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


CONCLUSIONS
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.
Schulz-Trieglaff
Ole
O
International Max Planck Research School for Computational Biology and Scientific Computing, Berlin, Germany. trieglaf@inf.fu-berlin.de
Machtejevas
Egidijus
E
Reinert
Knut
K
Schlüter
Hartmut
H
Thiemann
Joachim
J
Unger
Klaus
K