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    Exploring massive, genome scale datasets with the genometricorr package

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    Article-PLoS_Compu-Exploring_- ...
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    Type
    Article
    Authors
    Favorov, Alexander
    Mularoni, Loris
    Cope, Leslie M.
    Medvedeva, Yulia
    Mironov, Andrey A.
    Makeev, Vsevolod J.
    Wheelan, Sarah J.
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Date
    2012-05-31
    Permanent link to this record
    http://hdl.handle.net/10754/325275
    
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    Abstract
    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. © 2012 Favorov et al.
    Citation
    Favorov A, Mularoni L, Cope LM, Medvedeva Y, Mironov AA, et al. (2012) Exploring Massive, Genome Scale Datasets with the GenometriCorr Package. PLoS Comput Biol 8: e1002529. doi:10.1371/journal.pcbi.1002529.
    Publisher
    Public Library of Science (PLoS)
    Journal
    PLoS Computational Biology
    ISSN
    1553734X
    DOI
    10.1371/journal.pcbi.1002529
    PubMed ID
    22693437
    PubMed Central ID
    PMC3364938
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pcbi.1002529
    Scopus Count
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    Articles; Computational Bioscience Research Center (CBRC)

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