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05-03-2018 .R
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05-03-2018 .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")
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$temp= 0 # Creating a temp column with 0
listq = list("ggdf" = ggdf, "dfsum" = dfsum,"list" = listxx, "listn" = listyy,"udata" = udata,"permat"= NA, "persim" = NA, "br" = br, "cl" = cl, "met" = met, "ep" = ep , "clep" = clep, "nn" = nn, "sumper" = NA,"sumper2" = NA, "ela" = NA, "cel" = NA,"endper" = endper, "last" = last)
algo = "Identity"
algocol = 0
#list1 = listq
#list1 = result2
# Function Matrices compute the apsolute matrix, the matrix with percentages, the absolute similarity matrix and the percentage similarity matrix
Matrices <- function(list1,leaf){
if(flagtic == TRUE) tic("Matrices")
udata = list1$udata
permat = list1$permat
persim = list1$persim
br = list1$br
cl = list1$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
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])
if(algo == "Identity"){
if (sumper > endper){
nn = TRUE # When nn = TRUE the percentage of sumper < endper%
if (sumper2 > endper){
last = last+1
if (last == 1 && leaf == FALSE){
clmax = cl
brtemp = br
while (brtemp < clmax) {
brtemp = brtemp +1
print(brtemp)
print(clmax)
print(last)
list1 = list("ggdf" = list1$ggdf, "dfsum" = list1$dfsum,"list" = listxx, "listn" = listyy, "udata" = udata,"permat"= permat, "persim" = persim, "br" = brtemp, "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$endper, "last" = last)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
list1 = Matrices(list1,TRUE)
}
}
return(list1) # End of the programm, returning the final list
}
}
}else{
if (sumper2 > endper){
last = last+1
if (last == 1 && leaf == FALSE){
clmax = cl
brtemp = br
while (brtemp < clmax) {
brtemp = brtemp +1
print(brtemp)
print(clmax)
print(last)
list1 = list("ggdf" = list1$ggdf, "dfsum" = list1$dfsum,"list" = listxx, "listn" = listyy, "udata" = udata,"permat"= permat, "persim" = persim, "br" = brtemp, "cl" = cl, "met" = list1$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)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
list1 = Matrices(list1,TRUE)
}
}
return(list1) # End of the programm, returning the final list
}
}
vv = length(udata[udata$clusters == br,]$AA.JUNCTION)
dfsum[nrow(dfsum) + 1,] = c(sumper,sumper2,br,vv)
result1 = list("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$endper, "last" = last)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
if ( leaf == TRUE){
nn = FALSE
result1 = list("ggdf" = list1$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)
return(result1)
}else{
Choice(result1)
}
}
#list2 = result1
# Function Choice choose which matrix cell will be used for the division of the data
Choice <- function(list2){
if(flagtic == TRUE) tic("Choice")
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
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$persim
}
cel = which(permat[,(algocol + 1):ncol(permat)] == max(permat[,(algocol + 1):ncol(permat)]), arr.ind = TRUE)
ela = 1
poss = max(permat[,(algocol + 1):ncol(permat)])
if (max(permat[,(algocol + 1):ncol(permat)]) == 100){ # We exclude the 100 % from the max values
cel = which(permat[,(algocol + 1):ncol(permat)] == max(permat[,(algocol + 1):ncol(permat)][permat[,(algocol + 1):ncol(permat)]!=max(permat[,(algocol + 1):ncol(permat)])]), arr.ind = TRUE) # The desired cell
poss = max(permat[,(algocol + 1):ncol(permat)][permat[,(algocol + 1):ncol(permat)]!=max(permat[,(algocol + 1):ncol(permat)])])
}
if(poss == 0){
clmax = cl
brtemp = br
while (brtemp < clmax) {
brtemp = brtemp +1
print(brtemp)
list2 = list("ggdf" = list2$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)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
list2 = Matrices(list2,TRUE)
}
return(list2)
}
if(algo == "Identity"){
if ((length(cel)/2) > 1){
eqcol = vector('numeric') # A vector for the cell's rows with the same entropy
j = 1
for(i in 1:(length(cel)/2)){
if (permat[,(algocol + 1):ncol(permat)][nrow(permat[,(algocol + 1):ncol(permat)]),cel[i,2]] < permat[,(algocol + 1):ncol(permat)][nrow(permat[,(algocol + 1):ncol(permat)]),cel[ela,2]]) { # If the percentage is the same (cel multidimensional) we keep the cel with the lowest entropy
ela = i
eqcol[] = 0
eqcol[1] = i
j = 2
}else if(permat[,(algocol + 1):ncol(permat)][nrow(permat[,(algocol + 1):ncol(permat)]),cel[i,2]] == permat[nrow(permat[,(algocol + 1):ncol(permat)]),cel[ela,2]]){
eqcol[j] = i
j = j+1
}
}
eqcol <- eqcol[-which(eqcol == 0)]
if (length(eqcol)>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
ela = eqcol[1]
meg = persim[,(algocol + 1):ncol(persim)][str_which(sim,let[cel[ela,1]]),cel[ela,2]]
for(i in 2:length(eqcol)){
if(persim[,(algocol + 1):ncol(persim)][str_which(sim,let[cel[eqcol[i],1]]),cel[eqcol[i],2]] > meg){ # We find the maximum percentage of the similarity percentage marix
meg = persim[,(algocol + 1):ncol(persim)][str_which(sim,let[cel[eqcol[i],1]]),cel[eqcol[i],2]]
ela = eqcol[i]
} else if (persim[,(algocol + 1):ncol(persim)][str_which(sim,let[cel[eqcol[i],1]]),cel[eqcol[i],2]] == meg){ # If the percentage is the same we use the entropy
if(persim[,(algocol + 1):ncol(persim)][nrow(persim[,(algocol + 1):ncol(persim)]),cel[eqcol[i],2]] < persim[,(algocol + 1):ncol(persim)][nrow(persim[,(algocol + 1):ncol(persim)]),cel[ela,2]]){
ela = i
}
}
}
}
}
}else{
if ((length(cel)/2) > 1){
ela = 1
for(i in 2:(length(cel)/2)){
if (permat[,(algocol + 1):ncol(permat)][nrow(permat[,(algocol + 1):ncol(permat)]),cel[i,2]] < permat[,(algocol + 1):ncol(permat)][nrow(permat[,(algocol + 1):ncol(permat)]),cel[ela,2]]) { # If the percentage is the same (cel multidimensional) we keep the cel with the lowest entropy
ela = i
}
}
}
}
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
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)
}
for(i in 1:length(z1)){
udata[str_which(udata$AA.JUNCTION,z1[i]),sprintf('level.%d', ep)] = cl+1
}
for(i in 1:length(z2)){
udata[str_which(udata$AA.JUNCTION,z2[i]),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
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)
}
for(i in 1:length(z1)){
udata[str_which(udata$AA.JUNCTION,z1[i]),sprintf('level.%d', ep)] = cl+1
}
for(i in 1:length(z2)){
udata[str_which(udata$AA.JUNCTION,z2[i]),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("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(1,2,1,50)[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,]$AA.JUNCTION[1]))){
return(list2)
}
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
list2 = Matrices(list2, TRUE)
if(is.na(((clep[br] < clep[br+1]) && (sum(clep == clep[br]) != (2^clep[br]))))){
return(list2)
}
if( ((clep[br] < clep[br+1]) && (sum(clep == clep[br]) != (2^clep[br]))) == TRUE ){ # If the next branch is in the next level
met = 0
ep = ep + 1
br = br + 1
}else{
br = br + 1
met = met +2
}
list2 = list("ggdf" = list2$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$last)
}
if( met == geomSeq(1,2,1,50)[ep+1]){ # When the counter reaches the end value (geometric sequence) we increase the level counter
met = 0
ep = ep + 1
}
result2 = list("ggdf" = list2$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$last)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
Matrices(result2,FALSE)
}
# A function that generates a geometric sequence
geomSeq <- function(start,ratio,begin,end){
begin=begin-1
end=end-1
start*ratio**(begin:end)
}
tic()
lastlist = Matrices(listq,FALSE)
toc()
if(flagtic == TRUE){
tic.clearlog()
tic("lastlist")
}
lastlist = Matrices(listq,FALSE)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
# The final name of udata data frame
df = lastlist$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]
}
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(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)
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"))
# A function to plot with logo level lev
LogoLev <- function(lev){
if(flagtic == TRUE) tic(sprintf("LogoLev -- Level: %d ", lev))
t1 = which(lastlist$clep == lev)
listff = list()
if(length(t1) %% 3 == 0){
nc = length(t1)%/%3
}else{
nc = length(t1)%/%3 + 1
}
for(i in 1:length(t1)){
x1 = as.data.frame(na.omit(df[df[which(names(df) == sprintf("level.%d", lev))] == t1[i], ]$AA.JUNCTION))
names(x1)[1]= "AA.JUNCTION"
x1 = as.character(x1$AA.JUNCTION)
listff$temp = x1
names(listff)[length(listff)] = sprintf('Cluster.%d', t1[i])
}
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
ggseqlogo(listff, ncol=nc, method = "prob",col_scheme=cs1)
}
# 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)
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"){
t2 = which(lastlist$clep == lev)
if(length(t2) %% 3 == 0){
par(mfrow = c(length(t2)%/%3,3))
}else{
par(mfrow = c(length(t2)%/%3 + 1,3))
}
for(i in 1:length(t2)){
ar = str_which(names(perlist),as.character(t2[i]))[1]
par(xpd=TRUE)
output <- matrix(unlist(perlist[ar]), ncol = str_length(lastlist$udata$AA.JUNCTION[1]), byrow = FALSE)
barplot(output[-nrow(output),], col=heat.colors(length(output[,1])-1), width=2, main = sprintf('Cluster.%d', t2[i]))
legend("topright",inset=c(-0.03,0), fill=heat.colors(length(output[,1])-1), legend=let,cex = 0.6)
}
}else{
t2 = which(lastlist$clep == lev)
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)
barplot(output[-nrow(output),], col=heat.colors(length(output[,1])-1), width=2, main = sprintf('Cluster.%d', t2[i]))
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)
}
# 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"){
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{
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))
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
}
# Sequences and Id's for specific cluster
AminoCl <- function(clust){
#if(flagtic == TRUE) tic("AminoCl")
if(clust == 0){
x3 = data.frame("Sequence.ID" = df$Sequence.ID, "AA.JUNCTION" = df$AA.JUNCTION)
}else{
#lastlist$clep[clust]
x3 = data.frame(na.omit(df[df[which(names(df) == sprintf("level.%d", lastlist$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$AA.JUNCTION)
aa = as.list(aa)
names(aa) = se
}
#if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
x3 # Print in console
}
# A function which visualize the sequences of a data frame using common letters (i.e. "A _ _ _ _ K R _ _ _ Q _ Y Y Y _ _ _ T _")
Opt <- function(df){
#if(flagtic == TRUE) tic("Opt")
xar <- matrix(0,nrow=1, ncol=str_length(df$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 = ' ')
}
OptNew <- function(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$AA.JUNCTION,i,i))
if (length(which(f == max(f))) > 1){
tempopt = "-"
tempopt = paste(tempopt,names(which(f == max(f)))[1],sep = "")
#tempopt = names(which(f == max(f)))[1]
for(j in 2:(length(which(f == max(f))))){
tempopt = paste(tempopt, names(which(f == max(f)))[j],sep = "|")
}
tempopt = paste(tempopt,"-",sep = "")
xar[i] = tempopt
}else{
xar[i] = names(which(f == max(f)))
}
}
paste(xar,collapse = ' ')
}
# A function which visualize the sequences of a data frame using similarity groups (i.e. "A _ _ _ _ K Am _ Ba _ Ac _ Y Y Y _ _ _ T _")
Opt2 <- function(df,alt){
#if(flagtic == TRUE) tic("Opt2")
xar <- matrix(0,nrow=1, ncol=str_length(df$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 = ' ')
}
Opt2New <- function(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 = ' ')
}
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$sumper)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
ggplot(pp,aes(x = x,y = y)) + stat_sum()
}else{
par(xpd=TRUE)
pp <- data.frame(x = ff$level,y = ff$sumper2)
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
ggplot(pp,aes(x = x,y = y)) + stat_sum()
}
}
cc <- function(df){
if(flagtic == TRUE) tic("Collapsible Tree")
df = df[ do.call( order , df[ , match( colnames(df[str_which(names(df), "level.")]) , names(df) ) ] ) , ]
ii = Clus$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)
tempi = iii[,i] + 1
iii[i] = ii[tempi]
}
for(i in 2:length(str_which(names(df), "level."))){
trid1[,i] = paste(kk1[,i],iii[,i],trid1[,i], trsim[,i],sep = " ")
for(j in 1:nrow(df)){
if(str_detect(trid1[j,i],"NA") == TRUE){
trid1[j,i] = NA
}
}
}
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
collapsibleTree(
trid1,
hierarchy = colnames(df[str_which(names(df), "level.")]),
fill = c("jj",ii),
width = 1820,
height = 775,
collapsed = FALSE
)
}
idenlev <- function(lev){
if(flagtic == TRUE) tic(sprintf("idenlev -- Level: %d ", lev))
if(cho == "Identity"){
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
sum(Clus[Clus$level == lev,]$Identity) / length(Clus[Clus$level == lev,]$Identity)
}else{
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
sum(Clus[Clus$level == lev,]$Similarity) / length(Clus[Clus$level == lev,]$Similarity)
}
}
idencl <- function(cl){
if(flagtic == TRUE) tic(sprintf("idencl -- Cluster :%d ", cl))
if(cho == "Identity"){
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
Clus[Clus$ClusterId == cl,]$Identity
}else{
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
Clus[Clus$ClusterId == cl,]$Similarity
}
}
EmPin <- function(lastlist,Clus,dfsd,flagtic){
if(flagtic == TRUE) tic("EmPin")
for (i in 0:(max(lastlist$clep))) {
t1 = sum(Clus[Clus$level == i,]$Identity) / length(Clus[Clus$level == i,]$Identity)
t2 = sd(Clus[Clus$level == i,]$Identity)
t3 = sum(Clus[Clus$level == i,]$Similarity) / length(Clus[Clus$level == i,]$Similarity)
t4 = sd(Clus[Clus$level == i,]$Similarity)
rows = sprintf("level.%d", i)
dfsd[i+1,] = c(rows,t1,t2,t3,t4)
}
colnames(dfsd)[1]="Level"
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
dfsd
}
lev = 5
thrt = "Distance"
thrtyp = ffg
Netw <- function(lev,thr,thrt,netyp,df,lastlist,Clus,sim,altsim,ts,shth,net_sil,flagtic){
if(flagtic == TRUE) tic(sprintf("Network -- level: %d, Network type: %s, Threshold type: %s, Threshold value: %d, Using silhouette: %d ", lev,netyp,thrt,thr,net_sil))
# We change the plotting parameters
df = df[ do.call( order , df[ , match( colnames(df[str_which(names(df), "level.")]) , names(df) ) ] ) , ]
df_args <- c(df[str_which(names(df), "level.")], sep="/")
if(lev == max(lastlist$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$pathString<- do.call(paste, df_args)
#df$pathString <- paste(df$level.0, df$level.1, df$level.2 ,df$level.3 ,df$level.4 ,df$level.5 ,df$level.6 ,df$level.7 ,df$level.8 ,df$level.9,df$level.10 ,df$level.11 ,df$level.12 ,df$level.13 ,df$level.14 ,df$level.15 ,df$level.16, df$level.17, df$level.18, df$level.19 , sep = "/")
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)
bo = which(lastlist$clep == lev)
ffg = matrix(NA,nrow = max(bo)+1,ncol = (max(bo)+1)) # the distance between 2 Nodes
ffg2 = matrix(NA,nrow = max(bo)+1,ncol = (max(bo)+1)) # the string distance between 2 Nodes (string from function Opt())
ffg2sim = matrix(NA,nrow = max(bo)+1,ncol = (max(bo)+1))
ffg3 = matrix(NA,nrow = max(bo)+1,ncol = (max(bo)+1)) # the final distance combining ffg and ffg2
# anw trigwnikos
for(i in 1:(max(bo)+1)){
tempN1 = FindNode(xN,(sprintf("%d", i-1)))
tempN1 = tempN1$path
temp2N1 = Opt(AminoCl(i-1))
temp3N1 = Opt2(AminoCl(i-1),TRUE)
for(j in i:(max(bo)+1)){ # max(df$clusters)+1
tempN2 = FindNode(xN,(sprintf("%d", j-1)))
tempN2 = tempN2$path
tem = which(tempN1 == tempN2)
#tempN1[tem[length(tem)]]
d1 = length(tempN1) - tem[length(tem)]
d2 = length(tempN2) - tem[length(tem)]
ffg[i,j]= d1 + d2
temp2N2 = Opt(AminoCl(j-1))
temp2N2 = str_replace_all(temp2N2,"_"," ")
temp3N2 = Opt2(AminoCl(j-1),TRUE)
temp3N2 = str_replace_all(temp3N2,"_"," ")
ffg2[i,j] = stringdist(temp2N1,temp2N2,method = "lv" )
ffg2sim[i,j] = stringdist(temp3N1,temp3N2,method = "lv" )
if( i == j ){
ffg2[i,j] = str_length(lastlist$udata$AA.JUNCTION[1]) #gia na mhn fainontai velakia pisw
ffg2sim[i,j] = str_length(lastlist$udata$AA.JUNCTION[1])
}
}
}
#find max
ma =max(na.omit(as.vector(ffg)))
ffg[which(ffg == 0)] = ma #gia na mhn fainontai velakia pisw
ffg2 = str_length(lastlist$udata$AA.JUNCTION[1]) - ffg2
ffg2sim = str_length(lastlist$udata$AA.JUNCTION[1]) - ffg2sim
ffg3 = (2/str_length(lastlist$udata$AA.JUNCTION[1])) * ffg2 + (2/str_length(lastlist$udata$AA.JUNCTION[1])) * ffg2sim + (2 / ma) * (ma - ffg)
if(thrt == "Distance"){
thrtyp = ffg
}else if (thrt == "StrIdentSimilarity"){
thrtyp = ffg2
}else if (thrt == "StrGroupSimilarity"){
thrtyp = ffg2sim
}else{
thrtyp = ffg3
}
tempor = ffg
tempor2 = ffg2
tempor2sim = ffg2sim
tempor3 = ffg3
tempor[which(thrtyp > thr)] = 0
tempor2[which(thrtyp > thr)] = 0
tempor2sim[which(thrtyp > thr)] = 0
tempor3[which(thrtyp> thr)] = 0
if(thrt == "Distance"){
thrtyp2 = tempor
}else if (thrt == "StrIdentSimilarity"){
thrtyp2 = tempor2
}else if (thrt == "StrGroupSimilarity"){
thrtyp2 = tempor2sim
}else{
thrtyp2 = tempor3
}
if(net_sil == TRUE){
jhj = silhouette(Clus$level[1:(max(bo)+1)],t(tempor2))
matches <- regmatches(unique(x$pathString), gregexpr("[[:digit:]]+", unique(x$pathString)))
for(i in 1:length(unique(x$pathString))){
ttt =jhj[as.numeric(unlist(matches[i])),3]
flag = FALSE
j = length(ttt)
mcl = length(ttt)
while(flag == FALSE){
if(ttt[j] >= ttt[j-1]){
tempor3[j-1,j] = 0
ffg3[j-1,j] = 0
thrtyp[j-1,j] = 0
thrtyp2[j-1,j] = 0
j = j - 1
}else{
mcl = j
flag = TRUE
}
if(j == 1){
flag = TRUE
}
}
}
}
jjj = matrix(1,nrow = max(bo)+1,ncol = (max(bo)+1))
net0 <- graph_from_adjacency_matrix(jjj,mode = "upper")
net1 <- graph_from_adjacency_matrix(jjj,mode = "upper")
bb = vector(length = (max(bo)+1))
bb[1]=0
bb[2:length(bb)] = lastlist$clep[1:max(bo)]
#XRWMA
# Generate colors based on media type:
colrs <- c("#1E90FF", "#BA55D3", "#0000FF", "#557fd2", "#54d17e", "#8aad62", "#C6E2FF", "#e5e234", "#FFD700", "#00EE00", "#C1FFC1", "#ea8509", "#54FF9F", "#FF0000", "#ed3b1c", "#ed1c7a", "#0c0c0c", "#b8d8af", "#ED9121","#45f713")
colrs = colrs[1:(max(bb)+1)]
V(net0)$color <- colrs[bb+1]
V(net1)$color <- colrs[bb+1]
# Compute node degrees (#links) and use that to set node size:
deg <- (Clus$seqnum[1:length(bb)] / Clus$seqnum[1]) * 20
V(net0)$size <- deg
V(net1)$size <- deg
# The labels are currently node IDs.
# Setting them to NA will render no labels:
V(net0)$label <- V(net0)-1
V(net1)$label <- V(net1)-1
# Set edge width based on weight:
hhh = na.omit(as.vector(t(ffg3)))
hhh1 = na.omit(as.vector(t(tempor3)))
E(net0)$width <- hhh
E(net1)$width <- hhh1
#change arrow size and edge color:
E(net0)$arrow.size <- .2
E(net0)$edge.color <- "gray80"
E(net1)$arrow.size <- .2
E(net1)$edge.color <- "gray80"
pal1 <- rainbow(6, alpha=1)
net0.copy <- igraph::delete.edges(net0, which(E(net0)$width == 0))
net1.copy <- igraph::delete.edges(net1, which(E(net1)$width == 0))
if(flagtic == TRUE) toc(log = TRUE,quiet = TRUE)
listnet = list("net0.copy" = net0.copy, "net1.copy" = net1.copy, "bb" = bb, "colrs" = colrs, "ma" = ma, "ffg" = ffg, "ffg2" = ffg2, "ffg2sim" = ffg2sim, "ffg3" = ffg3 ,"tempor" = tempor, "tempor2" = tempor2, "tempor2sim" = tempor2sim, "tempor3" = tempor3,"thrtyp" = thrtyp, "thrtyp2" = thrtyp2)
return(listnet)
}
tddf = vector()
for(i in 1:123){
if(is.na(df$level.4[i]) || is.na(df$level.5[i])){
tddf[i] = ffg3[df$level.4[i],df$level.5[i]]
}else if (df$level.4[i] > df$level.5[i]){
tddf[i] = ffg3[df$level.5[i],df$level.4[i]]
}else if (df$level.4[i] < df$level.5[i]){
tddf[i] = ffg3[df$level.4[i],df$level.5[i]]
}
}tddf
ttt1 = df$level.2
ttt2 = df$level.3
for (i in min(na.omit(ttt1)):max(na.omit(ttt2))){
}
hgh = ffg3[min(na.omit(ttt1)):max(na.omit(ttt2)),min(na.omit(ttt1)):max(na.omit(ttt2))]
row.names(hgh) = min(na.omit(ttt1)):max(na.omit(ttt2))
colnames(hgh) = min(na.omit(ttt1)):max(na.omit(ttt2))
silhouette(df$level.2,t(hgh))
hgh = ffg3[min(na.omit(ttt1)):max(na.omit(ttt1)),min(na.omit(ttt1)):max(na.omit(ttt1))]
row.names(hgh) = min(na.omit(ttt1)):max(na.omit(ttt1))
colnames(hgh) = min(na.omit(ttt1)):max(na.omit(ttt1))
silhouette(df$level.2,hgh)
#express <- mouse[1:25,c("M1","M2","M3","NC1","NC2","NC3")]
#rownames(express) <- mouse$ID[1:25]
#intern <- clValid(express, 2:6, clMethods=c("hierarchical","kmeans","pam"),
# validation="internal")
#summary(intern)
#optimalScores(intern)
#plot(intern)
ffg3[7:8,7:8]
hgh = ffg3[c(5,10,11),c(5,10,11)]
rownames(hgh) = c(4,9,10)
colnames(hgh) = c(4,9,10)
silhouette(df[df$level.2 == 4,]$level.3,hgh)
bo = which(lastlist$clep == lev)
ffg = matrix(NA,nrow = nrow(ppp),ncol = nrow(ppp)) # the distance between 2 Nodes
ffg2 = matrix(NA,nrow = nrow(ppp),ncol = nrow(ppp)) # the string distance between 2 Nodes (string from function Opt())
ffg2sim = matrix(NA,nrow = nrow(ppp),ncol = nrow(ppp))
ffg3 = matrix(NA,nrow = nrow(ppp),ncol = nrow(ppp)) # the final distance combining ffg and ffg2
ppp = df[df$level.2 == 4,]
# anw trigwnikos
for(i in 1:nrow(ppp)){
tempN1 = FindNode(xN,(sprintf("%d", ppp$level.3[i])))
tempN1 = tempN1$path
temp2N1 = Opt(AminoCl(ppp$level.3[i]))
temp3N1 = Opt2(AminoCl(ppp$level.3[i]),TRUE)
for(j in i:nrow(ppp)){ # max(df$clusters)+1
tempN2 = FindNode(xN,(sprintf("%d", ppp$level.3[j])))
tempN2 = tempN2$path
tem = which(tempN1 == tempN2)
#tempN1[tem[length(tem)]]
d1 = length(tempN1) - tem[length(tem)]
d2 = length(tempN2) - tem[length(tem)]
ffg[i,j]= d1 + d2
temp2N2 = Opt(AminoCl(ppp$level.3[j]))
temp2N2 = str_replace_all(temp2N2,"_"," ")