The Annals of Applied Statistics

Latent protein trees

Ricardo Henao, J. Will Thompson, M. Arthur Moseley, Geoffrey S. Ginsburg, Lawrence Carin, and Joseph E. Lucas

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat., Volume 7, Number 2 (2013), 691-713.

Dates
First available in Project Euclid: 27 June 2013

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1372338464

Digital Object Identifier
doi:10.1214/13-AOAS639

Mathematical Reviews number (MathSciNet)
MR3112914

Zentralblatt MATH identifier
06279850

Keywords
Proteomics data hierarchical factor model coalescent

Citation

Henao, Ricardo; Thompson, J. Will; Moseley, M. Arthur; Ginsburg, Geoffrey S.; Carin, Lawrence; Lucas, Joseph E. Latent protein trees. Ann. Appl. Stat. 7 (2013), no. 2, 691--713. doi:10.1214/13-AOAS639. https://projecteuclid.org/euclid.aoas/1372338464


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