Exploring massive, genome scale datasets with the genometricorr package

Alexander Favorov, Loris Mularoni, Leslie M. Cope, Yulia Medvedeva, Andrey A. Mironov, Vsevolod J. Makeev, Sarah J. Wheelan

Research output: Contribution to journalArticlepeer-review

120 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)e1002529
JournalPLoS Computational Biology
Issue number5
StatePublished - May 31 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Ecology
  • Cellular and Molecular Neuroscience
  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Modeling and Simulation
  • Computational Theory and Mathematics
  • Molecular Biology


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