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ABSTRACT: 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.Availability
All R code available here: http://www.stat.tamu.edu/~adabney/share/xuan_code.zip.
SUBMITTER: Wang X
PROVIDER: S-EPMC3371829 | biostudies-literature | 2012 Jun
REPOSITORIES: biostudies-literature
Wang Xuan X Anderson Gordon A GA Smith Richard D RD Dabney Alan R AR
Bioinformatics (Oxford, England) 20120419 12
<h4>Motivation</h4>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.<h4>Results</h4>We outline a statistical method for protein differential expression, based ...[more]