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Exploring Massive, Genome Scale Datasets with the GenometriCorr Package

We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically...

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Detalles Bibliográficos
Autores principales: Favorov, Alexander, Mularoni, Loris, Cope, Leslie M., Medvedeva, Yulia, Mironov, Andrey A., Makeev, Vsevolod J., Wheelan, Sarah J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364938/
https://www.ncbi.nlm.nih.gov/pubmed/22693437
http://dx.doi.org/10.1371/journal.pcbi.1002529
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author Favorov, Alexander
Mularoni, Loris
Cope, Leslie M.
Medvedeva, Yulia
Mironov, Andrey A.
Makeev, Vsevolod J.
Wheelan, Sarah J.
author_facet Favorov, Alexander
Mularoni, Loris
Cope, Leslie M.
Medvedeva, Yulia
Mironov, Andrey A.
Makeev, Vsevolod J.
Wheelan, Sarah J.
author_sort Favorov, Alexander
collection PubMed
description We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. Availability and implementation: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor.
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spelling pubmed-33649382012-06-12 Exploring Massive, Genome Scale Datasets with the GenometriCorr Package Favorov, Alexander Mularoni, Loris Cope, Leslie M. Medvedeva, Yulia Mironov, Andrey A. Makeev, Vsevolod J. Wheelan, Sarah J. PLoS Comput Biol Research Article We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. Availability and implementation: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor. Public Library of Science 2012-05-31 /pmc/articles/PMC3364938/ /pubmed/22693437 http://dx.doi.org/10.1371/journal.pcbi.1002529 Text en Favorov et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Favorov, Alexander
Mularoni, Loris
Cope, Leslie M.
Medvedeva, Yulia
Mironov, Andrey A.
Makeev, Vsevolod J.
Wheelan, Sarah J.
Exploring Massive, Genome Scale Datasets with the GenometriCorr Package
title Exploring Massive, Genome Scale Datasets with the GenometriCorr Package
title_full Exploring Massive, Genome Scale Datasets with the GenometriCorr Package
title_fullStr Exploring Massive, Genome Scale Datasets with the GenometriCorr Package
title_full_unstemmed Exploring Massive, Genome Scale Datasets with the GenometriCorr Package
title_short Exploring Massive, Genome Scale Datasets with the GenometriCorr Package
title_sort exploring massive, genome scale datasets with the genometricorr package
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3364938/
https://www.ncbi.nlm.nih.gov/pubmed/22693437
http://dx.doi.org/10.1371/journal.pcbi.1002529
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