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Activity Number: 386 - Recent Developments in Integrating Multiple-Omics Data in Complex Diseases
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #326490 Presentation
Title: A Multivariate Mixed-Effects Selection Model Framework for Batch-Processed Proteomics Data with Nonignorable Missingness
Author(s): Jiebiao Wang and Pei Wang and Donald Hedeker and Lin Chen*
Companies: Carnegie Mellon University and Icahn School of Medicine at Mount Sinai and University of Chicago and University of Chicago
Keywords: proteomics; multivariate; mixed-effects model; non-ignorable missing data; factor-analytic random effects; alternating direction method of multipliers

In quantitative proteomics, mass tag labeling techniques have been widely adopted in mass spectrometry experiments. These techniques allow peptides and proteins from multiple samples of a batch being quantified in a single experiment, and greatly improve the efficiency. However, the batch-processing of samples also results in severe batch effects and non-ignorable missing data occurring at the batch level. Here we developed a multivariate MIxed-effects SElection model framework (mvMISE) to jointly analyze multiple correlated peptides/proteins in labeled proteomics data, considering the batch effects and the non-ignorable missingness. We proposed two different models: to model multiple peptides from the same protein, we employed a factor-analytic random effects structure to characterize the high and similar correlations among peptides; and to model biological dependence among multiple proteins in a functional pathway, we introduced a graphical lasso penalty on the error precision matrix, and implemented an efficient algorithm. We applied the proposed methods to the breast cancer proteomic data from the Clinical Proteomic Tumor Analysis Consortium.

Authors who are presenting talks have a * after their name.

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