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MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms
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 pro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5327533/ https://www.ncbi.nlm.nih.gov/pubmed/28241739 http://dx.doi.org/10.1186/s12859-017-1553-8 |
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author | Rohart, Florian Eslami, Aida Matigian, Nicholas Bougeard, Stéphanie Lê Cao, Kim-Anh |
author_facet | Rohart, Florian Eslami, Aida Matigian, Nicholas Bougeard, Stéphanie Lê Cao, Kim-Anh |
author_sort | Rohart, Florian |
collection | PubMed |
description | 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1553-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5327533 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53275332017-03-03 MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms Rohart, Florian Eslami, Aida Matigian, Nicholas Bougeard, Stéphanie Lê Cao, Kim-Anh BMC Bioinformatics Methodology Article 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/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1553-8) contains supplementary material, which is available to authorized users. BioMed Central 2017-02-27 /pmc/articles/PMC5327533/ /pubmed/28241739 http://dx.doi.org/10.1186/s12859-017-1553-8 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Rohart, Florian Eslami, Aida Matigian, Nicholas Bougeard, Stéphanie Lê Cao, Kim-Anh MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms |
title | MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms |
title_full | MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms |
title_fullStr | MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms |
title_full_unstemmed | MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms |
title_short | MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms |
title_sort | mint: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5327533/ https://www.ncbi.nlm.nih.gov/pubmed/28241739 http://dx.doi.org/10.1186/s12859-017-1553-8 |
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