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

View ORCID ProfileF. Rohart, A. Eslami, N. Matigian, S. Bougeard, View ORCID ProfileK-A. Lê Cao
doi: https://doi.org/10.1101/070813
F. Rohart
1The University of Queensland Diamantina Institute, Translational Research Institute, QLD 4102, Australia,
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A. Eslami
2Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, BC Canada V6T 2A1
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N. Matigian
1The University of Queensland Diamantina Institute, Translational Research Institute, QLD 4102, Australia,
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S. Bougeard
3Department of Epidemiology, French agency for food, environmental and occupational health safety (ANSES), 22440 Ploufragan, France
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K-A. Lê Cao
1The University of Queensland Diamantina Institute, Translational Research Institute, QLD 4102, Australia,
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Abstract

The solution to identify a reliable molecular signature in transcriptomics high-throughput experiments is to increase sample size by combining independent but related studies. However, those data sets are generated using different protocols and technological platforms, which results in unwanted systematic variation that strongly confounds the integrative analysis results. We introduce a Multi-variate INTegrative method, MINT, that identifies a highly reproducible, accurate and predictive gene signature to classify sample phenotypes while accounting for platform and study variation. MINT led to superior and unbiased classification performance compared to other existing methods, and identified highly relevant gene signatures when integrating two multi-transcriptomics studies.

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Posted August 22, 2016.
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MINT: A multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms
F. Rohart, A. Eslami, N. Matigian, S. Bougeard, K-A. Lê Cao
bioRxiv 070813; doi: https://doi.org/10.1101/070813
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MINT: A multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms
F. Rohart, A. Eslami, N. Matigian, S. Bougeard, K-A. Lê Cao
bioRxiv 070813; doi: https://doi.org/10.1101/070813

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