期刊
BIOINFORMATICS
卷 25, 期 16, 页码 2028-2034出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btp362
关键词
-
类别
资金
- PNNL
- NIH [R25-CA-90301, DK070146]
- National Institute of Allergy and Infectious Diseases [Y1-AI-4894-01]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据