Document Detail


Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies.
MedLine Citation:
PMID:  23629459     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Novel uses of automated flow cytometry technology for measuring levels of protein markers on thousands to millions of cells are promoting increasing need for relevant, customized Bayesian mixture modelling approaches in many areas of biomedical research and application. In studies of immune profiling in many biological areas, traditional flow cytometry measures relative levels of abundance of marker proteins using fluorescently labeled tags that identify specific markers by a single-color. One specific and important recent development in this area is the use of combinatorial marker assays in which each marker is targeted with a probe that is labeled with two or more fluorescent tags. The use of several colors enables the identification of, in principle, combinatorially increasingly numbers of subtypes of cells, each identified by a subset of colors. This represents a major advance in the ability to characterize variation in immune responses involving larger numbers of functionally differentiated cell subtypes. We describe novel classes of Markov chain Monte Carlo methods for model fitting that exploit distributed GPU (graphics processing unit) implementation. We discuss issues of cellular subtype identification in this novel, general model framework, and provide a detailed example using simulated data. We then describe application to a data set from an experimental study of antigen-specific T-cell subtyping using combinatorially encoded assays in human blood samples. Summary comments discuss broader questions in applications in immunology, and aspects of statistical computation.
Authors:
Lin Lin; Cliburn Chan; Sine R Hadrup; Thomas M Froesig; Quanli Wang; Mike West
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Statistical applications in genetics and molecular biology     Volume:  12     ISSN:  1544-6115     ISO Abbreviation:  Stat Appl Genet Mol Biol     Publication Date:  2013 Jun 
Date Detail:
Created Date:  2013-07-05     Completed Date:  2014-01-29     Revised Date:  2014-09-09    
Medline Journal Info:
Nlm Unique ID:  101176023     Medline TA:  Stat Appl Genet Mol Biol     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  309-31     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Amino Acid Sequence
Antigens, Differentiation / metabolism
Bayes Theorem
Computer Simulation
Flow Cytometry / methods*
Humans
Immunophenotyping / methods*
Kallikreins / chemistry,  metabolism
Markov Chains
Models, Biological*
Monte Carlo Method
Normal Distribution
Phenotype
Prostate-Specific Antigen / chemistry,  metabolism
T-Lymphocytes / metabolism*
Grant Support
ID/Acronym/Agency:
AI086032/AI/NIAID NIH HHS; P50 GM081883/GM/NIGMS NIH HHS; P50-GM081883/GM/NIGMS NIH HHS; RC1 AI086032/AI/NIAID NIH HHS
Chemical
Reg. No./Substance:
0/Antigens, Differentiation; EC 3.4.21.-/Kallikreins; EC 3.4.21.-/kallikrein-related peptidase 3, human; EC 3.4.21.77/Prostate-Specific Antigen
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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