MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms

Stéphanie Bougeard,Florian Rohart,Aida Eslami,Nicholas Matigian,Kim-Anh Lê Cao

doi:10.1186/s12859-017-1553-8

Abstract

BackgroundMolecular 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.ResultsTo 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.ConclusionsMINT 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/.

Highlights

Molecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies
In the MicroArray Quality Control (MAQC) project, poor overlap of differentially expressed genes was observed across different microarray platforms (∼ 60%), with low concordance observed between microarray and RNA-seq technologies [6]
Case studies We demonstrate the ability of Multivariate integration method (MINT) to identify the true positive genes on the MAQC project, highlight the strong properties of our method to combine independent data sets in order to identify reproducible and predictive gene signatures on two other biological studies

Summary

Introduction

Molecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. High-throughput technologies, based on microarray and RNA-sequencing, are being used to identify biomarkers or gene signatures that distinguish disease subgroups, predict cell phenotypes or classify responses to therapeutic drugs Few of these findings are reproduced when assessed in subsequent studies and even fewer lead to clinical applications [1, 2]. In the MicroArray Quality Control (MAQC) project, poor overlap of differentially expressed genes was observed across different microarray platforms (∼ 60%), with low concordance observed between microarray and RNA-seq technologies [6] These confounding factors and sources of systematic variation must be accounted for, when combining independent studies, to enable genuine biological variation to be identified

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