Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs

BMC Bioinformatics

Research

Ruedi Aebersold¹  Safia Thaminy²  Timothy Clough³  Olga Vitek⁴  Susanne Ragg⁵ 

[1] Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Switzerland;Faculty of Science, University of Zürich, Switzerland;Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Switzerland;Institute for Systems Biology, Seattle, WA, USA;Department of Statistics, Purdue University, West Lafayette, IN, USA;Department of Statistics, Purdue University, West Lafayette, IN, USA;Department of Computer Science, Purdue University, West Lafayette, IN, USA;School of Medicine, Indiana University, Indianapolis, IN, USA;

关键词: Label-free LC-MS/MS;    linear mixed effects models;    protein quantification;    quantitative proteomics;    statistical design of experiments;   

DOI  :  10.1186/1471-2105-13-S16-S6

来源: Springer

【 摘 要 】

BackgroundLiquid 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.ResultsWe 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.ConclusionsWe 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.

【 授权许可】

Unknown   
© Clough et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.