title: Retention time alignment algorithms for LC/MS data must consider non-linear shifts.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  R code for the regression-based alignment methods and simulated datasets are available at http://www.statistik.tu-dortmund.de/genetik-publikationen-alignment.html.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Fakultät Statistik, Technische Universität Dortmund, 44221 Dortmund, Germany. katharina.podwojski@tu-dortmund.de
authors.forename: Katharina
authors.initials: K
authors.lastname: Podwojski
authors.forename: Arno
authors.initials: A
authors.lastname: Fritsch
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Chamrad
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Paul
authors.forename: Barbara
authors.initials: B
authors.lastname: Sitek
authors.forename: Kai
authors.initials: K
authors.lastname: Stühler
authors.forename: Petra
authors.initials: P
authors.lastname: Mutzel
authors.forename: Christian
authors.initials: C
authors.lastname: Stephan
authors.forename: Helmut E
authors.initials: HE
authors.lastname: Meyer
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Urfer
authors.forename: Katja
authors.initials: K
authors.lastname: Ickstadt
authors.forename: Jörg
authors.initials: J
authors.lastname: Rahnenführer
mesh_terms: D000465:Algorithms; D002853:Chromatography, Liquid / Q000379:methods*; D003198:Computer Simulation; D013058:Mass Spectrometry / Q000379:methods*; D011506:Proteins / Q000737:chemistry; D020543:Proteome / Q000737:chemistry; D040901:Proteomics / Q000379:methods*
chemical_list: D011506:Proteins; D020543:Proteome