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Podwojski/etal/2009a: Retention time alignment algorithms for LC/MS data must consider non-linear shifts

Bibtype Article
Bibkey Podwojski/etal/2009a
Author Katharina Podwojski and Arno Fritsch and Daniel C Chamrad and Wolfgang Paul and Barbara Sitek and Kai Stühler and Petra Mutzel and Christian Stephan and Helmut E Meyer and Wolfgang Urfer and Katja Ickstadt and Jörg Rahnenführer
Title Retention time alignment algorithms for LC/MS data must consider non-linear shifts
Journal Bioinformatics
Volume 25
Number 6
Pages 758--764
Institution Fakultät Statistik, Technische Universität Dortmund, 44221 Dortmund, Germany. katharina.podwojski@tu-dortmund.de
Abstract 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 (rt) alignment of LC/MS runs. Especially non-linear shifts in the rt between pairs of LC/MS runs make this a crucial and non-trivial problem. RESULTS: For the purpose of demonstrating the particular importance of correcting non-linear rt 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 non-linear shifts. As an example for another type of alignment method we use an established alignment algorithm based on shifting vectors that we adapted to allow for correcting non-linear shifts also. In a simulation study, we show that rt alignment procedures that can estimate non-linear shifts yield clearly better alignments. This is even true under mild non-linear deviations. AVAILABILITY: R code for the regression-based alignment methods and simulated datasets are available at http://www.statistik.tu-dortmund.de/genetik-publikationen-alignment.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Month March
Year 2009
Projekt SFB876-A3
Url http://dx.doi.org/10.1093/bioinformatics/btp052
 
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