title: Improving the accuracy of expression data analysis in time course experiments using resampling.
abstract: BACKGROUND
  As time series experiments in higher eukaryotes usually obtain data from different individuals collected at the different time points, a time series sample itself is not equivalent to a true biological replicate but is, rather, a combination of several biological replicates. The analysis of expression data derived from a time series sample is therefore often performed with a low number of replicates due to budget limitations or limitations in sample availability. In addition, most algorithms developed to identify specific patterns in time series dataset do not consider biological variation in samples collected at the same conditions.
  
  
  RESULTS
  Using artificial time course datasets, we show that resampling considerably improves the accuracy of transcripts identified as rhythmic. In particular, the number of false positives can be greatly reduced while at the same time the number of true positives can be maintained in the range of other methods currently used to determine rhythmically expressed genes.
  
  
  CONCLUSIONS
  The resampling approach described here therefore increases the accuracy of time series expression data analysis and furthermore emphasizes the importance of biological replicates in identifying oscillating genes. Resampling can be used for any time series expression dataset as long as the samples are acquired from independent individuals at each time point.
authors.affiliation: Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, D-07745, Jena, Germany. wwalter@cnic.es.
authors.forename: Wencke
authors.initials: W
authors.lastname: Walter
authors.affiliation: Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Naturfagbygget, Dramsvegen 201, 9037, Tromsø, Norway. bernd.ketelsen@uit.no.
authors.forename: Bernd
authors.initials: B
authors.lastname: Striberny
authors.affiliation: Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, D-07745, Jena, Germany. egaquerel@ice.mpg.de.|Center for Organismal Studies, University of Heidelberg, Im Neuenheimer Feld 360, 69120, Heidelberg, Germany. egaquerel@ice.mpg.de.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Gaquerel
authors.affiliation: Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, D-07745, Jena, Germany. baldwin@ice.mpg.de.
authors.forename: Ian T
authors.initials: IT
authors.lastname: Baldwin
authors.affiliation: Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, D-07745, Jena, Germany. skim@ice.mpg.de.|Center for Genome Engineering, Institute for Basic Science, Gwanak-ro 1, Gwanak-gu, Seoul, 151-747, South Korea. skim@ice.mpg.de.
authors.forename: Sang-Gyu
authors.initials: SG
authors.lastname: Kim
authors.affiliation: Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Naturfagbygget, Dramsvegen 201, 9037, Tromsø, Norway. ines.heiland@uit.no.
authors.forename: Ines
authors.initials: I
authors.lastname: Heiland
mesh_terms: D000465:Algorithms; D057906:Circadian Clocks / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D007700:Kinetics; D015203:Reproducibility of Results