title: MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms.
abstract: BACKGROUND
  Molecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. One solution is to combine independent studies into a single integrative analysis, additionally increasing sample size. However, the different protocols and technological platforms across transcriptomic studies produce unwanted systematic variation that strongly confounds the integrative analysis results. When studies aim to discriminate an outcome of interest, the common approach is a sequential two-step procedure; unwanted systematic variation removal techniques are applied prior to classification methods.
  
  
  RESULTS
  To limit the risk of overfitting and over-optimistic results of a two-step procedure, we developed a novel multivariate integration method, MINT, that simultaneously accounts for unwanted systematic variation and identifies predictive gene signatures with greater reproducibility and accuracy. In two biological examples on the classification of three human cell types and four subtypes of breast cancer, we combined high-dimensional microarray and RNA-seq data sets and MINT identified highly reproducible and relevant gene signatures predictive of a given phenotype. MINT led to superior classification and prediction accuracy compared to the existing sequential two-step procedures.
  
  
  CONCLUSIONS
  MINT is a powerful approach and the first of its kind to solve the integrative classification framework in a single step by combining multiple independent studies. MINT is computationally fast as part of the mixOmics R CRAN package, available at http://www.mixOmics.org/mixMINT/ and http://cran.r-project.org/web/packages/mixOmics/ .
authors.affiliation: The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, 4102, QLD, Australia.
authors.forename: Florian
authors.initials: F
authors.lastname: Rohart
authors.affiliation: Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC V6Z 1Y6, Canada.
authors.forename: Aida
authors.initials: A
authors.lastname: Eslami
authors.affiliation: The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, 4102, QLD, Australia.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Matigian
authors.affiliation: French agency for food, environmental and occupational health safety (Anses), Department of Epidemiology, Ploufragan, 22440, France.
authors.forename: Stéphanie
authors.initials: S
authors.lastname: Bougeard
authors.affiliation: The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, 4102, QLD, Australia. k.lecao@uq.edu.au.
authors.forename: Kim-Anh
authors.initials: KA
authors.lastname: Lê Cao
mesh_terms: D020869:Gene Expression Profiling; D006801:Humans; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D018401:Sample Size
keywords: Algorithm; Classification; Integration; Multivariate; Partial-least-square; Transcriptome analysis