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\name{scores.boxplot}
\Rdversion{1.1}
\alias{scores.boxplot}
\title{
Creates boxplots of correlation/covariation scores
}
\description{
Given a list of correlation/covariation matrices, build boxplots for comparative purposes.
}
\usage{
scores.boxplot(corr_matrix_list, name_list, filepathroot, elite=25, high=275)
}
\arguments{
\item{corr_matrix_list}{
A list of correlation/covariation matrices to be compared
}
\item{name_list}{
The names of the correlation/covariation matrices
}
\item{filepathroot}{
The root of the full path name for the output file. if NULL, a BOXPLOT.png file will be created in tempdir(). If not NULL, the "_BOXPLOT.png" extension is added to the filepathroot.
}
\item{elite}{
An integer to determine the number of pairs with the highest and lowest scores (e.g. 25: pairs ranked 1 to 25 in decreasing or increasing order) to be colored with the "elite" color codes. Default is 25.
}
\item{high}{
An integer to determine the number of pairs with the next highest and lowest scores (e.g. 275: pairs ranked 26 to 275 in decreasing or increasing order) to be colored with the "high" color codes. Default is 275.
}
}
\details{
The correlation/covariation matrices contain the correlation/covariation scores for each pair of elements [i,j].
The boxplots will allow comparing these scores using color codes : the highest values are dark blue, the next highest values are light blue, the lowest values are red and the next lowest values are pink.
}
\value{
A pdf figure with boxplots to compare correlation/covariation scores
}
\references{
For an application of these boxplots, see :
Pele J, Abdi H, Moreau M, Thybert D and Chabbert M (2011) Multidimensional scaling reveals the main evolutionary pathways of class A G-protein-coupled receptors. \emph{PLoS ONE} \bold{6}: e19094. doi:10.1371.
}
\author{
Julien PELE and Antoine GARNIER
}
\examples{
#File path for output file
out <- tempdir()
file <- file.path(out,"test_seq")
#Importing MSA file
msf <- system.file("msa/toy_align.msf", package = "Bios2cor")
align <- import.msf(msf)
#Creating OMES correlation object
omes <- omes(align, gap_ratio = 0.2)
#Selecting correlation matrices
omes <-omes$Zscore
#Creating a list of matrices and plotting the boxplots in a graph
#One matrix
corr_matrix_list <- list(omes)
name <- c("omes")
scores.boxplot(corr_matrix_list, name, file, 25, 275)
}
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