Phenotype

Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis.

Installation

install.packages("Phenotype")

Usage

Remove outliers from phenotypic data

library("Phenotype")
data("wheatds")
inlier <- outlier(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS", mode = "blup")

Calculate statistical indicators of phenotypic data

data("wheatds")
inlier <- outlier(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS", mode = "blup")
stat_out <- stat(x = inlier, sample = "Sample", phe = "inlier")

Histogram drawing

data("wheatds")
inlier <- outlier(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS", mode = "blup")
stat_out <- stat(x = inlier, sample = "Sample", phe = "inlier")
histplot(x = stat_out$mean)

Performs the Best Linear Unbiased Prediction (BLUP)

data("wheatds")
blup_out <- blup(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS")

Contact

For any bugs/issues/suggestions, please send emails to: Peng Zhao pengzhao@nwafu.edu.cn.