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

• Published in: Bioinformatics Vol. 25; no. 6; pp. 758 - 764
• Main Authors: Podwojski, Katharina, Fritsch, Arno, Chamrad, Daniel C., Paul, Wolfgang, Sitek, Barbara, Stühler, Kai, Mutzel, Petra, Stephan, Christian, Meyer, Helmut E., Urfer, Wolfgang, Ickstadt, Katja, Rahnenführer, Jörg
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.03.2009; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biological and medical sciences; Chromatography, Liquid - methods; Computer Simulation; Fundamental and applied biological sciences. Psychology; General aspects; Mass Spectrometry - methods; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Proteins - chemistry; Proteome - chemistry; Proteomics - methods; Algorithm; Data
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btp052

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.