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Latent protein trees

dc.contributor.author Carin, Lawrence
dc.contributor.author Ginsburg, Geoffrey Steven
dc.contributor.author Henao, R
dc.contributor.author Lucas, Joseph E
dc.contributor.author Moseley, Martin Arthur III
dc.contributor.author Thompson, JW
dc.date.accessioned 2014-07-22T16:14:18Z
dc.date.issued 2013-06-01
dc.identifier.issn 1932-6157
dc.identifier.uri http://hdl.handle.net/10161/8948
dc.description.abstract Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore, there is the potential for very complex and informative correlational structure inherent in these data. Recent attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. However, to date, there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model such correlation structure in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation in the data to appropriately compress features into latent proteins and to estimate their correlation structure. We demonstrate the effectiveness of the model using artificial/benchmark data and in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza. © Institute of Mathematical Statistics, 2013.
dc.relation.ispartof Annals of Applied Statistics
dc.relation.isversionof 10.1214/13-AOAS639
dc.title Latent protein trees
dc.type Journal article
pubs.begin-page 691
pubs.end-page 713
pubs.issue 2
pubs.organisational-group Basic Science Departments
pubs.organisational-group Biomedical Engineering
pubs.organisational-group Biostatistics & Bioinformatics
pubs.organisational-group Clinical Science Departments
pubs.organisational-group Duke
pubs.organisational-group Duke Cancer Institute
pubs.organisational-group Electrical and Computer Engineering
pubs.organisational-group Institutes and Centers
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Medicine
pubs.organisational-group Medicine, Cardiology
pubs.organisational-group Pathology
pubs.organisational-group Pharmacology & Cancer Biology
pubs.organisational-group Pratt School of Engineering
pubs.organisational-group School of Medicine
pubs.organisational-group School of Nursing
pubs.organisational-group School of Nursing - Secondary Group
pubs.organisational-group Social Science Research Institute
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 7
dc.identifier.eissn 1941-7330


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