Assessing reproducibility of matrix factorization methods in independent transcriptomes

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dc.contributor.authorCantini, Laura
dc.contributor.authorKairov, Ulykbek
dc.contributor.authorde Reynies, Aurelien
dc.contributor.authorBarillot, Emmanuel
dc.contributor.authorRadvanyi, Francois
dc.contributor.authorZinovyev, Andrei
dc.date.accessioned2019-12-11T09:30:18Z
dc.date.available2019-12-11T09:30:18Z
dc.date.issued2019
dc.descriptionhttps://www.ncbi.nlm.nih.gov/pubmed/30938767en_US
dc.description.abstractMotivation Matrix factorization (MF) methods are widely used in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). MF algorithms have never been compared based on the between-datasets reproducibility of their outputs in similar independent datasets. Lack of this knowledge might have a crucial impact when generalizing the predictions made in a study to others. Results We systematically test widely used MF methods on several transcriptomic datasets collected from the same cancer type (14 colorectal, 8 breast and 4 ovarian cancer transcriptomic datasets). Inspired by concepts of evolutionary bioinformatics, we design a novel framework based on Reciprocally Best Hit (RBH) graphs in order to benchmark the MF methods for their ability to produce generalizable components. We show that a particular protocol of application of independent component analysis (ICA), accompanied by a stabilization procedure, leads to a significant increase in the between-datasets reproducibility. Moreover, we show that the signals detected through this method are systematically more interpretable than those of other standard methods. We developed a user-friendly tool for performing the Stabilized ICA-based RBH meta-analysis. We apply this methodology to the study of colorectal cancer (CRC) for which 14 independent transcriptomic datasets can be collected. The resulting RBH graph maps the landscape of interconnected factors associated to biological processes or to technological artifacts. These factors can be used as clinical biomarkers or robust and tumor-type specific transcriptomic signatures of tumoral cells or tumoral microenvironment. Their intensities in different samples shed light on the mechanistic basis of CRC molecular subtyping. Availability and implementation The RBH construction tool is available from http://goo.gl/DzpwYp Supplementary information Supplementary data are available at Bioinformatics online.en_US
dc.identifier.citationCantini, L., Kairov, U., de Reyniès, A., Barillot, E., Radvanyi, F., & Zinovyev, A. (2019). Assessing reproducibility of matrix factorization methods in independent transcriptomes. Bioinformatics, 35(21), 4307–4313. https://doi.org/10.1093/bioinformatics/btz225en_US
dc.identifier.other10.1093/bioinformatics/btz225
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/4391
dc.language.isoenen_US
dc.publisherOXFORD UNIV PRESSen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectAssessing reproducibilityen_US
dc.subjectGene expressionen_US
dc.subjectMatrix factorizationen_US
dc.subjectMFen_US
dc.subjectcolorectal canceren_US
dc.subjectCRCen_US
dc.subjectbreast canceren_US
dc.subjectBRCAen_US
dc.titleAssessing reproducibility of matrix factorization methods in independent transcriptomesen_US
dc.typeArticleen_US
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