Using Multivariate Mixed-Effects Selection Models for Analyzing Batch-Processed Proteomics Data with Non-Ignorable Missingness

• Activity Number: 598 - Statistical Learning with Unconventional Missing Data
• Type: Topic Contributed
• Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
• Sponsor: International Chinese Statistical Association
• Author(s): Lin Chen* and Jiebiao Wang and Pei Wang and Donald Hedeker
• Companies: University of Chicago and Carnegie Mellon University and Icahn School of Medicine at Mount Sinai and University of Chicago
• Keywords: non-ignorable missing data; multivariate; batch effects; proteomics data; batch-level missingness

Abstract

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 as such greatly improve the efficiency of protein profiling. However, the batch-processing of samples also results in severe batch effects and non-ignorable missing data occurring at the batch level. Motivated by the breast cancer proteomic data from the Clinical Proteomic Tumor Analysis Consortium, in this work, we developed two tailored multivariate MIxed-effects SElection models (mvMISE) to jointly analyze multiple correlated peptides/proteins in labeled proteomics data, considering the batch effects and the non-ignorable missingness. Applying the proposed methods to the motivating data set, we identified phosphoproteins and biological pathways that showed different activity patterns in triple negative breast tumors versus other breast tumors. The proposed methods can also be applied to other high-dimensional multivariate analyses based on clustered data with or without non-ignorable missingness.