title: Impact of smoothing on parameter estimation in quantitative DNA amplification experiments.
abstract: BACKGROUND
  Quantification cycle (Cq) and amplification efficiency (AE) are parameters mathematically extracted from raw data to characterize quantitative PCR (qPCR) reactions and quantify the copy number in a sample. Little attention has been paid to the effects of preprocessing and the use of smoothing or filtering approaches to compensate for noisy data. Existing algorithms largely are taken for granted, and it is unclear which of the various methods is most informative. We investigated the effect of smoothing and filtering algorithms on amplification curve data.
  
  
  METHODS
  We obtained published high-replicate qPCR data sets from standard block thermocyclers and other cycler platforms and statistically evaluated the impact of smoothing on Cq and AE.
  
  
  RESULTS
  Our results indicate that selected smoothing algorithms affect estimates of Cq and AE considerably. The commonly used moving average filter performed worst in all qPCR scenarios. The Savitzky-Golay smoother, cubic splines, and Whittaker smoother resulted overall in the least bias in our setting and exhibited low sensitivity to differences in qPCR AE, whereas other smoothers, such as running mean, introduced an AE-dependent bias.
  
  
  CONCLUSIONS
  The selection of a smoothing algorithm is an important step in developing data analysis pipelines for real-time PCR experiments. We offer guidelines for selection of an appropriate smoothing algorithm in diagnostic qPCR applications. The findings of our study were implemented in the R packages chipPCR and qpcR as a basis for the implementation of an analytical strategy.
authors.affiliation: University Medical Center Hamburg-Eppendorf, Hamburg, Germany;
authors.forename: Andrej-Nikolai
authors.initials: AN
authors.lastname: Spiess
authors.affiliation: Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany;
authors.forename: Claudia
authors.initials: C
authors.lastname: Deutschmann
authors.affiliation: University of Wroclaw, Wroclaw, Poland;
authors.forename: Michał
authors.initials: M
authors.lastname: Burdukiewicz
authors.affiliation: Fraunhofer-Institute for Chemical Technology, branch IMM (ICT-IMM)-Mainz, Mainz, Germany;
authors.forename: Ralf
authors.initials: R
authors.lastname: Himmelreich
authors.affiliation: Fraunhofer-Institute for Chemical Technology, branch IMM (ICT-IMM)-Mainz, Mainz, Germany; Darmstadt University of Applied Sciences, Darmstadt, Germany.
authors.forename: Katharina
authors.initials: K
authors.lastname: Klat
authors.affiliation: Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany;
authors.forename: Peter
authors.initials: P
authors.lastname: Schierack
authors.affiliation: Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany; stefan.roediger@hs-lausitz.de.
authors.forename: Stefan
authors.initials: S
authors.lastname: Rödiger
mesh_terms: D000465:Algorithms; D004247:DNA / Q000235:genetics*; D009010:Monte Carlo Method; D060888:Real-Time Polymerase Chain Reaction / Q000379:methods*; D012044:Regression Analysis
chemical_list: D004247:DNA