; NORMAL DISTRIBUTION; PHENOTYPE; PROCEDURES;

ALGORITHMS; AMINO ACID SEQUENCE; ANTIGENS, DIFFERENTIATION; BAYES THEOREM; COMPUTER SIMULATION; FLOW CYTOMETRY; HUMANS; IMMUNOPHENOTYPING; KALLIKREINS; MARKOV CHAINS; MODELS, BIOLOGICAL; MONTE CARLO METHOD; NORMAL DISTRIBUTION; PHENOTYPE; PROSTATE-SPECIFIC ANTIGEN; T-LYMPHOCYTES;

EID: 84881635497     PISSN: None     EISSN: 15446115     Source Type: Journal    
DOI: 10.1515/sagmb-2012-0001     Document Type: Article

References (18)

1. Andersen, R. S., P. Kvistborg, T. M. Frøsig, N. W. Pedersen, R. Lyngaa, A. H. Bakker, C. J. Shu, P. thor Straten, T. N. Schumacher and S. R. Hadrup (2012): "Parallel detection of antigen-specifict cell responses by combinatorial encoding of mhc multimers," Nature Protocols, 7, 891-902.
2. Chan, C., F. Feng, M. West and T. B. Kepler (2008): "Statistical mixture modelling for cell subtype identification inflow cytometry," Cytometry A, 73, 693-701.
3. Cron, A. J. and M. West (2011): "Efficient classification-based relabeling in mixture models," Am. Stat., 65, 16-20.
4. Escobar, M. D. and M. West (1995): "Bayesian density estimation and inference using mixtures," J. Am. Stat. Assoc., 90, 577-588.
5. Feyerabend S., S. Stevanovic, C. Gouttefangeas, D. Wernet, J. Hennenlotter, J. Bedke, K. Dietz, S. Pascolo, M. Kuczyk, H. G. Rammensee and A. Stenzl (2009): "Novel multi-peptide vaccination in hla-a2+ hormone sensitive patients with biochemical relapse of prostate cancer," Prostate, 69, 917-927.
6. Finak, G., A. Bashashati, R. Brinkman and R. Gottardo (2009): "Merging mixture components for cell population identification in flow cytometry," Adv. Bioinform, Article ID 247646.
7. Frelinger, J., J. Ottinger, C. Gouttefangeas and C. Chan (2010): "Modeling flow cytometry data for cancer vaccine immune monitoring," Cancer Immunol. Immun., 59, 1435-1441.
8. Hadrup, S. R., A. H. Bakker, C. J. Shu, R. S. Andersen, J. van Veluw, P. Hombrink, E. Castermans, P. thor Straten, C. Blank, J. B. Haanen, M. H. Heemskerk and T. N. Schumacher (2009): "Parallel detection of antigen-specific T-cell responses by multidimensional encoding of MHC multimers," Nat. Methods, 6, 520-528.
9. Hadrup, S. R. and T. N. Schumacher (2010): "MHC-based detection of antigen-specific CD8+ T cell responses," Cancer Immunol. Immun., 59, 1425-1433.
10. Ishwaran, H. and L. F. James (2001): "Gibbs sampling methods for stick-breaking priors," J. Am. Stat. Assoc., 96, 161-173.
11. Ji, C., D. Merl, T. B. Kepler and M. West (2009): "Spatial mixture modelling for unobserved point processes: application to immunofluorescence histology," Bayesian Analysis, 4, 297-316.
12. Lo, K., R. R. Brinkman and R. Gottardo (2008): "Automated gating of flow cytometry data via robust model-based clustering," Cytometry A, 73, 321-332.
13. Manolopoulou, I., C. Chan and M. West (2010): "Selection sampling from large datasets for targeted inference in mixture modeling (with discusion)," Bayesian Analysis, 5, 429-450, PMC2943396.
14. Newell, E. W., L. O. Klein, W. Yu and M. M. Davis (2009): "Simultaneous detection of many T-cell specificities using combinatorial tetramer staining," Nat. Methods, 6, 497-499.
15. Pyne, S., X. Hu, K. Wang, E. Rossin, T. Lin, L. M. Maier, C. Baecher-Allan, G. J. McLachlan, P. Tamayo, D. A. Hafler, P. L. DeJager and J. P. Mesirov (2009): "Automated high-dimensional flow cytometric data analysis," Proc. Natl. Acad. Sci., 106, 8519.
16. Suchard, M. A., Q. Wang, C. Chan, J. Frelinger, A. J. Cron and M. West (2010): "Understanding GPU programming for statistical computation: studies in massively parallel massive mixtures," J. Comput. Graph. Stat., 19, 419-438.
17. Teh, Y. W., M. I. Jordan, M. J. Beal and D. M. Blei (2006): "Hierarchical Dirichlet processes," J. Am. Stat. Assoc., 101, 1566-1581.
18. West, M., P. Müller and M. D. Escobar (1994): Hierarchical priors and mixture models, with application in regression and density estimation. In: Smith, A. F. M., Freeman P. R. (Eds.), Aspects of Uncertainty: A Tribute to D. V. Lindley. London: Wiley, pp. 363-386.