Semiparametric Bayesian Inference for Phage Display Data
Identifieur interne : 000247 ( Istex/Checkpoint ); précédent : 000246; suivant : 000248Semiparametric Bayesian Inference for Phage Display Data
Auteurs : Luis G. Le N-Novelo [États-Unis] ; Peter Müller [États-Unis] ; Wadih Arap [États-Unis] ; Mikhail Kolonin [États-Unis] ; Jessica Sun [États-Unis] ; Renata Pasqualini [États-Unis] ; Kim-Anh Do [États-Unis]Source :
- Biometrics [ 0006-341X ] ; 2013-03.
English descriptors
- Teeft :
- Anderson cancer center, Arap, Base measure, Bayesian, Bayesian analysis, Biometrics, Bone marrow, Cancer patients, Certain tissues, Consecutive stages, Contingency tables, Convenient mechanism, Decision problem, Decision theoretic framework, Dirichlet, Dirichlet process, Dirichlet process mixture models, Email, Empirical bayes, False discovery rate, Gamma distribution, Graphical statistics, Hierarchical model, Holcombe boulevard, Human data, Human phage display experiment, Identity line, Inference, Inference goal, Kolonin, Large count, Large number, Large sample size, Ller, Massive multiplicity problem, Mcmc simulation, Mixture model, Monte carlo, Mouse data, Nondecreasing counts, Nonparametric, Normal distributions, Optimal bayes rule, Optimal discovery procedure, Optimal rule, Other parameters, Other words, Overall slope, Pair counts, Parametric, Parametric model, Parametric version, Pasqualini, Peptide, Phage, Phage display, Phage display data, Phage display experiment, Poisson, Poisson model, Posterior, Posterior distribution, Posterior inference, Posterior probabilities, Posterior probability, Posterior simulation, Predictive distribution, Preferential binding, Pressler street, Probability model, Random baseline, Random distribution, Random peptides, Random probability measure, Same tissue, Sampling model, Schematic representation, Second stages, Semiparametric, Semiparametric bayesian inference, Semiparametric mixture model, Semiparametric model, Simulation, Simulation study, Simulation truth, Squared errors, Statistical methodology, Third stage, Third stages, Threshold value, Total mass parameter, Tripeptide, Tripeptides, Utility function, Variance, Vertical line.
Abstract
We discuss inference for a human phage display experiment with three stages. The data are tripeptide counts by tissue and stage. The primary aim of the experiment is to identify ligands that bind with high affinity to a given tissue. We formalize the research question as inference about the monotonicity of mean counts over stages. The inference goal is then to identify a list of peptide–tissue pairs with significant increase over stages. We use a semiparametric Dirichlet process mixture of Poisson model. The posterior distribution under this model allows the desired inference about the monotonicity of mean counts. However, the desired inference summary as a list of peptide–tissue pairs with significant increase involves a massive multiplicity problem. We consider two alternative approaches to address this multiplicity issue. First we propose an approach based on the control of the posterior expected false discovery rate. We notice that the implied solution ignores the relative size of the increase. This motivates a second approach based on a utility function that includes explicit weights for the size of the increase.
Url:
DOI: 10.1111/j.1541-0420.2012.01817.x
Affiliations:
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D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 0018‐1, P.O. Box 301439, Houston, Texas 77230‐1439</wicri:regionArea><wicri:noRegion>Texas 77230‐1439</wicri:noRegion></affiliation><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Cancer Biology, University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Unit 0018‐1, P.O. 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Anderson Cancer Center, 1400 Pressler Street, Unit Number: 1411, Houston, Texas 77030</wicri:regionArea><wicri:noRegion>Texas 77030</wicri:noRegion></affiliation><affiliation></affiliation><affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Biometrics</title><title level="j" type="alt">BIOMETRICS</title><idno type="ISSN">0006-341X</idno><idno type="eISSN">1541-0420</idno><imprint><biblScope unit="vol">69</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="174">174</biblScope><biblScope unit="page" to="183">183</biblScope><biblScope unit="page-count">10</biblScope><date type="published" when="2013-03">2013-03</date></imprint><idno type="ISSN">0006-341X</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0006-341X</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="Teeft" xml:lang="en"><term>Anderson cancer center</term><term>Arap</term><term>Base measure</term><term>Bayesian</term><term>Bayesian analysis</term><term>Biometrics</term><term>Bone marrow</term><term>Cancer patients</term><term>Certain tissues</term><term>Consecutive stages</term><term>Contingency tables</term><term>Convenient mechanism</term><term>Decision problem</term><term>Decision theoretic framework</term><term>Dirichlet</term><term>Dirichlet process</term><term>Dirichlet process mixture models</term><term>Email</term><term>Empirical bayes</term><term>False discovery rate</term><term>Gamma distribution</term><term>Graphical statistics</term><term>Hierarchical model</term><term>Holcombe boulevard</term><term>Human data</term><term>Human phage display experiment</term><term>Identity line</term><term>Inference</term><term>Inference goal</term><term>Kolonin</term><term>Large count</term><term>Large number</term><term>Large sample size</term><term>Ller</term><term>Massive multiplicity problem</term><term>Mcmc simulation</term><term>Mixture model</term><term>Monte carlo</term><term>Mouse data</term><term>Nondecreasing counts</term><term>Nonparametric</term><term>Normal distributions</term><term>Optimal bayes rule</term><term>Optimal discovery procedure</term><term>Optimal rule</term><term>Other parameters</term><term>Other words</term><term>Overall slope</term><term>Pair counts</term><term>Parametric</term><term>Parametric model</term><term>Parametric version</term><term>Pasqualini</term><term>Peptide</term><term>Phage</term><term>Phage display</term><term>Phage display data</term><term>Phage display experiment</term><term>Poisson</term><term>Poisson model</term><term>Posterior</term><term>Posterior distribution</term><term>Posterior inference</term><term>Posterior probabilities</term><term>Posterior probability</term><term>Posterior simulation</term><term>Predictive distribution</term><term>Preferential binding</term><term>Pressler street</term><term>Probability model</term><term>Random baseline</term><term>Random distribution</term><term>Random peptides</term><term>Random probability measure</term><term>Same tissue</term><term>Sampling model</term><term>Schematic representation</term><term>Second stages</term><term>Semiparametric</term><term>Semiparametric bayesian inference</term><term>Semiparametric mixture model</term><term>Semiparametric model</term><term>Simulation</term><term>Simulation study</term><term>Simulation truth</term><term>Squared errors</term><term>Statistical methodology</term><term>Third stage</term><term>Third stages</term><term>Threshold value</term><term>Total mass parameter</term><term>Tripeptide</term><term>Tripeptides</term><term>Utility function</term><term>Variance</term><term>Vertical line</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We discuss inference for a human phage display experiment with three stages. The data are tripeptide counts by tissue and stage. The primary aim of the experiment is to identify ligands that bind with high affinity to a given tissue. We formalize the research question as inference about the monotonicity of mean counts over stages. The inference goal is then to identify a list of peptide–tissue pairs with significant increase over stages. We use a semiparametric Dirichlet process mixture of Poisson model. The posterior distribution under this model allows the desired inference about the monotonicity of mean counts. However, the desired inference summary as a list of peptide–tissue pairs with significant increase involves a massive multiplicity problem. We consider two alternative approaches to address this multiplicity issue. First we propose an approach based on the control of the posterior expected false discovery rate. We notice that the implied solution ignores the relative size of the increase. This motivates a second approach based on a utility function that includes explicit weights for the size of the increase.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Texas</li></region><settlement><li>Austin (Texas)</li></settlement><orgName><li>Université du Texas à Austin</li></orgName></list><tree><country name="États-Unis"><noRegion><name sortKey="Le N Ovelo, Luis G" sort="Le N Ovelo, Luis G" uniqKey="Le N Ovelo L" first="Luis G." last="Le N-Novelo">Luis G. Le N-Novelo</name></noRegion><name sortKey="Arap, Wadih" sort="Arap, Wadih" uniqKey="Arap W" first="Wadih" last="Arap">Wadih Arap</name><name sortKey="Arap, Wadih" sort="Arap, Wadih" uniqKey="Arap W" first="Wadih" last="Arap">Wadih Arap</name><name sortKey="Do, Kim Nh" sort="Do, Kim Nh" uniqKey="Do K" first="Kim-Anh" last="Do">Kim-Anh Do</name><name sortKey="Kolonin, Mikhail" sort="Kolonin, Mikhail" uniqKey="Kolonin M" first="Mikhail" last="Kolonin">Mikhail Kolonin</name><name sortKey="Muller, Peter" sort="Muller, Peter" uniqKey="Muller P" first="Peter" last="Müller">Peter Müller</name><name sortKey="Pasqualini, Renata" sort="Pasqualini, Renata" uniqKey="Pasqualini R" first="Renata" last="Pasqualini">Renata Pasqualini</name><name sortKey="Sun, Jessica" sort="Sun, Jessica" uniqKey="Sun J" first="Jessica" last="Sun">Jessica Sun</name><name sortKey="Sun, Jessica" sort="Sun, Jessica" uniqKey="Sun J" first="Jessica" last="Sun">Jessica Sun</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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