scores.boxplot {Bios2cor} | R Documentation |
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=NULL, elite=25, high=275)
Arguments
corr_matrix_list |
A list of correlation/covariation matrices to be compared |
name_list |
The names of the correlation/covariation matrices |
filepathroot |
The root of the full path name for the output file. Default is NULL (a BOXPLOT.png file will be created). If not NULL, the "_BOXPLOT.png" extension is added to the filepathroot. |
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. |
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
Author(s)
Julien PELE and Antoine GARNIER
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. PLoS ONE 6: e19094. doi:10.1371.
Examples
#File path for output file
wd <- tempdir()
#wd <-getwd()
file <- file.path(wd,"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)
#Creating MIP correlation object
#mip <- mip(align, gap_ratio = 0.2)
#Selecting correlation matrices
omes <-omes$Zscore
#mip <- mip$Zscore
#Creating a list of matrices and plotting the boxplots in a graph
#Two matrices
#corr_matrix_list <- list(omes,mip)
#name <- c("omes","mip")
#One matrix
corr_matrix_list <- list(omes)
name <- c("omes")
scores.boxplot(corr_matrix_list, name, file, 25, 275)