#' Writes a text files with pymol scripts to list significant peptides
#'
#' Function write a script that can be used in pymol to color structure.
#' Number of colors and corresponding to them ranges can be defined by user.
#'
#' @param df output from functions output_tp
#' @param path location where the scripts will be saved
#' @param pv_cutoff p-value cutoff here set up to 0.01
#' @param replicates number of replicates in sample. Default set to 3.
#' @param ranges ranges for coloring scheme. Default set to c(-Inf, seq(-30, 30, by=10), Inf)
#' @param order.pep flag allowing to either order peptide acccording to the peptide length (default), or to position in the protein sequence.
#' @return pymol script with colors assigned per peptide
#' @examples
#' \donttest{
#' file_nm<-system.file("extdata", "All_results_table.csv", package = "HDXBoxeR")
#' a<- output_tp(file_nm)
#' pymol_script_significant_peptide(df=a, replicates=3, path=tempdir(), pv_cutoff=0.01,
#' ranges=c(-Inf,-40, -30,-20,-10, 0,10, 20,30,40, Inf), order.pep=TRUE )
#' pymol_script_significant_peptide(df=a, path=tempdir())
#' }
#' @export
pymol_script_significant_peptide<-function(df,path="", ranges=c(-Inf, seq(-30, 30, by=10), Inf),
pv_cutoff=0.01, replicates=3, order.pep=TRUE){
oldwd<-getwd()
on.exit(setwd(oldwd))
setwd(path)
#####from HDX get data and
for ( deut_time in(unique(df$Deut.Time))){
pv<-pv_timepoint(df[df$Deut.Time==deut_time,], replicates)
sd<-sd_timepoint(df[df$Deut.Time==deut_time,], replicates)
df1<-ave_timepoint(df[df$Deut.Time==deut_time,], replicates)
#preparation significance per residue & coverage
cl1<-significant_peptide_uptake(df1, pv, sd, pv_cutoff, replicates)
start_col<-which(colnames(df1)=='Start')
end_col<-which(colnames(df1)=='End')
###preparation of the coloring ranges. Difference of average.
fc.d<-data.frame((df1[,7]-df1[,8:dim(df1)[2]])/(df1[,7])*10*cl1)###vector which has significant average
##sumarized occurances of peptides
si.f=fc.d
#####preparation of average per residue data.frame, which will have
xli=ranges/10; num_ass<-c(-10001:(-10001-(length(xli)-2)))
for ( i in 1:(length(xli)-1)){
si.f[xli[i]< si.f & si.f < xli[i+1]] <- num_ass[i]}
si.f[ si.f==0]<- (-10000)
si_apc<-abs(si.f)-9999
##pallette definition
##color set up
cbr1<-color_ranges_Blue_Red_heat_map(ranges=xli, c("white"))
##assign name to colors in pallettes
col_nm<-c("NSig")
for ( i in 1:(length(ranges)-1)){
col_nm<-c(col_nm, paste("col_", ranges[i],"_", ranges[i+1], sep=""))}
rgb_col<-col2rgb(cbr1, alpha = FALSE) ## function to return rgb values for colors in pallette
##set_color 0%, [0 , 0 , 120], make command for pymol, to set colors
set_colors<-c()
for ( i in 1:length(col_nm)){
set_colors<-c(set_colors, paste("set_color ", col_nm[i],", [", rgb_col[1,i],
",", rgb_col[2,i], ",", rgb_col[3,i], "]", sep=""))}
###write outputs per each state in the
nm1<-str_sub(colnames(df1[8:dim(df1)[2]]), start=4, end=-9)
for (j in 1:dim(si_apc)[2]){
output_name<-paste("pymol_all_peptides_", nm1[j],"_", deut_time, ".txt", sep="")
res.txt<-c()
len.pep<-c()
for ( i in 1:length(col_nm)){
if (length(which(si_apc[,j]==i)) !=0 ) {
pep_nb<-which(si_apc[,j] == i)
for ( k in pep_nb){line<-c()
line<- paste(c("color ", col_nm[i],", resi ", df1[k,start_col], "-", df1[k,end_col] , sep=""))
len.pep<-c(len.pep, df1[k,end_col]-df1[k,start_col] )
res.txt<-c(res.txt,
paste(line, sep="' '", collapse=""))}
}}
if (order.pep==T){
nsig_pep<-res.txt[grep("color NSig",res.txt)]
sig_pep<-res.txt[grep("color col", res.txt)]
len_pep_sig<-len.pep[grep("color col", res.txt)]
sig_pep<-sig_pep[rev(order(len_pep_sig))]
res.txt<-c(nsig_pep, sig_pep)
message("peptides ordered according to peptide length")
} else if (order.pep==FALSE){
message("peptides ordered according to position in sequence")}
fileConn<-file(output_name)
writeLines(c("hide","show cartoon","color black", "bg white",
set_colors, res.txt ), fileConn)
close(fileConn)}}
leg_nm<-c("Not Sig")
for ( i in 1:(length(ranges)-1)){
leg_nm<-c(leg_nm, paste(ranges[i],":", ranges[i+1], "%", sep=""))}
pallette_ll(cbr1, leg_nm)
return()}
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