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Kernel-PCA data integration with enhanced interpretability

BACKGROUND: Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of...

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Detalles Bibliográficos
Autores principales: Reverter, Ferran, Vegas, Esteban, Oller, Josep M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101706/
https://www.ncbi.nlm.nih.gov/pubmed/25032747
http://dx.doi.org/10.1186/1752-0509-8-S2-S6
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author Reverter, Ferran
Vegas, Esteban
Oller, Josep M
author_facet Reverter, Ferran
Vegas, Esteban
Oller, Josep M
author_sort Reverter, Ferran
collection PubMed
description BACKGROUND: Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. RESULTS: We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. CONCLUSIONS: The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.
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spelling pubmed-41017062014-07-18 Kernel-PCA data integration with enhanced interpretability Reverter, Ferran Vegas, Esteban Oller, Josep M BMC Syst Biol Research BACKGROUND: Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. RESULTS: We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. CONCLUSIONS: The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge. BioMed Central 2014-03-13 /pmc/articles/PMC4101706/ /pubmed/25032747 http://dx.doi.org/10.1186/1752-0509-8-S2-S6 Text en Copyright © 2014 Reverter et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 Research
Reverter, Ferran
Vegas, Esteban
Oller, Josep M
Kernel-PCA data integration with enhanced interpretability
title Kernel-PCA data integration with enhanced interpretability
title_full Kernel-PCA data integration with enhanced interpretability
title_fullStr Kernel-PCA data integration with enhanced interpretability
title_full_unstemmed Kernel-PCA data integration with enhanced interpretability
title_short Kernel-PCA data integration with enhanced interpretability
title_sort kernel-pca data integration with enhanced interpretability
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101706/
https://www.ncbi.nlm.nih.gov/pubmed/25032747
http://dx.doi.org/10.1186/1752-0509-8-S2-S6
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