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PhenStat: A Tool Kit for Standardized Analysis of High Throughput Phenotypic Data

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  • Natalja Kurbatova
  • Jeremy C Mason
  • Hugh Morgan
  • Terrence F Meehan
  • Natasha A Karp

Abstract

The lack of reproducibility with animal phenotyping experiments is a growing concern among the biomedical community. One contributing factor is the inadequate description of statistical analysis methods that prevents researchers from replicating results even when the original data are provided. Here we present PhenStat – a freely available R package that provides a variety of statistical methods for the identification of phenotypic associations. The methods have been developed for high throughput phenotyping pipelines implemented across various experimental designs with an emphasis on managing temporal variation. PhenStat is targeted to two user groups: small-scale users who wish to interact and test data from large resources and large-scale users who require an automated statistical analysis pipeline. The software provides guidance to the user for selecting appropriate analysis methods based on the dataset and is designed to allow for additions and modifications as needed. The package was tested on mouse and rat data and is used by the International Mouse Phenotyping Consortium (IMPC). By providing raw data and the version of PhenStat used, resources like the IMPC give users the ability to replicate and explore results within their own computing environment.

Suggested Citation

  • Natalja Kurbatova & Jeremy C Mason & Hugh Morgan & Terrence F Meehan & Natasha A Karp, 2015. "PhenStat: A Tool Kit for Standardized Analysis of High Throughput Phenotypic Data," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-16, July.
  • Handle: RePEc:plo:pone00:0131274
    DOI: 10.1371/journal.pone.0131274
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    1. Ross N. W. Kettleborough & Elisabeth M. Busch-Nentwich & Steven A. Harvey & Christopher M. Dooley & Ewart de Bruijn & Freek van Eeden & Ian Sealy & Richard J. White & Colin Herd & Isaac J. Nijman & Fr, 2013. "A systematic genome-wide analysis of zebrafish protein-coding gene function," Nature, Nature, vol. 496(7446), pages 494-497, April.
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