Limits...
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 scatterplotGenotypeWeight function.Data shown is the output from the scatterplotGenotypeWeight function during analysis of the ischemic peak contracture pressure from a study on rats comparing SS strain to SS-3BN/Mcwi strain. Both a regression line and a loess line (locally weighted line) fitted for each genotype.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4493137&req=5

pone.0131274.g009: Example output of the PhenStat scatterplotGenotypeWeight function.Data shown is the output from the scatterplotGenotypeWeight function during analysis of the ischemic peak contracture pressure from a study on rats comparing SS strain to SS-3BN/Mcwi strain. Both a regression line and a loess line (locally weighted line) fitted for each genotype.

Mentions: Alternatively, the Mixed Model method can be run to include a covariate to adjust for the animals’ weight (Eq 4). This is critical in phenotyping experiments, as body weight has been found to be a common phenotype with genotype alterations [24] and body size is a significant source of variation for many phenotyping variables [10,25]. Including a covariate for body weight can therefore increase the sensitivity of the study by accounting for more variation, or it can remove a confounding effect where the difference is arising solely from a body weight difference. Including body weight in the analysis, gives a model where batch was significant, the variances were heterogeneous across the genotype groups, and there was no evidence of sexual dimorphism. The final optimized model was used and it was found that there was no longer a statistically significant genotype effect (p value = 0.0959), the genotype differences was estimated at -6.23±3.73mmHg as the variation was now associated with body weight (p value = 4.08e-12). Looking at the body weight (Fig 8) we can see a large body weight phenotype particularly amongst the male rats, furthermore we can see that body weight correlates strongly with the variable of interest (Fig 9).


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 scatterplotGenotypeWeight function.Data shown is the output from the scatterplotGenotypeWeight function during analysis of the ischemic peak contracture pressure from a study on rats comparing SS strain to SS-3BN/Mcwi strain. Both a regression line and a loess line (locally weighted line) fitted for each genotype.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0131274.g009: Example output of the PhenStat scatterplotGenotypeWeight function.Data shown is the output from the scatterplotGenotypeWeight function during analysis of the ischemic peak contracture pressure from a study on rats comparing SS strain to SS-3BN/Mcwi strain. Both a regression line and a loess line (locally weighted line) fitted for each genotype.
Mentions: Alternatively, the Mixed Model method can be run to include a covariate to adjust for the animals’ weight (Eq 4). This is critical in phenotyping experiments, as body weight has been found to be a common phenotype with genotype alterations [24] and body size is a significant source of variation for many phenotyping variables [10,25]. Including a covariate for body weight can therefore increase the sensitivity of the study by accounting for more variation, or it can remove a confounding effect where the difference is arising solely from a body weight difference. Including body weight in the analysis, gives a model where batch was significant, the variances were heterogeneous across the genotype groups, and there was no evidence of sexual dimorphism. The final optimized model was used and it was found that there was no longer a statistically significant genotype effect (p value = 0.0959), the genotype differences was estimated at -6.23±3.73mmHg as the variation was now associated with body weight (p value = 4.08e-12). Looking at the body weight (Fig 8) we can see a large body weight phenotype particularly amongst the male rats, furthermore we can see that body weight correlates strongly with the variable of interest (Fig 9).

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