Exploring massive, genome scale datasets with the GenometriCorr package

• Published in: PLoS computational biology Vol. 8; no. 5; p. e1002529
• Main Authors: Favorov, Alexander, Mularoni, Loris, Cope, Leslie M, Medvedeva, Yulia, Mironov, Andrey A, Makeev, Vsevolod J, Wheelan, Sarah J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.05.2012; Public Library of Science (PLoS)
• Subjects: Analysis; Animals; Biology; Chromosomes; Databases, Genetic; Epigenomics; Genetic Loci; Genome; Genomes; Genomics - methods; Humans; Information Storage and Retrieval; Internet; Mathematics; Models, Genetic; Models, Statistical; RNA, Transfer - genetics; Software; Statistical methods; Statistics, Nonparametric; User-Computer Interface; Saudi Arabia; Spain; User-Computer Interface; Epigenomics; Information Storage & Retrieval; Genomics; Humans; Databases, Genetic; Genetic Loci; Models, Statistical; RNA, Transfer; Animals; Statistics, Nonparametric; Models, Genetic; Software; Internet; Genome; Chromosomes
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1002529

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. 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.