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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.


Related in: MedlinePlus

Example output of the PhenStat qqplotGenotype function.Data shown is the output from the qqplotGenotype function during the analysis of the ischemic peak contracture pressure from a study on rats comparing SS strain to SS-3BN/Mcwi strain when fitted with the Mixed Model method including body weight. This function allows an assessment of the model by examining the behaviour of the residuals, which is the difference between the measures and the model estimated values. A good model will have a normal distribution of residuals and the plot allows this to be assessed for each group being compared. Looking at the example, the residuals for both groups show no systematic deviations from the line indicating the model is fitting this data well.
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pone.0131274.g007: Example output of the PhenStat qqplotGenotype function.Data shown is the output from the qqplotGenotype function during the analysis of the ischemic peak contracture pressure from a study on rats comparing SS strain to SS-3BN/Mcwi strain when fitted with the Mixed Model method including body weight. This function allows an assessment of the model by examining the behaviour of the residuals, which is the difference between the measures and the model estimated values. A good model will have a normal distribution of residuals and the plot allows this to be assessed for each group being compared. Looking at the example, the residuals for both groups show no systematic deviations from the line indicating the model is fitting this data well.

Mentions: Processing the same data using the Mixed Model method and excluding weight (Eq 3) completed a model optimization process and for this dataset, batch was not found to be significant source of variation, variance was found to be homogenous and the genotype effect was found to be sexual dimorphic as it depended on the sex of the animals. The final optimized model was used and it was found that there was a statistically significant genotype effect (p value = 9.92e-6) classified as sexual dimorphic as the effect was larger in the males (-26.65±2.44mmHg) than the females (-16.53±2.88 mmHg). A variety of diagnostics can be run to assess the model fit, for example Fig 7 assesses the residuals (differences between the measured and fitted) for normality.


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)

Example output of the PhenStat qqplotGenotype function.Data shown is the output from the qqplotGenotype function during the analysis of the ischemic peak contracture pressure from a study on rats comparing SS strain to SS-3BN/Mcwi strain when fitted with the Mixed Model method including body weight. This function allows an assessment of the model by examining the behaviour of the residuals, which is the difference between the measures and the model estimated values. A good model will have a normal distribution of residuals and the plot allows this to be assessed for each group being compared. Looking at the example, the residuals for both groups show no systematic deviations from the line indicating the model is fitting this data well.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0131274.g007: Example output of the PhenStat qqplotGenotype function.Data shown is the output from the qqplotGenotype function during the analysis of the ischemic peak contracture pressure from a study on rats comparing SS strain to SS-3BN/Mcwi strain when fitted with the Mixed Model method including body weight. This function allows an assessment of the model by examining the behaviour of the residuals, which is the difference between the measures and the model estimated values. A good model will have a normal distribution of residuals and the plot allows this to be assessed for each group being compared. Looking at the example, the residuals for both groups show no systematic deviations from the line indicating the model is fitting this data well.
Mentions: Processing the same data using the Mixed Model method and excluding weight (Eq 3) completed a model optimization process and for this dataset, batch was not found to be significant source of variation, variance was found to be homogenous and the genotype effect was found to be sexual dimorphic as it depended on the sex of the animals. The final optimized model was used and it was found that there was a statistically significant genotype effect (p value = 9.92e-6) classified as sexual dimorphic as the effect was larger in the males (-26.65±2.44mmHg) than the females (-16.53±2.88 mmHg). A variety of diagnostics can be run to assess the model fit, for example Fig 7 assesses the residuals (differences between the measured and fitted) for normality.

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.


Related in: MedlinePlus