期刊
BIOINFORMATICS
卷 28, 期 12, 页码 1586-1591出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bts193
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资金
- NIH National Center for Research Resources [RR18522]
- National Institute of Allergy and Infectious Diseases NIH/DHHS [Y1-AI-8401, U54AI081680]
- King Abdullah University of Science and Technology (KAUST) [KUS-C1-016-04]
- US Department of Energys Office of Biological and Environmental Research at Pacific Northwest National Laboratory in Richland, Washington
- US Department of Energy [DE-AC05-76RL0 1830]
Motivation: Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical MS-based proteomics datasets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. Results: We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of 'presence/absence,' we enable the selection of proteins not typically amenable to quantitative analysis; e.g. 'one-state' proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence/absence analysis of a given dataset in a principled way, resulting in a single list of selected proteins with a single-associated false discovery rate.
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