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

Rohart, Florian, Eslami, Aida , Matigian, Nicholas, Bougeard, Stéphanie and Le Cao, Kim-Anh (2017) MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms. BMC Bioinformatics, 18 1: 128.1-128.13. doi:10.1186/s12859-017-1553-8


Author Rohart, Florian
Eslami, Aida
Matigian, Nicholas
Bougeard, Stéphanie
Le Cao, Kim-Anh
Title MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms
Journal name BMC Bioinformatics   Check publisher's open access policy
ISSN 1471-2105
Publication date 2017-02-27
Sub-type Article (original research)
DOI 10.1186/s12859-017-1553-8
Open Access Status DOI
Volume 18
Issue 1
Start page 128.1
End page 128.13
Total pages 13
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted 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/.
Keyword Integration
Multivariate
Classification
Transcriptome analysis
Algorithm
Partial-least-square
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
UQ Diamantina Institute Publications
Admin Only - UQ Diamantina Institute
 
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Created: Tue, 14 Mar 2017, 19:01:03 EST by Florian Rohart on behalf of School of Chemistry & Molecular Biosciences