Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. Results: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. Availability: The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).

Item Type:	Refereed Article
Research Division:	Mathematical Sciences
Research Group:	Statistics
Research Field:	Biostatistics
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the mathematical sciences
UTAS Author:	Karpievitch, Y (Dr Yuliya Karpievitch)
ID Code:	80883
Year Published:	2009
Web of Science® Times Cited:	116
Deposited By:	Mathematics and Physics
Deposited On:	2012-11-14
Last Modified:	2015-06-22
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