Bioinformatics Advance Access published online on January 28, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp052
Retention Time Alignment Algorithms for LC/MS Data must consider Nonlinear Shifts
1 Fakultät Statistik, Technische Universität Dortmund, 44221 Dortmund, Germany.
2 Zentrum für Angewandte Proteomik, Dortmund, Germany.
3 Protagen AG, Otto-Hahn-Str. 15, 44227 Dortmund, Germany.
4 Fakultät für Informatik, Technische Universität Dortmund, 44221 Dortmund, Germany.
5 Medizinisches Proteom-Center (MPC), Ruhr-Universität Bochum, 44801 Bochum, Germany.
*To whom correspondence should be addressed. Miss Katharina Podwojski, E-mail: katharina.podwojski{at}uni-dortmund.de
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Abstract |
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Motivation: Proteomics has particularly evolved to become of high interest for the field of biomarker discovery and drug development. Especially the combination of liquid chromatography and mass spectrometry (LC/MS) has proven to be a powerful technique for analyzing protein mixtures. Clinically orientated proteomic studies will have to compare hundreds of LC/MS runs at a time. In order to compare different runs, sophisticated preprocessing steps have to be performed. An important step is the retention-time alignment of LC/MS runs. Especially nonlinear shifts in the retention time between pairs of LC/MS runs make this a crucial and nontrivial problem.
Results: for the purpose of demonstrating the particular importance of correcting nonlinear retention-time shifts we evaluate and compare different alignment algorithms. We present and analyze two versions of a new algorithm that is based on regression techniques, once assuming and estimating only linear shifts and once also allowing for the estimation of nonlinear shifts. As an example for another type of alignment method we use an established alignment algorithm based on shifting vectors that we adapted to also allow for correcting nonlinear shifts. In a simulation study we show that retention-time alignment procedures that can estimate nonlinear shifts yield clearly better alignments. This is even true under mild nonlinear deviations.
Availability: R code for the regression-based alignment methods and simulated datasets are available at http://www.statistik.tudortmund.de/genetik-publikationen-alignment.html
Contact: katharina.podwojski{at}tu-dortmund.de
Associate Editor: Prof. John Quackenbush
Both authors contributed equally to this work.
Received on October 7, 2008; revised on January 22, 2009; accepted on January 22, 2009
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