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

Kurbatova N, Mason JC, Morgan H, Meehan TF, Karp NA - PLoS ONE (2015)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: The EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, United Kingdom.

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.

No MeSH data available.


Graphical representation of a typical Time Fixed Effect data structure.The Time as a Fixed Effect analysis requires a data structure where there are multiple batches but each batch has concurrent controls.
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pone.0131274.g002: Graphical representation of a typical Time Fixed Effect data structure.The Time as a Fixed Effect analysis requires a data structure where there are multiple batches but each batch has concurrent controls.

Mentions: This framework can be used in cases when there are up to five batches of treatment animals (e.g. knockout animals) and concurrent controls have been collected (Fig 2). Typically in high throughput studies the number of treated animals is limited in a concurrent design. That is why in the construct of the framework we limited the number of batches considered. The analysis requires removal of records that are not concurrent with treatment records or if treatment records lack concurrent controls. This is achieved by the TFDataset function, which will also report the data cleaning outcome impact on the data.


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

Kurbatova N, Mason JC, Morgan H, Meehan TF, Karp NA - PLoS ONE (2015)

Graphical representation of a typical Time Fixed Effect data structure.The Time as a Fixed Effect analysis requires a data structure where there are multiple batches but each batch has concurrent controls.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493137&req=5

pone.0131274.g002: Graphical representation of a typical Time Fixed Effect data structure.The Time as a Fixed Effect analysis requires a data structure where there are multiple batches but each batch has concurrent controls.
Mentions: This framework can be used in cases when there are up to five batches of treatment animals (e.g. knockout animals) and concurrent controls have been collected (Fig 2). Typically in high throughput studies the number of treated animals is limited in a concurrent design. That is why in the construct of the framework we limited the number of batches considered. The analysis requires removal of records that are not concurrent with treatment records or if treatment records lack concurrent controls. This is achieved by the TFDataset function, which will also report the data cleaning outcome impact on the data.

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: The EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, United Kingdom.

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.

No MeSH data available.