scores_entropy.plot: Scores versus entropy graph

In Bios2cor: From Biological Sequences and Simulations to Correlation Analysis

Scores versus entropy graph

Description

The aim of this graph is the visualization of the entropy values of the elements in top and bottom pairs.

Usage

  scores_entropy.plot(entropy, corr_matrix, 
    filepathroot=NULL, 
    elite=25, 
    high=275, 
    filter=NULL)

Arguments

entropy	An object created by the entropy (sequence alignment) or the dynamic_entropy (trajectory) function.
corr_matrix	A correlation/covariation matrix created by one of the correlation/covariation functions:
filepathroot	The root of the full path name of the graph that will be created. Default is NULL (a "ei_ej.pdf" file will be created). If not NULL, the filepathroot will have the "_ei_ej.pdf" extension.
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.
filter	A vector created by the delta_filter function, to limit the analysis to elements within a given entropy range. When filter is not NULL, only top elite and high pairs are visualized. Default is NULL.

Details

Using the result of a correlation/covariation method and an entropy structure, creates a graph comparing correlation/covariation scores with entropy values. Each pair of elements (i,j) is placed in the graph with (entropy[i] ; entropy[j]) as coordinates. In the absence of filter, the color code of each point is based on its correlation/covariation score (dark and light blue for top elite and high values, red and pink for bottom elite and high values). In the presence of an entropy based filter, only top elite and high scores are visualized in dark and light blue, respectively,

Value

A graph showing the entropy values of the elements in pairs with top and bottom entropy scores

Author(s)

Julien PELE, Antoine GARNIER and Marie CHABBERT

References

For an application of these graphs 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

  
##Example with MSA
  #File path for output file
  wd <- tempdir()
  #wd <-getwd() 
  file <- file.path(wd,"test_seq7") 
  
  #Importing MSA file  
  msf <- system.file("msa/toy_align.msf", package = "Bios2cor")
  align <- import.msf(msf)

  #Creating OMES correlation object and selecting a correlation matrix
  correlation <- omes(align, gap_ratio = 0.2)
  corr_matrix <- correlation$Zscore 

  #Creating entropy object
  entropy <- entropy(align)

  #Creating a delta filter based on entropy
  filter <- delta_filter(entropy, Smin = 0.4, Smax = 0.7)
 
  #Creating the entropy graph
  scores_entropy.plot(entropy, corr_matrix, filepathroot = file, filter=filter)

##Example with MD
#  #File path for output file
#  wd <- tempdir()
#  #wd <-getwd() 
#  file <- file.path(wd,"test_dyn7") 

#  #Reading pdb and dcd files
#  pdb <- system.file("rotamer/toy_coordinates.pdb", package= "Bios2cor")
#  trj <- system.file("rotamer/toy_dynamics.dcd", package= "Bios2cor")

#  #Creating dynamic_structure object for selected frames 
#  wanted_frames <- seq(from = 1, to = 40, by = 1)
#  dynamic_structure <- dynamic_structure(pdb, trj, wanted_frames)
#    
#  #Creating rotamers object using conversion_file
#  conversion_file <- system.file("rotamer/dynameomics_rotamers.csv", package= "Bios2cor")
#  rotamers <- angle2rotamer(dynamic_structure, conversion_file)
# 
#  #Creating the dynamic_entropy and filter objects 
#  entropy <- dynamic_entropy(rotamers)
#  filter <- delta_filter(entropy, Smin = 0.0, Smax = 0.1)

#  #Creating correlation object
#  #dyn_cor <- dynamic_circular(dynamic_structure)
#  dyn_cor <- dynamic_omes(dynamic_structure,rotamers) 

#  #selection correlation matrix
#  corr_matrix <- dyn_cor$Zscore_noauto
# 
#  #Creating the entropy graph
#  scores_entropy.plot(entropy, corr_matrix, filepathroot = file, filter=filter)

Bios2cor documentation built on July 8, 2022, 5:05 p.m.