Phenostat: visualization and statistical tool for analysis of phenotyping data

Phenostat: visualization and statistical tool for analysis of phenotyping data The effective extraction of information from multidimensional data sets derived from phenotyping experiments is a growing challenge in biology. Data visualization tools are important resources that can aid in exploratory data analysis of complex data sets. Phenotyping experiments of model organisms produce data sets in which a large number of phenotypic measures are collected for each individual in a group. A critical initial step in the analysis of such multidimensional data sets is the exploratory analysis of data distribution and correlation. To facilitate the rapid visualization and exploratory analysis of multidimensional complex trait data, we have developed a user-friendly, web-based software tool called Phenostat. Phenostat is composed of a dynamic graphical environment that allows the user to inspect the distribution of multiple variables in a data set simultaneously. Individuals can be selected by directly clicking on the graphs and thus displaying their identity, highlighting corresponding values in all graphs, allowing their inclusion or exclusion from the analysis. Statistical analysis is provided by R package functions. Phenostat is particularly suited for rapid distribution and correlation analysis of subsets of data. An analysis of behavioral and physiologic data stemming from a large mouse phenotyping experiment using Phenostat reveals previously unsuspected correlations. Phenostat is freely available to academic institutions and nonprofit organizations and can be used from our website at http://www.bioinfo.embl.it/phenostat/ . http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Mammalian Genome Springer Journals

Phenostat: visualization and statistical tool for analysis of phenotyping data

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Publisher
Springer-Verlag
Copyright
Copyright © 2007 by Springer Science+Business Media, LLC
Subject
Life Sciences; Zoology ; Anatomy ; Cell Biology
ISSN
0938-8990
eISSN
1432-1777
D.O.I.
10.1007/s00335-007-9042-4
Publisher site
See Article on Publisher Site

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