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dc.contributor.authorRohart, F
dc.contributor.authorEslami, A
dc.contributor.authorMatigian, N
dc.contributor.authorBougeard, S
dc.contributor.authorLê Cao, K-A
dc.date.accessioned2020-12-22T05:50:00Z
dc.date.available2020-12-22T05:50:00Z
dc.date.issued2017-02-27
dc.identifierpii: 10.1186/s12859-017-1553-8
dc.identifier.citationRohart, F., Eslami, A., Matigian, N., Bougeard, S. & Lê Cao, K. -A. (2017). MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms.. BMC Bioinformatics, 18 (1), pp.128-. https://doi.org/10.1186/s12859-017-1553-8.
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/11343/258365
dc.description.abstractBACKGROUND: 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/ .
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.titleMINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms.
dc.typeJournal Article
dc.identifier.doi10.1186/s12859-017-1553-8
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.source.titleBMC Bioinformatics
melbourne.source.volume18
melbourne.source.issue1
melbourne.source.pages128-
dc.rights.licenseCC BY
melbourne.elementsid1195544
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5327533
melbourne.contributor.authorLe Cao, Kim-Anh
dc.identifier.eissn1471-2105
melbourne.accessrightsOpen Access


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