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20-04-2018.R
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20-04-2018.R
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ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dependencies = TRUE)
sapply(pkg, require, character.only = TRUE)
}
packages <- c("stringr","dplyr","entropy","ggplot2","ggseqlogo","gridExtra","cluster","seqinr","collapsibleTree","data.tree","DiagrammeR","stringdist","igraph","networkD3","plsgenomics","shinycssloaders","shiny","shinyFiles","shinyjs","shinyBS","DT","plotly","xtable","tictoc","data.table")
ipak(packages)
data <- read.csv(file.choose(), header = TRUE, sep = ";")
udata <- data[c(1,3)]
udata$AA.JUNCTION <- as.character(udata$AA.JUNCTION)
sim = list("F","W",c("A","I","L","V"),c("M","C"),"P","G","Y",c("T","S"),c("H","K","R"),c("E","D"),c("Q","N"))
# Naming the group of similarities
names(sim) = c("F","W","Al","Su","P","G","Y","Hy","Ba","Ac","Am")
altsim = list("F","W",c("A","I","L","V"),c("M","C"),"P","G","Y",c("T","S"),c("H","K","R"),c("E","D"),c("Q","N"))
# Naming the group of similarities
names(altsim) = c("f","w","a","s","p","g","y","h","b","c","m")
# A table with the letters
let = c("A","C","D","E","F","G","H","I","K","L","M","N","P","Q","R","S","T","V","W","Y") # The letters matrix
# An empty vector wich will store the level for every cluster
clep = vector('numeric')
br = 0 # Initial value of branch
cl = 0 # Initial value of new clusters
met = 0 # Initial value of level capacity counter
ep = 1 # Initial value of level
d = 0
nn = FALSE # Initial value for the condition sumper < endper%
endper = 90 # Set the percentage, which ends the programm
listxx = list() # Initialize a list for saving the permat of all branches
listyy = list()
dfsum = data.frame(sumper = numeric(0),sumper2 = numeric(0),branch = numeric(0), len = numeric(0))
dfsd = data.frame(ff = character(0),Average_Identity_Value = numeric(0),Identity_Standar_Deviation = numeric(0),Average_Similarity_Value = numeric(0), Similarity_Standar_Deviation = numeric(0), stringsAsFactors = FALSE)
ggdf = data.frame(branch = numeric(0), len = numeric(0))
last = 0
flagtic = TRUE
udata$AA.JUNCTION = as.character(udata$AA.JUNCTION)
udata$clusters = 0 # Initialiaze the column clusters with 0
udata$level.0 = 0 # Initialize the column of cl.0 with 0
udata$temp= 0 # Creating a temp column with 0
progend = FALSE
listax = list()
listax$temp = 1:length(udata$AA.JUNCTION)
names(listax)[length(listax)] = sprintf('cl.%d', 0)
listq = list("listax" = listax,"ggdf" = ggdf, "dfsum" = dfsum,"list" = listxx, "listn" = listyy,"udata" = udata,"permat"= NA, "persim" = NA, "br" = br, "cl" = cl, "met" = met, "ep" = ep , "clep" = clep, "nn" = nn, "sumper" = NA,"sumper2" = NA, "ela" = NA, "cel" = NA,"endper" = endper, "last" = last,"progend" = progend, "leaf" = FALSE)
algo = "Identity"
algo = "Similarity"
algocol = 0
listb = listq
levcut = 3
levcut =levcut +1
#list1 = listb
#list1 = result2
# Function Matrices compute the apsolute matrix, the matrix with percentages, the absolute similarity matrix and the percentage similarity matrix
Matrices <- function(list1){
tic()
udata = list1$udata
br = list1$br
permat = list1$permat
persim = list1$persim
cl = list1$cl
listxx = list1$list
listyy = list1$listn
sumper = list1$sumper # The total percentage of permat
sumper2 = list1$sumper2 # The total percentage of persim
endper = list1$endper
dfsum = list1$dfsum
nn = list1$nn
last = list1$last
progend = list1$progend
leaf = list1$leaf
mymat = matrix(0,nrow=length(let), ncol=str_length(udata$AA.JUNCTION[1]))
permat = matrix(0,nrow=length(let) + 1, ncol=str_length(udata$AA.JUNCTION[1]))
simmat = matrix(0,nrow = length(sim),ncol =str_length(udata$AA.JUNCTION[1]))
persim = matrix(0,nrow = length(sim)+1,ncol =str_length(udata$AA.JUNCTION[1]))
rownames(mymat) = let
rownames(permat) = c(let,"Entropy")
rownames(simmat) = c("F","W","Al","Su","P","G","Y","Hy","Ba","Ac","Am")
rownames(persim) = c("F","W","Al","Su","P","G","Y","Hy","Ba","Ac","Am","Entropy")
print(udata)
trimudata = strsplit(udata[udata$clusters == br,]$AA.JUNCTION,"")
align = data.frame(matrix(unlist(trimudata), nrow=length(trimudata), byrow=T))
for(i in 1:str_length(udata[udata$clusters == br,]$AA.JUNCTION[1])){
temptab = plyr::count(align[i],vars = colnames(align)[i])
names(temptab)[1] = "X1"
mymat[which(is.na(match(names(mymat[,i]),as.vector(unlist(temptab[1])))) == FALSE),i] = as.vector(unlist(temptab[2]))
permat[length(let) + 1,i] = entropy(xtabs(freq ~ X1, temptab),base=exp(1))
temptab1 = temptab
temptab1[1] = names(sim)[sapply(as.vector(unlist(temptab1[1])),function(x) which(str_detect(sim,x)))]
temptab1 = aggregate(temptab1[2], by=temptab1[1], FUN=sum)
simmat[sapply(as.vector(unlist(temptab1[1])),function(x) which(names(sim) == x)),i] = as.vector(unlist(temptab1[2]))
persim[length(sim)+1,i] = entropy(simmat[,i],base = exp(1))
}
permat[1:length(let),] = (mymat / length(udata[udata$clusters == br,]$AA.JUNCTION)) * 100
persim[1:length(sim),] = (simmat / length(udata[udata$clusters == br,]$AA.JUNCTION)) * 100
listxx$temp = permat
names(listxx)[length(listxx)] = sprintf('permat_br.%d', br) # Save the permat with this format
listyy$temp = persim
names(listyy)[length(listyy)] = sprintf('persim_br.%d', br)
################################ Finish ###########################
t1 =which(permat[-length(permat),] == 100,arr.ind = TRUE)
sumper = (length(as.numeric(t1[,2]))* 100) / str_length(udata$AA.JUNCTION[1])
t2 =which(persim[-length(persim),] == 100,arr.ind = TRUE)
sumper2 = (length(as.numeric(t2[,2]))* 100) / str_length(udata$AA.JUNCTION[1])
vv = length(udata[udata$clusters == br,]$AA.JUNCTION)
dfsum[nrow(dfsum) + 1,] = c(sumper,sumper2,br,vv)
if(algo == "Identity"){
if (sumper > endper){
nn = TRUE # When nn = TRUE the percentage of sumper < endper%
if (sumper2 > endper){
progend = TRUE
list1 = list("listax" = list1$listax,"ggdf" = list1$ggdf, "dfsum" = dfsum,"list" = listxx, "listn" = listyy, "udata" = udata,"permat"= permat, "persim" = persim, "br" = br, "cl" = cl, "met" = list1$met, "ep"= list1$ep, "clep" = list1$clep,"nn" = list1$nn, "sumper" = list1$sumper, "sumper2" = list1$sumper2, "ela" = list1$ela, "cel" = list1$cel,"endper" = list1$endper, "last" = last,"progend" = list1$progend, "leaf" = leaf)
en = toc(quiet = TRUE)
cat(paste0("Matrices","\t",length(udata[udata$clusters == br,]$AA.JUNCTION),"\t",str_length(udata$AA.JUNCTION[1]),"\t",en$toc - en$tic), file=logFile, append=TRUE, sep = "\n")
return(list1) # End of the programm, returning the final list
}
}
}else{
if (sumper2 > endper){
progend = TRUE
list1 = list("listax" = list1$listax,"ggdf" = list1$ggdf, "dfsum" = dfsum,"list" = listxx, "listn" = listyy, "udata" = udata,"permat"= permat, "persim" = persim, "br" = br, "cl" = cl, "met" = list1$met, "ep"= list1$ep, "clep" = list1$clep,"nn" = list1$nn, "sumper" = list1$sumper, "sumper2" = list1$sumper2, "ela" = list1$ela, "cel" = list1$cel,"endper" = list1$endper, "last" = last,"progend" = list1$progend, "leaf" = leaf)
en = toc(quiet = TRUE)
cat(paste0("Matrices","\t",length(udata[udata$clusters == br,]$AA.JUNCTION),"\t",str_length(udata$AA.JUNCTION[1]),"\t",en$toc - en$tic), file=logFile, append=TRUE, sep = "\n")
return(list1) # End of the programm, returning the final list
}
}
result1 = list("listax" = list1$listax,"ggdf" = list1$ggdf, "dfsum" = dfsum,"list" = listxx, "listn" = listyy, "udata" = udata,"permat"= permat, "persim" = persim, "br" = br, "cl" = cl, "met" = list1$met, "ep"= list1$ep, "clep" = list1$clep,"nn" = nn, "sumper" = sumper, "sumper2" = sumper2, "ela" = list1$ela, "cel" = list1$cel,"endper" = list1$endper, "last" = last, "progend" = progend, "leaf" = leaf)
en = toc(quiet = TRUE)
cat(paste0("Matrices","\t",length(udata[udata$clusters == br,]$AA.JUNCTION),"\t",str_length(udata$AA.JUNCTION[1]),"\t",en$toc - en$tic), file=logFile, append=TRUE, sep = "\n")
if ( leaf == TRUE){
nn = FALSE
result1 = list("listax" = list1$listax,"ggdf" = list1$ggdf, "dfsum" = dfsum,"list" = listxx, "listn" = listyy, "udata" = udata,"permat"= permat, "persim" = persim, "br" = br, "cl" = cl, "met" = list1$met, "ep"= list1$ep, "clep" = list1$clep,"nn" = nn, "sumper" = sumper, "sumper2" = sumper2, "ela" = list1$ela, "cel" = list1$cel,"endper" = list1$endper, "last" = last, "progend" = progend, "leaf" = leaf)
return(result1)
}else{
return(result1)
}
}
#list2 = result1
# Function Choice choose which matrix cell will be used for the division of the data
Choice <- function(list2){
tic()
udata = list2$udata
br = list2$br
cl = list2$cl
nn = list2$nn
cel = list2$cel
ela = list2$ela
met = list2$met
ep = list2$ep
clep = list2$clep
ggdf = list2$ggdf
progend = list2$progend
leaf = list2$leaf
listax = list2$listax
if(nn == TRUE || (algo == "Similarity") ){
permat = list2$persim # If sumper < endper% we want to check only the persim matrix
}else{
permat =list2$permat # Else permat and if it is necessary the persim matrix
persim = list2$persim
}
cel = which(permat[,(algocol + 1):ncol(permat)] == max(permat[,(algocol + 1):ncol(permat)]), arr.ind = TRUE)
ela = 1
poss = max(permat[,(algocol + 1):ncol(permat)])
if (max(permat[,(algocol + 1):ncol(permat)]) == 100){ # We exclude the 100 % from the max values
cel = which(permat[,(algocol + 1):ncol(permat)] == max(permat[,(algocol + 1):ncol(permat)][permat[,(algocol + 1):ncol(permat)]!=max(permat[,(algocol + 1):ncol(permat)])]), arr.ind = TRUE) # The desired cell
poss = max(permat[,(algocol + 1):ncol(permat)][permat[,(algocol + 1):ncol(permat)]!=max(permat[,(algocol + 1):ncol(permat)])])
}
if(poss == 0){
clmax = cl
brtemp = br
progend = TRUE
list2 = list("listax" = list2$listax,"ggdf" = list2$ggdf, "dfsum" = list2$dfsum,"list" = list2$list, "listn" = list2$listn, "udata" = list2$udata,"permat"= list2$permat, "persim" = list2$persim, "br" = brtemp, "cl" = list2$cl, "met" = list2$met, "ep"= list2$ep, "clep" = list2$clep,"nn" = list2$nn, "sumper" = list2$sumper, "sumper2" = list2$sumper2, "ela" = list2$ela, "cel" = list2$cel,"endper" = list2$endper, "last" = list2$last,"progend" = progend, "leaf" = leaf)
en = toc(quiet = TRUE)
cat(paste0("Choice","\t",length(udata[udata$clusters == br,]$AA.JUNCTION),"\t",str_length(udata$AA.JUNCTION[1]),"\t",en$toc - en$tic), file=logFile, append=TRUE, sep = "\n")
return(list2)
}
ela = 1
if(algo == "Identity"){
if ((length(cel)/2) > 1){
dddff = min(permat[,(algocol + 1):ncol(permat)][nrow(permat[,(algocol + 1):ncol(permat)]),cel[,2]])
dddff2 = which(permat[,(algocol + 1):ncol(permat)][nrow(permat[,(algocol + 1):ncol(permat)]),cel[,2]] == dddff)
ela = dddff2[1]
if (length(dddff2)>1 && nn == FALSE){ # If the vector has 2 or more numbers means that we have columns with the same entropy and nn = FALSE in order not to double check the persim
dddff3 = persim[,(algocol + 1):ncol(persim)][sapply(1:length(dddff2), function (x){str_which(sim,let[cel[x,1]])}),cel[,2]]
dddff3 = max(diag(dddff3))
dddff4 = which(diag( persim[,(algocol + 1):ncol(persim)][sapply(1:length(dddff2), function (x){str_which(sim,let[cel[x,1]])}),cel[,2]]) == dddff3)
ela = dddff4[1]
if(length(dddff4)> 1){
dddff5 = min(persim[,(algocol + 1):ncol(persim)][nrow(persim[,(algocol + 1):ncol(persim)]),cel[dddff4,2]])
dddff6 = which(persim[,(algocol + 1):ncol(persim)][nrow(persim[,(algocol + 1):ncol(persim)]),cel[dddff4,2]] == dddff5)
ela = dddff6[1]
}
}
}
}else{
if ((length(cel)/2) > 1){
dddff = min(permat[,(algocol + 1):ncol(permat)][nrow(permat[,(algocol + 1):ncol(permat)]),cel[,2]])
dddff2 = which(permat[,(algocol + 1):ncol(permat)][nrow(permat[,(algocol + 1):ncol(permat)]),cel[,2]] == dddff)
ela = dddff2[1]
}
}
nn = FALSE # Return nn in it's original value
##################################################### Divide ############################
if (met == 0){ # If we need a new level, then we create a new column with its name (level.ep)
udata$temp = NA
names(udata)[length(udata)] = sprintf('level.%d', ep)
}
if (algo == "Identity"){
x1 = str_which(str_detect(str_sub(udata[udata$clusters == br,]$AA.JUNCTION,(cel[ela,2]+algocol),(cel[ela,2]+algocol)), let[cel[ela,1]]), "TRUE")
mk1 = length(x1)
cltp1 = cl + 1
y1 = udata[udata$clusters == br,]$AA.JUNCTION
z1 = y1[x1]
x2 = str_which(str_detect(str_sub(udata[udata$clusters == br,]$AA.JUNCTION,(cel[ela,2]+algocol),(cel[ela,2]+algocol)), let[cel[ela,1]]), "FALSE")
mk2 = length(x2)
cltp2 = cl + 2
y2 = udata[udata$clusters == br,]$AA.JUNCTION
z2 = y2[x2]
lengdif = FALSE
if(mk1 < mk2){
lengdif = TRUE
templeng = z1
z1 = z2
z2 = templeng
temp2 = x1
x1 = x2
x2 = temp2
ggdf[nrow(ggdf) + 1,] = c(cltp1,mk2)
ggdf[nrow(ggdf) + 1,] = c(cltp2,mk1)
}else{
ggdf[nrow(ggdf) + 1,] = c(cltp1,mk1)
ggdf[nrow(ggdf) + 1,] = c(cltp2,mk2)
}
listax$temp = as.vector(unlist(listax[sprintf('cl.%d', br)]))[x1]
names(listax)[length(listax)] = sprintf('cl.%d', cl+1) # Save the permat with this format
listax$temp = as.vector(unlist(listax[sprintf('cl.%d', br)]))[x2]
names(listax)[length(listax)] = sprintf('cl.%d', cl+2) # Save the permat with this format
udata[as.vector(unlist(listax[sprintf('cl.%d', br)]))[x1],sprintf('level.%d', ep)] = cl+1
udata[as.vector(unlist(listax[sprintf('cl.%d', br)]))[x2],sprintf('level.%d', ep)] = cl+2
if(lengdif == TRUE){
udata[udata$clusters == br,]$clusters <- ifelse(str_detect(str_sub(udata[udata$clusters == br,]$AA.JUNCTION,(cel[ela,2]+algocol),(cel[ela,2]+algocol)), let[cel[ela,1]]), cl+2 ,cl+1)
}else{
udata[udata$clusters == br,]$clusters <- ifelse(str_detect(str_sub(udata[udata$clusters == br,]$AA.JUNCTION,(cel[ela,2]+algocol),(cel[ela,2]+algocol)), let[cel[ela,1]]), cl+1 ,cl+2)
}
}else{
strings.to.find = unlist(sim[cel[ela,1]])
x1 = str_which(str_detect(str_sub(udata[udata$clusters == br,]$AA.JUNCTION,(cel[ela,2]+algocol),(cel[ela,2]+algocol)), str_c(strings.to.find, collapse="|")), "TRUE")
mk1 = length(x1)
cltp1 = cl + 1
y1 = udata[udata$clusters == br,]$AA.JUNCTION
z1 = y1[x1]
x2 = str_which(str_detect(str_sub(udata[udata$clusters == br,]$AA.JUNCTION,(cel[ela,2]+algocol),(cel[ela,2]+algocol)), str_c(strings.to.find, collapse="|")), "FALSE")
mk2 = length(x2)
cltp2 = cl + 2
y2 = udata[udata$clusters == br,]$AA.JUNCTION
z2 = y2[x2]
lengdif = FALSE
if(mk1 < mk2){
lengdif = TRUE
templeng = z1
z1 = z2
z2 = templeng
temp2 = x1
x1 = x2
x2 = temp2
ggdf[nrow(ggdf) + 1,] = c(cltp1,mk2)
ggdf[nrow(ggdf) + 1,] = c(cltp2,mk1)
}else{
ggdf[nrow(ggdf) + 1,] = c(cltp1,mk1)
ggdf[nrow(ggdf) + 1,] = c(cltp2,mk2)
}
listax$temp = as.vector(unlist(listax[sprintf('cl.%d', br)]))[x1]
names(listax)[length(listax)] = sprintf('cl.%d', cl+1) # Save the permat with this format
listax$temp = as.vector(unlist(listax[sprintf('cl.%d', br)]))[x2]
names(listax)[length(listax)] = sprintf('cl.%d', cl+2) # Save the permat with this format
udata[as.vector(unlist(listax[sprintf('cl.%d', br)]))[x1],sprintf('level.%d', ep)] = cl+1
udata[as.vector(unlist(listax[sprintf('cl.%d', br)]))[x2],sprintf('level.%d', ep)] = cl+2
if(lengdif == TRUE){
udata[udata$clusters == br,]$clusters <- ifelse(str_detect(str_sub(udata[udata$clusters == br,]$AA.JUNCTION,(cel[ela,2]+algocol),(cel[ela,2]+algocol)), str_c(strings.to.find, collapse="|")), cl+2 ,cl+1)
}else{
udata[udata$clusters == br,]$clusters <- ifelse(str_detect(str_sub(udata[udata$clusters == br,]$AA.JUNCTION,(cel[ela,2]+algocol),(cel[ela,2]+algocol)), str_c(strings.to.find, collapse="|")), cl+1 ,cl+2)
}
}
############################################ Control #############################################
clep[cl+1] = ep # Level of the cluster cl+1
clep[cl+2] = ep # Level of the cluster cl+2
br = br + 1 # Increase the branch by 1
cl = cl + 2 # Increase the cluster by 2
met = met + 2 # Increase the counter by 2
list2 = list("listax" = listax,"ggdf" = ggdf, "dfsum" = list2$dfsum,"list" = list2$list, "listn" = list2$listn, "udata" = udata,"permat"= list2$permat, "persim" = list2$persim, "br" = br, "cl" = cl, "met" = met, "ep"= ep, "clep" = clep,"nn" = nn, "sumper" = list2$sumper, "sumper2" = list2$sumper2, "ela" = ela, "cel" = cel,"endper" = list2$endper, "last" = list2$last)
if( ((clep[br-1] < clep[br]) && (sum(clep == clep[br]) != (2^clep[br]))) == TRUE && (length(which(udata$clusters == br)) > 2) ){ # If the next branch is in the next level
met = geomSeq(1,2,1,1000)[ep+1]
}
while (length(which(udata$clusters == br)) <= 2){ # While the number of sequences in the branch is less than 2, go to the next branch and change counter
if(is.na(str_length(udata[udata$clusters == br,]$AA.JUNCTION[1]))){
progend = TRUE
list2 = list("listax" = listax, "ggdf" = list2$ggdf, "dfsum" = list2$dfsum,"list" = list2$list, "listn" = list2$listn, "udata" = udata,"permat"= list2$permat, "persim" = list2$persim, "br" = br, "cl" = cl, "met" = met, "ep"= ep, "clep" = clep,"nn" = nn, "sumper" = list2$sumper, "sumper2" = list2$sumper2, "ela" = ela, "cel" = cel,"endper" = list2$endper, "last" = list2$last, "progend" = progend, "leaf" = leaf)
en = toc(quiet = TRUE)
cat(paste0("Choice","\t",length(udata[udata$clusters == br,]$AA.JUNCTION),"\t",str_length(udata$AA.JUNCTION[1]),"\t",en$toc - en$tic), file=logFile, append=TRUE, sep = "\n")
return(list2)
}
leaf = TRUE
list2 = list("listax" = listax,"ggdf" = list2$ggdf, "dfsum" = list2$dfsum,"list" = list2$list, "listn" = list2$listn, "udata" = udata,"permat"= list2$permat, "persim" = list2$persim, "br" = br, "cl" = cl, "met" = met, "ep"= ep, "clep" = clep,"nn" = nn, "sumper" = list2$sumper, "sumper2" = list2$sumper2, "ela" = ela, "cel" = cel,"endper" = list2$endper, "last" = list2$last, "progend" = progend, "leaf" = leaf)
list2 = Matrices(list2)
if(is.na(((clep[br] < clep[br+1]) && (sum(clep == clep[br]) != (2^clep[br]))))){
progend = TRUE
list2 = list("listax" = list2$listax,"ggdf" = list2$ggdf, "dfsum" = list2$dfsum,"list" = list2$list, "listn" = list2$listn, "udata" = udata,"permat"= list2$permat, "persim" = list2$persim, "br" = br, "cl" = cl, "met" = met, "ep"= ep, "clep" = clep,"nn" = nn, "sumper" = list2$sumper, "sumper2" = list2$sumper2, "ela" = ela, "cel" = cel,"endper" = list2$endper, "last" = list2$last, "progend" = progend, "leaf" = leaf)
en = toc(quiet = TRUE)
cat(paste0("Choice","\t",length(udata[udata$clusters == br,]$AA.JUNCTION),"\t",str_length(udata$AA.JUNCTION[1]),"\t",en$toc - en$tic), file=logFile, append=TRUE, sep = "\n")
return(list2)
}
if( ((clep[br] < clep[br+1]) && (sum(clep == clep[br]) != (2^clep[br]))) == TRUE ){ # If the next branch is in the next level
met = 0
ep = ep + 1
br = br + 1
}else{
br = br + 1
met = met +2
}
list2 = list("listax" = list2$listax,"ggdf" = list2$ggdf, "dfsum" = list2$dfsum,"list" = list2$list, "listn" = list2$listn, "udata" = list2$udata,"permat"= list2$permat, "persim" = list2$persim, "br" = br, "cl" = cl, "met" = met, "ep"= ep, "clep" = clep,"nn" = list2$nn, "sumper" = list2$sumper, "sumper2" = list2$sumper2, "ela" = list2$ela, "cel" = list2$cel,"endper" = list2$endper, "last" = list2$last, "progend" = progend, "leaf" = leaf)
}
if( met == geomSeq(1,2,1,1000)[ep+1]){ # When the counter reaches the end value (geometric sequence) we increase the level counter
met = 0
ep = ep + 1
}
leaf = FALSE
result2 = list("listax" = list2$listax,"ggdf" = list2$ggdf, "dfsum" = list2$dfsum,"list" = list2$list, "listn" = list2$listn, "udata" = udata,"permat"= list2$permat, "persim" = list2$persim, "br" = br, "cl" = cl, "met" = met, "ep"= ep, "clep" = clep,"nn" = nn, "sumper" = list2$sumper, "sumper2" = list2$sumper2, "ela" = ela, "cel" = cel,"endper" = list2$endper, "last" = list2$last, "progend" = progend, "leaf" = leaf)
en = toc(quiet = TRUE)
cat(paste0("Choice","\t",length(udata[udata$clusters == br,]$AA.JUNCTION),"\t",str_length(udata$AA.JUNCTION[1]),"\t",en$toc - en$tic), file=logFile, append=TRUE, sep = "\n")
return(result2)
}
# A function that generates a geometric sequence
geomSeq <- function(start,ratio,begin,end){
begin=begin-1
end=end-1
start*ratio**(begin:end)
}
#logfile
logFile = paste0(getwd(),"/log_file ",trunc(as.numeric(Sys.time())),".txt")
cat(paste0("Function","\t","Num of input rows","\t","Num of input columns","\t","Duration"), file=logFile, append=FALSE, sep = "\n")
tic()
while (progend == FALSE){
lista = Matrices(listb)
progend = lista$progend
if(progend == TRUE){
break
}
listb = Choice(lista)
progend = listb$progend
}
en = toc(quiet = TRUE)
cat(paste0("lastlist","\t",length(udata[udata$clusters == br,]$AA.JUNCTION),"\t",str_length(udata$AA.JUNCTION[1]),"\t",en$toc - en$tic), file=logFile, append=TRUE, sep = "\n")
lastlist = listb
lastlist2 = listb
lastlist3 = listb
# The final name of udata data frame
df = lastlist$udata
# A list with the permat matrix for every branch
perlist = lastlist$list
persimlist = lastlist$listn
# Create a dataframe with all clusters and their identity and similarity percentage
ff = lastlist$dfsum
ff$level[1]= 0
ff$level[2:(length(lastlist$clep[ff$branch]) +1 )] = lastlist$clep[ff$branch] #without leaves
Clus = as.data.frame(matrix(100, ncol = 3, nrow = max(df$clusters)+1))
names(Clus) = c("ClusterId","Identity","Similarity")
Clus$ClusterId = 0:max(df$clusters)
Clus$seqnum[1] = nrow(df)
Clus$seqnum[2:(max(df$clusters)+1)] = lastlist$ggdf$len
Clus$level = c(0,lastlist$clep)
for(i in 1:length(ff$branch) ){
ll = which(Clus$ClusterId == ff$branch[i])
Clus$Identity[ll] = ff$sumper[i]
Clus$Similarity[ll] = ff$sumper2[i]
}
threshold1 = 5
threshold2 = 5
if(enthr == TRUE){
lev = max(na.omit(lastlist$clep))
df = df[ do.call( order , df[ , match( colnames(df[str_which(names(df), "level.")]) , names(df) ) ] ) , ]
df_args <- c(df[str_which(names(df), "level.")], sep="/")
if(lev == max(na.omit(lastlist$clep))){
df$pathString<- do.call(paste, df_args)
kk = df$pathString
for(i in 1:length(kk)){
temp = str_locate(kk[i],"/NA")
if(is.na(temp[1]) == FALSE){
temp2 = str_sub(kk[i], 1, temp[1]-1);
kk[i] = temp2
}
}
}else{
df$pathString<- do.call(paste, df_args)
kk = df$pathString
gg =as.data.frame(str_locate_all(kk,"/"))
tem = 1
for (i in 1:length(kk)){
kk[i] = str_sub(kk[i],1,gg[,tem][lev+1]-1)
tem = tem + 2
}
for(i in 1:length(kk)){
temp = str_locate(kk[i],"/NA")
if(is.na(temp[1]) == FALSE){
temp2 = str_sub(kk[i], 1, temp[1]-1);
kk[i] = temp2
}
}
}
df$pathString = kk
x <- ToDataFrameTree(df, "pathstring")
xN <- as.Node(x)
matches <- regmatches(unique(x$pathString), gregexpr("[[:digit:]]+", unique(x$pathString)))
arxpath = unique(x$pathString)
for(i in 1:length(matches)){
ggg = as.numeric(unlist(matches[i])) +1
if(levcut <= (length(ggg)-1)){
for(j in levcut:(length(ggg)-1)){
if(algo == "Identity"){
ddd1 = ((Clus$seqnum[ggg[j]] - Clus$seqnum[ggg[j+1]]) / Clus$seqnum[ggg[j]]) * 100
ddd2 = (Clus$Identity[ggg[j+1]] - Clus$Identity[ggg[j]]) * 0.5 + (Clus$Similarity[ggg[j+1]] - Clus$Similarity[ggg[j]]) * 0.5
if(ddd1 < threshold1 || ddd2 < threshold2){
deik = which(x$pathString == arxpath[i])
tem = str_locate(arxpath[i],as.character(ggg[j]-1))
fftemp = str_sub(x$pathString[deik[1]],1,tem[2])
ff2 = str_sub(x$pathString[deik[1]],tem[1],tem[2])
ff3 = as.numeric(ff2)
x$clusters[deik] = ff3
ff4 = lastlist$clep[ff3]
ff5 = str_which(names(x),sprintf("level.%d",ff4))
ff6 = str_which(names(x), "level.")
if(is.na(ff5[1]) == FALSE){
x[deik,(ff5[1]+1):max(ff6)] = NA
}
x$pathString[deik] = fftemp
break()
}
}else if(algo == "Similarity"){
ddd1 = ((Clus$seqnum[ggg[j]] - Clus$seqnum[ggg[j+1]]) / Clus$seqnum[ggg[j]]) * 100
ddd2 = Clus$Similarity[ggg[j+1]] - Clus$Similarity[ggg[j]]
print(paste0("ddd1=",ddd1," ddd2=",ddd2))
print(paste0("i=",i," j=",j) )
if(ddd1 < threshold1 || ddd2 < threshold2){
deik = which(x$pathString == arxpath[i])
tem = str_locate(arxpath[i],as.character(ggg[j]-1))
fftemp = str_sub(x$pathString[deik[1]],1,tem[2])
ff2 = str_sub(x$pathString[deik[1]],tem[1],tem[2])
ff3 = as.numeric(ff2)
x$clusters[deik] = ff3
ff4 = lastlist$clep[ff3]
ff5 = str_which(names(x),sprintf("level.%d",ff4))
ff6 = str_which(names(x), "level.")
if(is.na(ff5[1]) == FALSE){
x[deik,(ff5[1]+1):max(ff6)] = NA
}
x$pathString[deik] = fftemp
break()
}
}
}
}
}
xN <- as.Node(x)
bo = which(lastlist$clep == lev)
levtel = lastlist$clep
j = 1
jfjf = vector()
for(i in 1:max(bo)){
temp1 = as.numeric(names(FindNode(xN,(sprintf("%d",i)))$children))
if(length(temp1) == 1){
if(temp1[1] %% 2 == 1){
ff7 = temp1[1] + 1
}else{
ff7 = temp1[1] - 1
}
ff8 = lastlist$clep[ff7]
ff9 = str_which(names(x),sprintf("level.%d",ff8))
deik2 = which(df[ff9] == ff7)
x[deik2,ff9] = df[deik2,ff9]
tem = str_locate(df$pathString[deik2[1]],as.character(ff7))
fftemp = str_sub(df$pathString[deik2[1]],1,tem[2])
x$pathString[deik2] = fftemp
jfjf[j] = ff7
j = j+1
}
if(is.null(FindNode(xN,(sprintf("%d",i)))$isLeaf) && is.element(i, jfjf) == FALSE){
levtel[i] = NA
Clus[i+1,] = NA
ff[i+1,] = NA
}
}
xN <- as.Node(x)
}
df = x
lastlist$clep = levtel
lev = 4
Den <- function(lev){
if(flagtic == TRUE) tic(sprintf("Den -- Level: %d ", lev))
df = df[ do.call( order , df[ , match( colnames(df[str_which(names(df), "level.")]) , names(df) ) ] ) , ]
df_args <- c(df[str_which(names(df), "level.")], sep="/")
if(lev == max(na.omit(lastlist$clep))){
df$pathString<- do.call(paste, df_args)
kk = df$pathString
for(i in 1:length(kk)){
temp = str_locate(kk[i],"/NA")
if(is.na(temp[1]) == FALSE){
temp2 = str_sub(kk[i], 1, temp[1]-1);
kk[i] = temp2
}
}
}else{
df$pathString<- do.call(paste, df_args)
kk = df$pathString
gg =as.data.frame(str_locate_all(kk,"/"))
tem = 1
for (i in 1:length(kk)){
kk[i] = str_sub(kk[i],1,gg[,tem][lev+1]-1)
tem = tem + 2
}
for(i in 1:length(kk)){
temp = str_locate(kk[i],"/NA")
if(is.na(temp[1]) == FALSE){
temp2 = str_sub(kk[i], 1, temp[1]-1);
kk[i] = temp2
}
}
}
df$pathString = kk
x <- ToDataFrameTree(df, "pathstring")
#par = str_count(df$pathString,"/")
#str_locate(df$pathString,"/")
#par = rev(gregexpr("\\/", df$pathString))
if(enthr == TRUE){
matches <- regmatches(unique(x$pathString), gregexpr("[[:digit:]]+", unique(x$pathString)))
arxpath = unique(x$pathString)
for(i in 1:length(matches)){
# i = 2
ggg = as.numeric(unlist(matches[i])) +1
if(levcut <= (length(ggg)-1)){
for(j in levcut:(length(ggg)-1)){
if(algo == "Identity"){
ddd1 = ((Clus$seqnum[ggg[j]] - Clus$seqnum[ggg[j+1]]) / Clus$seqnum[ggg[j]]) * 100
ddd2 = (Clus$Identity[ggg[j+1]] - Clus$Identity[ggg[j]]) * 0.5 + (Clus$Similarity[ggg[j+1]] - Clus$Similarity[ggg[j]]) * 0.5
if(ddd1 < threshold1 || ddd2 < threshold2){
deik = which(x$pathString == arxpath[i])
tem = str_locate(arxpath[i],as.character(ggg[j]-1))
fftemp = str_sub(x$pathString[deik[1]],1,tem[2])
ff2 = str_sub(x$pathString[deik[1]],tem[1],tem[2])
ff3 = as.numeric(ff2)
x$clusters[deik] = ff3
ff4 = lastlist$clep[ff3]
ff5 = str_which(names(x),sprintf("level.%d",ff4))
ff6 = str_which(names(x), "level.")
if(is.na(ff5[1]) == FALSE){
x[deik,(ff5[1]+1):max(ff6)] = NA
}
x$pathString[deik] = fftemp
break()
}
}else if(algo == "Similarity"){
ddd1 = ((Clus$seqnum[ggg[j]] - Clus$seqnum[ggg[j+1]]) / Clus$seqnum[ggg[j]]) * 100
ddd2 = Clus$Similarity[ggg[j+1]] - Clus$Similarity[ggg[j]]
print(paste0("ddd1=",ddd1," ddd2=",ddd2))
print(paste0("i=",i," j=",j) )
if(ddd1 < threshold1 || ddd2 < threshold2){
deik = which(x$pathString == arxpath[i])
tem = str_locate(arxpath[i],as.character(ggg[j]-1))
fftemp = str_sub(x$pathString[deik[1]],1,tem[2])
ff2 = str_sub(x$pathString[deik[1]],tem[1],tem[2])
ff3 = as.numeric(ff2)
x$clusters[deik] = ff3
ff4 = lastlist$clep[ff3]
ff5 = str_which(names(x),sprintf("level.%d",ff4))
ff6 = str_which(names(x), "level.")
if(is.na(ff5[1]) == FALSE){
x[deik,(ff5[1]+1):max(ff6)] = NA
}
x$pathString[deik] = fftemp
break()
}
}
}
}
}
xN <- as.Node(x)
bo = which(lastlist$clep == lev)
levtel = lastlist$clep
j = 1
jfjf = vector()
for(i in 1:max(bo)){
temp1 = as.numeric(names(FindNode(xN,(sprintf("%d",i)))$children))
if(length(temp1) == 1){
if(temp1[1] %% 2 == 1){
ff7 = temp1[1] + 1
}else{
ff7 = temp1[1] - 1
}
ff8 = lastlist$clep[ff7]
ff9 = str_which(names(x),sprintf("level.%d",ff8))
deik2 = which(df[ff9] == ff7)
x[deik2,ff9] = df[deik2,ff9]
tem = str_locate(df$pathString[deik2[1]],as.character(ff7))
fftemp = str_sub(df$pathString[deik2[1]],1,tem[2])
x$pathString[deik2] = fftemp
jfjf[j] = ff7
j = j+1
}
if(is.null(FindNode(xN,(sprintf("%d",i)))$isLeaf) && is.element(i, jfjf) == FALSE){
levtel[i] = NA
Clus[i+1,] = NA
ff[i+1,] = NA
}
}
}
xN <- as.Node(x)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
plot(xN)
}
# Create custom colour scheme
cs1 = make_col_scheme(chars=c("F","W","A","I","L","V","M","C","P","G","Y","T","S","H","K","R","E","D","Q","N"),
cols=c("#1E90FF", "#BA55D3", "#0000FF", "#0000FF", "#0000FF", "#0000FF", "#C6E2FF", "#C6E2FF", "#FFD700", "#00EE00", "#C1FFC1", "#54FF9F", "#54FF9F", "#FF0000", "#FF0000", "#FF0000", "#FFD700", "#FFD700", "#ED9121", "#ED9121"))
#df= x
#lev = 2
#lastlist$clep = levtel
# A function to plot with logo level lev
LogoLev <- function(lev){
if(flagtic == TRUE) tic(sprintf("LogoLev -- Level: %d ", lev))
if( is.element(lev,lastlist$clep) == FALSE){
print("Den yparxei")
}else{
t1 = which(lastlist$clep == lev)
# epipleon
xm = as.numeric(as.data.frame(xN$leaves))
orio = min(which(lastlist$clep == lev))
xm2 = sort(xm[xm < orio])
t1 = sort(append(xm2,t1,after = length(xm2)))
listff = list()
if(length(t1) %% 3 == 0){
nc = length(t1)%/%3
}else{
nc = length(t1)%/%3 + 1
}
for(i in 1:length(t1)){
if(i<= length(xm2)){
listff$temp = na.omit(df[df[names(df) == sprintf('level.%d', lastlist$clep[t1[i]])] == t1[i],]$AA.JUNCTION)
names(listff)[length(listff)] = sprintf('Cluster.%d - num:%d - leaf', t1[i],Clus$seqnum[t1[i]+1])
}else{
x1 = as.data.frame(na.omit(df[df[which(names(df) == sprintf("level.%d", lev))] == t1[i], ]$AA.JUNCTION))
if (nrow(x1) >0){
names(x1)[1]= "AA.JUNCTION"
x1 = as.character(x1$AA.JUNCTION)
listff$temp = x1
names(listff)[length(listff)] = sprintf('Cluster.%d - num:%d', t1[i],Clus$seqnum[t1[i]+1])
}
}
}
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
ggseqlogo(listff, ncol=nc, method = "prob",col_scheme=cs1)
}
}
#df = x
# Creating a plot for cluster 5 for example
LogoCl <- function(cl){
if(flagtic == TRUE) tic(sprintf("LogoCl -- Cluster: %d ", cl))
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
if(is.na(lastlist$clep[cl])){
print("Den yparxei")
}else{
ggseqlogo(na.omit(df[df[names(df) == sprintf('level.%d', lastlist$clep[cl])] == cl,]$AA.JUNCTION), method = "prob", col_scheme=cs1)
}
}
# A function to plot with barplot level lev
BarLev <- function(lev){
if(flagtic == TRUE) tic(sprintf("BarLev -- Level: %d ", lev))
if(cho == "Identity"){
if( is.element(lev,lastlist$clep) == FALSE){
print("Den yparxei")
}else{
t2 = which(lastlist$clep == lev)
# epipleon
xm = as.numeric(as.data.frame(xN$leaves))
orio = min(which(lastlist$clep == lev))
xm2 = sort(xm[xm < orio])
t2 = sort(append(xm2,t2,after = length(xm2)))
if(length(t2) %% 3 == 0){
par(mfrow = c(length(t2)%/%3,3))
}else{
par(mfrow = c(length(t2)%/%3 + 1,3))
}
for(i in 1:length(t2)){
ar = str_which(names(perlist),as.character(t2[i]))[1]
par(xpd=TRUE)
output <- matrix(unlist(perlist[ar]), ncol = str_length(lastlist$udata$AA.JUNCTION[1]), byrow = FALSE)
if(i<= length(xm2)){
barplot(output[-nrow(output),], col=heat.colors(length(output[,1])-1), width=2, main = sprintf('Cluster.%d - seqnum:%d - leaf', t2[i],Clus$seqnum[t2[i]+1]))
}else{
barplot(output[-nrow(output),], col=heat.colors(length(output[,1])-1), width=2, main = sprintf('Cluster.%d - seqnum:%d', t2[i],Clus$seqnum[t2[i]+1]))
}
legend("topright",inset=c(-0.03,0), fill=heat.colors(length(output[,1])-1), legend=let,cex = 0.6)
}
}
}else{
if( is.element(lev,lastlist$clep) == FALSE){
print("Den yparxei")
}else{
t2 = which(lastlist$clep == lev)
# epipleon
xm = as.numeric(as.data.frame(xN$leaves))
orio = min(which(lastlist$clep == lev))
xm2 = sort(xm[xm < orio])
t2 = sort(append(xm2,t2,after = length(xm2)))
if(length(t2) %% 3 == 0){
par(mfrow = c(length(t2)%/%3,3))
}else{
par(mfrow = c(length(t2)%/%3 + 1,3))
}
for(i in 1:length(t2)){
ar = str_which(names(persimlist),as.character(t2[i]))[1]
par(xpd=TRUE)
output <- matrix(unlist(persimlist[ar]), ncol = str_length(lastlist$udata$AA.JUNCTION[1]), byrow = FALSE)
if(i<= length(xm2)){
barplot(output[-nrow(output),], col=heat.colors(length(output[,1])-1), width=2, main = sprintf('Cluster.%d - seqnum:%d - leaf', t2[i],Clus$seqnum[t2[i]+1]))
}else{
barplot(output[-nrow(output),], col=heat.colors(length(output[,1])-1), width=2, main = sprintf('Cluster.%d - seqnum:%d', t2[i],Clus$seqnum[t2[i]+1]))
}
legend("topright",inset=c(-0.03,0), fill=heat.colors(length(output[,1])-1), legend=names(sim),cex = 0.6)
}
}
}
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
}
cho = "Identity"
# Create the grid (we need to miximize the pop-up window in order to have a perfect fit)
#get( getOption( "device" ) )()
# An alternative plot for cluster 3 (We must determine the cluster's permat)
BarCl <- function(cl){
if(flagtic == TRUE) tic(sprintf("BarCl -- Cluster :%d ", cl))
if(cho == "Identity"){
if(is.na(lastlist$clep[cl])){
print("Den yparxei")
}else{
par(mar=c(3,3,4,4),xpd=TRUE)
output <- matrix(unlist(perlist[cl]), ncol = str_length(lastlist$udata$AA.JUNCTION[1]), byrow = FALSE)
barplot(output[-nrow(output),], col=heat.colors(length(output[,1])-1), width=2, main = sprintf('Cluster.%d',cl))
legend("topright",inset=c(-0.03,0), fill=heat.colors(length(output[,1])-1), legend=let,cex = 0.6)
}
}else{
if(is.na(lastlist$clep[cl])){
print("Den yparxei")
}else{
par(mar=c(3,3,4,4),xpd=TRUE)
output <- matrix(unlist(persimlist[cl]), ncol = str_length(lastlist$udata$AA.JUNCTION[1]), byrow = FALSE)
barplot(output[-nrow(output),], col=heat.colors(length(output[,1])-1), width=2, main = sprintf('Cluster.%d',cl))
legend("topright",inset=c(-0.03,0), fill=heat.colors(length(output[,1])-1), legend=names(sim),cex = 0.6)
}
}
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
}
# Sequences and Id's for specific level
AminoLev <- function(level){
if(flagtic == TRUE) tic(sprintf("AminoLev -- Level: %d ", level))
if( is.element(lev,lastlist$clep) == FALSE){
print("Den yparxei")
}else{
sum(Clus[Clus$level == level,]$seqnum) # akoloy8ies sto level
x4 = data_frame("Sequence.ID" = character(0),"AA.JUNCTION" = character(0))
gg = na.omit(unique(df[,which(names(df) == sprintf("level.%d", level))]))
for(i in 1:length(gg)){
clust = gg[i]
x3 = data.frame(na.omit(df[df[which(names(df) == sprintf("level.%d", lastlist$clep[clust]))] == clust, ]$Sequence.ID), na.omit(df[df[which(names(df) == sprintf("level.%d", lastlist$clep[clust]))] == clust, ]$AA.JUNCTION))
names(x3) = c("Sequence.ID","AA.JUNCTION")
se = x3$Sequence.ID
aa = as.character(x3$AA.JUNCTION)
aa = as.list(aa)
names(aa) = se
x4 = rbind(x4,x3)
}
se2 = x4$Sequence.ID
aa2 = as.character(x4$AA.JUNCTION)
aa2 = as.list(aa2)
names(aa2) = se2
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
x4 # Print in console
}
}
#write.fasta(aa2, names= FALSE ,file = sprintf("Level.%d", level)) # Create a fasta file
# Sequences and Id's for specific cluster
AminoCl <- function(clust){
#if(flagtic == TRUE) tic("AminoCl")
#if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
if(clust == 0){
x3 = data.frame("Sequence.ID" = df$Sequence.ID, "AA.JUNCTION" = df$AA.JUNCTION)
x3
}else{
if(is.na(lastlist$clep[clust]) == FALSE){
# print("Den yparxei")
#}else{
x3 = data.frame(na.omit(df[df[which(names(df) == sprintf("level.%d", lastlist$clep[clust]))] == clust, ]$Sequence.ID), na.omit(df[df[which(names(df) == sprintf("level.%d", lastlist$clep[clust]))] == clust, ]$AA.JUNCTION))
names(x3) = c("Sequence.ID","AA.JUNCTION")
se = x3$Sequence.ID
aa = as.character(x3$AA.JUNCTION)
aa = as.list(aa)
names(aa) = se
x3 # Print in console
}
}
}
# A function which visualize the sequences of a data frame using common letters (i.e. "A _ _ _ _ K R _ _ _ Q _ Y Y Y _ _ _ T _")
Opt <- function(df){
if(is.list(df)){
#print("Den yparxei")
#if(flagtic == TRUE) tic("Opt")
xar <- matrix(0,nrow=1, ncol=str_length(df$AA.JUNCTION[1]))
for (i in 1:str_length(df$AA.JUNCTION[1])) {
f <- table(str_sub(df$AA.JUNCTION,i,i))
xar[i] = "_"
if (f[1] == nrow(df)){
xar[i] = names(f[1])
}
}
# if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
paste(xar,collapse = ' ')
}
}
#Opt(AminoCl(1))
fgh = sprintf('cluster.%d: %s',t1[1],(Opt(AminoCl(t1[1]))))
for(i in 2:length(t1)){
#fgh = Opt(AminoCl(t1[i]))
fgh = paste(fgh,sprintf('cluster.%d: %s',t1[i],(Opt(AminoCl(t1[i])))),sep = "\n")
}
cncn = cat(fgh,sep = "\n")
OptNew <- function(df){
if(is.list(df)){
xar <- matrix(0,nrow=1, ncol=str_length(df$AA.JUNCTION[1]))
for (i in 1:str_length(df$AA.JUNCTION[1])) {
f <- table(str_sub(df$AA.JUNCTION,i,i))
if (length(which(f == max(f))) > 1){
tempopt = "-"
tempopt = paste(tempopt,names(which(f == max(f)))[1],sep = "")
#tempopt = names(which(f == max(f)))[1]
for(j in 2:(length(which(f == max(f))))){
tempopt = paste(tempopt, names(which(f == max(f)))[j],sep = "|")
}
tempopt = paste(tempopt,"-",sep = "")
xar[i] = tempopt
}else{
xar[i] = names(which(f == max(f)))
}
}
paste(xar,collapse = ' ')
}
}
#OptNew(AminoCl(1))
# A function which visualize the sequences of a data frame using similarity groups (i.e. "A _ _ _ _ K Am _ Ba _ Ac _ Y Y Y _ _ _ T _")
Opt2 <- function(df,alt){
if(is.list(df)){
#if(flagtic == TRUE) tic("Opt2")
xar <- matrix(0,nrow=1, ncol=str_length(df$AA.JUNCTION[1]))
for (i in 1:str_length(df$AA.JUNCTION[1])) {
f <- table(str_sub(df$AA.JUNCTION,i,i))
xar[i] = "_"
if (f[1] == nrow(df)){
xar[i] = names(f[1])
}else{
y = TRUE
d = str_which(sim,names(f[1]))
for(j in 2:length(f)){
y = y && str_detect(sim[d],names(f[j]))
}
if( y == TRUE){
if(alt == TRUE){
xar[i] = names(altsim[d])
}else{
xar[i] = names(sim[d])
}
}
}
}
#if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
paste(xar,collapse = ' ')
}
}
#Opt2(AminoCl(5),TRUE)
Opt2New <- function(df){
if(is.list(df)){
xar <- matrix(0,nrow=1, ncol=str_length(df$AA.JUNCTION[1]))
for (i in 1:str_length(df$AA.JUNCTION[1])) {
f <- table(str_sub(df$AA.JUNCTION,i,i))
for(j in 1:length(f)){
if( length(which(str_detect(names(f),names(sim[str_which(sim,names(f)[j])])))) == 0 ){
names(f)[j] = names(sim[str_which(sim,names(f)[j])])
}else if(which(str_detect(names(f),names(sim[str_which(sim,names(f)[j])]))) != j){
f[which(str_detect(names(f),names(sim[str_which(sim,names(f)[j])])))] = f[which(str_detect(names(f),names(sim[str_which(sim,names(f)[j])])))] + f[j]
names(f)[j] = "dip"
}
}
if(length(which(names(f) == "dip")) != 0 ){
f = f[-which(names(f) == "dip")]
}
if (length(which(f == max(f))) > 1){
tempopt = "-"
tempopt = paste(tempopt,names(which(f == max(f)))[1],sep = "")
for(j in 2:(length(which(f == max(f))))){
tempopt = paste(tempopt, names(which(f == max(f)))[j],sep = "|")
}
tempopt = paste(tempopt,"-",sep = "")
xar[i] = tempopt
}else{
xar[i] = names(which(f == max(f)))
}
}
paste(xar,collapse = ' ')
}
}
#Opt2New(AminoCl(5))
Id <- function(ff){
if(flagtic == TRUE) tic(sprintf("Id -- Type: $s ", cho))
if(cho == "Identity"){
par(xpd=TRUE)
pp <- data.frame(x = ff$level,y = ff$sumper)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
ggplot(na.omit(pp),aes(x = x,y = y)) + stat_sum()
}else{
par(xpd=TRUE)
pp <- data.frame(x = ff$level,y = ff$sumper2)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
ggplot(na.omit(pp),aes(x = x,y = y)) + stat_sum()
}
}
cc <- function(df){
if(flagtic == TRUE) tic("Collapsible Tree")
df = df[ do.call( order , df[ , match( colnames(df[str_which(names(df), "level.")]) , names(df) ) ] ) , ]
ii = Clus$seqnum
ii = as.character(ii)
trid1 = df[str_which(names(df), "level.")] # Data frame with identities percentage
kk1 = trid1
trsim = df[str_which(names(df), "level.")] # Data frame with similarities percentage
iii = df[str_which(names(df), "level.")]
for(i in 1:length(trid1)){
tempg = trid1[,i]+1
trid1[i] = round(Clus$Identity[tempg],digits = 2)
temph = trsim[,i]+1
trsim[i] = round(Clus$Similarity[temph], digits = 2)