title: Test set bias affects reproducibility of gene signatures.
abstract: BACKGROUND
  Prior to applying genomic predictors to clinical samples, the genomic data must be properly normalized to ensure that the test set data are comparable to the data upon which the predictor was trained. The most effective normalization methods depend on data from multiple patients. From a biomedical perspective, this implies that predictions for a single patient may change depending on which other patient samples they are normalized with. This test set bias will occur when any cross-sample normalization is used before clinical prediction.
  
  
  RESULTS
  We demonstrate that results from existing gene signatures which rely on normalizing test data may be irreproducible when the patient population changes composition or size using a set of curated, publicly available breast cancer microarray experiments. As an alternative, we examine the use of gene signatures that rely on ranks from the data and show why signatures using rank-based features can avoid test set bias while maintaining highly accurate classification, even across platforms.
  
  
  METHODS
  The code, data and instructions necessary to reproduce our entire analysis is available at https://github.com/prpatil/testsetbias.
authors: Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA.
authors: Prasad
authors: P
authors: Patil
authors: Institut de Recherches Cliniques de Montréal, Montreal, Quebec H2W 1R7, Canada.
authors: Pierre-Olivier
authors: PO
authors: Bachant-Winner
authors: Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 1L7, Canada and Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada.
authors: Benjamin
authors: B
authors: Haibe-Kains
authors: Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA.
authors: Jeffrey T
authors: JT
authors: Leek
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D001943:Breast Neoplasms / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D023281:Genomics / Q000379:methods*; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism; D015203:Reproducibility of Results; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; C508053:ERBB2 protein, human