Title |
Exploring Massive, Genome Scale Datasets with the GenometriCorr Package
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Published in |
PLoS Computational Biology, May 2012
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DOI | 10.1371/journal.pcbi.1002529 |
Pubmed ID | |
Authors |
Alexander Favorov, Loris Mularoni, Leslie M. Cope, Yulia Medvedeva, Andrey A. Mironov, Vsevolod J. Makeev, Sarah J. Wheelan |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 27% |
Germany | 2 | 9% |
France | 1 | 5% |
Australia | 1 | 5% |
Netherlands | 1 | 5% |
India | 1 | 5% |
Grenada | 1 | 5% |
Japan | 1 | 5% |
United Kingdom | 1 | 5% |
Other | 1 | 5% |
Unknown | 6 | 27% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 13 | 59% |
Members of the public | 8 | 36% |
Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 3% |
Spain | 3 | 1% |
United Kingdom | 2 | <1% |
Netherlands | 1 | <1% |
Australia | 1 | <1% |
Sweden | 1 | <1% |
Denmark | 1 | <1% |
Switzerland | 1 | <1% |
Brazil | 1 | <1% |
Other | 3 | 1% |
Unknown | 200 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 72 | 33% |
Student > Ph. D. Student | 54 | 25% |
Student > Master | 16 | 7% |
Student > Bachelor | 13 | 6% |
Professor > Associate Professor | 12 | 5% |
Other | 43 | 20% |
Unknown | 10 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 125 | 57% |
Biochemistry, Genetics and Molecular Biology | 45 | 20% |
Computer Science | 10 | 5% |
Medicine and Dentistry | 7 | 3% |
Immunology and Microbiology | 4 | 2% |
Other | 13 | 6% |
Unknown | 16 | 7% |