Methodology

Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments

Ole Schulz-Trieglaff^1,2 , Egidijus Machtejevas³ , Knut Reinert² , Hartmut Schlüter⁴ , Joachim Thiemann⁴ and Klaus Unger³

¹  International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany

²  Department Computer Science and Mathematics, Freie Universität Berlin, Berlin, Germany

³  Institute for Anorganic and Analytical Chemistry, Johannes Gutenberg-Universität, Mainz, Germany

⁴  Core Facility Protein Analytics, Charité – Universitätsmedizin Berlin, Berlin, Germany

author email corresponding author email

BioData Mining 2009, 2:4doi:10.1186/1756-0381-2-4

Published:	7 April 2009

Abstract

Background

Quality assessment methods, that are common place in engineering and industrial production, are not widely spread in large-scale proteomics experiments. But modern technologies such as Multi-Dimensional Liquid Chromatography coupled to Mass Spectrometry (LC-MS) produce large quantities of proteomic data. These data are prone to measurement errors and reproducibility problems such that an automatic quality assessment and control become increasingly important.

Results

We propose a methodology to assess the quality and reproducibility of data generated in quantitative LC-MS experiments. We introduce quality descriptors that capture different aspects of the quality and reproducibility of LC-MS data sets. Our method is based on the Mahalanobis distance and a robust Principal Component Analysis.

Conclusion

We evaluate our approach on several data sets of different complexities and show that we are able to precisely detect LC-MS runs of poor signal quality in large-scale studies.