Latent protein trees
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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.
Published Version (Please cite this version)10.1214/13-AOAS639
Publication InfoHenao, R; Thompson, JW; Moseley, MA; Ginsburg, GS; Carin, L; & Lucas, JE (2013). Latent protein trees. Annals of Applied Statistics, 7(2). pp. 691-713. 10.1214/13-AOAS639. Retrieved from https://hdl.handle.net/10161/8948.
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James L. Meriam Distinguished Professor of Electrical and Computer Engineering
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989 he joined the Electrical Engineering Department at Polytechnic University (Brooklyn) as an Assistant Professor, and became an Associate Professor there in 1994. In September 1995 he joined the Electrical and Computer Engineering (ECE) Department at Duke University, where he is now a Professor. He was ECE Department Chair from 2011
Professor of Medicine
Dr. Geoffrey S. Ginsburg's research interests are in the development of novel paradigms for developing and translating genomic information into medical practice and the integration of personalized medicine into health care.
Assistant Professor in Biostatistics & Bioinformatics
Associate Research Professor in the Social Science Research Institute
This author no longer has a Scholars@Duke profile, so the information shown here reflects their Duke status at the time this item was deposited.
Associate Professor in Medicine
Adjunct Assistant Professor in the Department of Pharmacology & Cancer Biology
Dr. Thompson's research focuses on the development and deployment of proteomics and metabolomics mass spectrometry techniques for the analysis of biological systems. He is the Assistant Director of the Proteomics and Metabolomics Shared Resource in the Duke School of Medicine. In this role, he enjoys utilizing mass spectrometry 'omics techniques in research collaborations with investigators throughout the Duke community.
Alphabetical list of authors with Scholars@Duke profiles.