title: Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences.
abstract: BACKGROUND
  The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between datasets, and their incomplete removal by pre-processing algorithms.
  
  
  METHODS
  To test this hypothesis, we systematically assessed the effects of pre-processing on biomarker classification using 24 different pre-processing methods and 15 distinct signatures of tumour hypoxia in 10 datasets (2,143 patients).
  
  
  RESULTS
  We confirm strong pre-processing effects for all datasets and signatures, and find that these differ between microarray versions. Importantly, exploiting different pre-processing techniques in an ensemble technique improved classification for a majority of signatures.
  
  
  CONCLUSIONS
  Assessing biomarkers using an ensemble of pre-processing techniques shows clear value across multiple diseases, datasets and biomarkers. Importantly, ensemble classification improves biomarkers with initially good results but does not result in spuriously improved performance for poor biomarkers. While further research is required, this approach has the potential to become a standard for transcriptomic biomarkers.
authors.forename: Natalie S
authors.initials: NS
authors.lastname: Fox
authors.forename: Maud H W
authors.initials: MH
authors.lastname: Starmans
authors.forename: Syed
authors.initials: S
authors.lastname: Haider
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambin
authors.affiliation: Informatics and Bio-computing Platform, Ontario Institute for Cancer Research, Toronto, Canada. Paul.Boutros@oicr.on.ca.
authors.forename: Paul C
authors.initials: PC
authors.lastname: Boutros
mesh_terms: D000465:Algorithms; D015687:Cell Hypoxia; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000473:pathology*; D011379:Prognosis; D015203:Reproducibility of Results; D046888:Tissue Array Analysis