Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness.

ABSTRACT: In quantitative proteomics, mass tag labeling techniques have been widely adopted in mass spectrometry experiments. These techniques allow peptides (short amino acid sequences) and proteins from multiple samples of a batch being detected and 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 no

SUBMITTER: Wang J 

PROVIDER: S-EPMC6797056 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature