Journal Article

Retention time alignment algorithms for LC/MS data must consider non-linear shifts

Katharina Podwojski, Arno Fritsch, Daniel C. Chamrad, Wolfgang Paul, Barbara Sitek, Kai Stühler, Petra Mutzel, Christian Stephan, Helmut E. Meyer, Wolfgang Urfer, Katja Ickstadt and Jörg Rahnenführer

in Bioinformatics

Volume 25, issue 6, pages 758-764
Published in print March 2009 | ISSN: 1367-4803
Published online January 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp052
Retention time alignment algorithms for LC/MS data must consider non-linear shifts

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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 (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

Contact: katharina.podwojski@tu-dortmund.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6222 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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