Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs
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Date
2012-11
Publication Type
Journal Article
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yes
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Abstract
Background
Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is widely used for quantitative proteomic investigations. The typical output of such studies is a list of identified and quantified peptides. The biological and clinical interest is, however, usually focused on quantitative conclusions at the protein level. Furthermore, many investigations ask complex biological questions by studying multiple interrelated experimental conditions. Therefore, there is a need in the field for generic statistical models to quantify protein levels even in complex study designs.
Results
We propose a general statistical modeling approach for protein quantification in arbitrary complex experimental designs, such as time course studies, or those involving multiple experimental factors. The approach summarizes the quantitative experimental information from all the features and all the conditions that pertain to a protein. It enables both protein significance analysis between conditions, and protein quantification in individual samples or conditions. We implement the approach in an open-source R-based software package MSstats suitable for researchers with a limited statistics and programming background.
Conclusions
We demonstrate, using as examples two experimental investigations with complex designs, that a simultaneous statistical modeling of all the relevant features and conditions yields a higher sensitivity of protein significance analysis and a higher accuracy of protein quantification as compared to commonly employed alternatives. The software is available at http://www.stat.purdue.edu/~ovitek/Software.html.
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published
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Journal / series
Volume
13 (Supplement 16)
Pages / Article No.
Publisher
BioMed Central
Event
Edition / version
Methods
Software
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Date collected
Date created
Subject
Label-free LC-MS/MS; linear mixed effects models; protein quantification; quantitative proteomics; statistical design of experiments
Organisational unit
03663 - Aebersold, Rudolf (emeritus) / Aebersold, Rudolf (emeritus)