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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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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. |
format | Online Article Text |
id | pubmed-3364938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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