R/GraphsOPLSDA.R
In AlzbetaG/Metabol: The statistical analysis of metabolomic data
#' Orthogonal partial least squares - discriminant analysis (OPLS-DA)
#'
#' Makes orthogonal partial least squares - discriminant analysis (OPLS-DA), displays score plots and s-plots.
#' @param data Data table with variables (metabolites) in columns. Samples in rows are sorted according to specific groups.
#' @param name A character string or expression indicating a name of data set. It occurs in names of every output.
#' @param groupnames A character vector defining specific groups in data. Every string must be specific for each group and they must not overlap.
#' @param tsf Data transformation must be defined by "clr" (default), "log", "log10", "PQN", "lnPQN", "pareto" or "none". See Details.
#' @param axx Which axis of S-plot is used to choose most important variables: p1 or p1corr (default).
#' @param top How many most important variables (in absolute values) should be highlighted in s-plot? The default is 30.
#' @param qu Which quantile of the important variables (in absolute values) should be highlighted in s-plot? The default is 0.75.
#' @param QCs logical. If FALSE (default) quality control samples (QCs) are automatically distinguished and skipped.
#' @details Data transformation: with "clr" clr trasformation is used (see References), with "log" natural logarithm is used, with "log10" decadic logarithm is used, with "pareto" data are only scaled by Pareto scaling, with "PQN" probabilistic quotient normalization is done, with "lnPQN" natural logarithm of PQN transformed data is done, with "none" no tranformation is done.
#' @details S-plots can be used only for comparison of two groups. If there is more groups in data, all possible combinations of pairs are evaluated.
#' @details Up to twenty different groups can be distinguished in data (including QCs).
#' @return Score plot and s-plots of OPLS-DA.
#' @return Excel file with s-plot summary for every variable.
#' @import car
#' @import rrcov
#' @import utils
#' @importFrom stats median prcomp pf cov
#' @importFrom robCompositions cenLR
#' @references Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman & Hall Ltd., London (UK). p. 416.
#' @references Gaude, E.et al. (2012) muma: Metabolomics Univariate and Multivariate Analysis. R package version 1.4. \url{https://CRAN.R-project.org/package=muma}
#' @export
GraphsOPLSDA=function(data,name,groupnames,tsf="clr",axx="p1corr",top=30,qu=0.75,QCs=FALSE){
dirout=getwd()
##########################################################################################################################
if (tsf=="clr"){
dataM=cenLR(data)$x.clr
}
if (tsf=="log"){
dataM=log(data)
}
if (tsf=="log10"){
dataM=log10(data)
}
if (tsf=="pareto"){
dataM=scale(data, scale=TRUE, center=FALSE)
}
PQN1=function (x){
xref=apply(x,2,median)
podil=x
for (i in 1:ncol(x)){
podil[,i]=x[,i]/xref[i]
}
s=apply(podil,1,median)
PQN=x
for (j in 1:nrow(x)){
PQN[j,]=x[j,]/s[j]
}
return(PQN)
}
if (tsf=="PQN"){
dataM=PQN1(data)
}
if (tsf=="lnPQN"){
dataM=log(PQN1(data))
}
if (tsf=="none"){
dataM=data
}
#################################################################################################
count=length(groupnames)
basecolor=c("blue","magenta","forestgreen","darkorange","deepskyblue","mediumaquamarine","lightslateblue","saddlebrown",
"gray40","darkslateblue","firebrick","darkcyan","darkmagenta", "deeppink1","limegreen","gold2","bisque2",
"lightcyan3","red","darkolivegreen3") # Basic colours from: http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
basemarks=c(15,17,18,8,11,2,0,16,5,6,4,10,3,7,9,12)
Group=matrix(rep(NA,count*3),ncol=3)
colnames(Group)=c("min","max","length")
rownames(Group)=groupnames
groups=NULL
marks=NULL
color=NULL
for (i in 1:count){
Gr=grep(groupnames[i],rownames(dataM))
Group[i,1]=min(Gr)
Group[i,2]=max(Gr)
Group[i,3]=length(Gr)
gr=rep(i,length(Gr))
groups=c(groups,gr)
zn=rep(basemarks[i],length(Gr))
marks=c(marks,zn)
cl=rep(basecolor[i],length(Gr))
color=c(color,cl)
}
##########################################################################################################################
if (QCs==FALSE){
QC=grep("QC",rownames(dataM))
if (length(QC)!=0){
dataM=dataM[-QC,]
color=color[-QC]
marks=marks[-QC]
groups=groups[-QC]
groupnames=groupnames[unique(groups)]
count=length(groupnames)
Group=matrix(rep(NA,count*3),ncol=3)
colnames(Group)=c("min","max","length")
rownames(Group)=groupnames
for (i in 1:count){
Gr=grep(groupnames[i],rownames(dataM))
Group[i,1]=min(Gr)
Group[i,2]=max(Gr)
Group[i,3]=length(Gr)
}
}
}
##########################################################################################################################
dataSetM2=dataM
colors=matrix(unique(color),ncol=1)
#################################################################################################
dirout2 = paste(dirout,"/","OPLSDA",sep = "")
dir.create(dirout2, recursive=TRUE, showWarnings = FALSE) # vytvori v zadane ceste slozku s nazvem "note"
setwd(dirout2)
#################################################################################################
explore.data2=function (file, scaling, scal = TRUE, normalize = FALSE, imputation = FALSE,
imput,color=colors2)
{
#comp = read.csv("OPLSDA_Data.csv", sep = ";", header = TRUE)
comp <- file
comp.x = comp[, 3:ncol(comp)]
comp.x = cbind(comp[, 2], comp[, 1], comp.x)
x <- comp.x
x.x <- x[, 3:ncol(x)]
rownames(x.x) <- x[, 2]
if (!scal) {
scaling = ""
}
dirout = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/", sep = "")
dir.create(dirout)
if (imputation) {
y = x.x
r = is.na(y)
for (k in 1:ncol(r)) {
vec = matrix(r[, k], ncol = 1)
who.miss.rows = which(apply(vec, 1, function(i) {
any(i)
}))
if (length(who.miss.rows) > nrow(y) * 0.8) {
warning(paste("The variable -", colnames(y)[k],
"- has a number of missing values > 80%, therefore has been eliminated",
sep = " "))
y = y[, -k]
}
}
r = is.na(y)
who.miss.columns = c()
for (i in 1:nrow(y)) {
for (j in 1:ncol(y)) {
if (r[i, j] == TRUE) {
if (imput == "mean") {
v2 = matrix(r[, j], ncol = 1)
who.miss.rows = which(apply(v2, 1, function(i) {
any(i)
}))
y[i, j] = mean(y[-who.miss.rows, j])
print(paste("Imputing missing value of variable -",
colnames(y)[j], "- for the observation -",
rownames(y)[i], "- with", imput, "value",
sep = " "))
}
else if (imput == "minimum") {
v2 = matrix(r[, j], ncol = 1)
who.miss.rows = which(apply(v2, 1, function(i) {
any(i)
}))
y[i, j] = min(y[-who.miss.rows, j])
print(paste("Imputing missing value of variable -",
colnames(y)[j], "- for the observation -",
rownames(y)[i], "- with", imput, "value",
sep = " "))
}
else if (imput == "half.minimum") {
v2 = matrix(r[, j], ncol = 1)
who.miss.rows = which(apply(v2, 1, function(i) {
any(i)
}))
y[i, j] = min(y[-who.miss.rows, j])/2
print(paste("Imputing missing value of variable -",
colnames(y)[j], "- for the observation -",
rownames(y)[i], "- with", imput, "value",
sep = " "))
}
else if (imput == "zero") {
v2 = matrix(r[, j], ncol = 1)
who.miss.rows = which(apply(v2, 1, function(i) {
any(i)
}))
y[i, j] = 0
print(paste("Imputing missing value of variable -",
colnames(y)[j], "- for the observation -",
rownames(y)[i], "- with", imput, "value",
sep = " "))
}
}
}
}
pwdi = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ImputedMatrix.csv", sep = "")
write.csv(y, pwdi)
x.x = y
}
#for (i in 1:nrow(x.x)) {
# for (j in 1:ncol(x.x)) {
# if (x.x[i, j] <= 0) {
# x.x[i, j] = runif(1, 0, 1e-10)
# }
# }
#}
x.x = cbind(comp[, 2], x.x)
write.csv(x.x, paste(dirout, "CorrectedTable.csv", sep = ""))
pwd.c = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP, "/CorrectedTable.csv",
sep = "")
x <- read.csv(pwd.c, sep = ",", header = TRUE)
x.x <- x[, 3:ncol(x)]
rownames(x.x) <- x[, 1]
k = matrix(x[, 2], ncol = 1)
if (normalize) {
x.t <- t(x.x)
x.s <- matrix(colSums(x.t), nrow = 1)
uni = matrix(rep(1, nrow(x.t)), ncol = 1)
area.uni <- uni %*% x.s
x.areanorm <- x.t/area.uni
x.areanorm = t(x.areanorm)
write.csv(x.areanorm, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
else {
write.csv(x.x, paste(dirout, "/ProcessedTable.csv", sep = ""))
}
if (scal) {
if (scaling == "Pareto" | scaling == "pareto" | scaling ==
"P" | scaling == "p") {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.areanorm.tc <- scale(x.x, center = TRUE, scale = FALSE)
all.sd <- matrix(apply(x.areanorm.tc, 2, sd), nrow = 1)
uni.exp.all = matrix(rep(1, nrow(x.areanorm.tc)),
ncol = 1)
all.sdm = uni.exp.all %*% all.sd
all.sqsd = sqrt(all.sdm)
all.pareto <- x.areanorm.tc/all.sqsd
write.csv(all.pareto, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
else if (scaling == "Auto" | scaling == "auto" | scaling ==
"A" | scaling == "a") {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.areanorm.tc <- scale(x.x, center = TRUE, scale = FALSE)
all.sd <- matrix(apply(x.areanorm.tc, 2, sd), nrow = 1)
uni.exp.all = matrix(rep(1, nrow(x.areanorm.tc)),
ncol = 1)
all.sdm = uni.exp.all %*% all.sd
all.auto <- x.areanorm.tc/all.sdm
write.csv(all.auto, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
else if (scaling == "Vast" | scaling == "vast" | scaling ==
"V" | scaling == "v") {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.areanorm.tc <- scale(x.x, center = TRUE, scale = FALSE)
all.sd <- matrix(apply(x.areanorm.tc, 2, sd), nrow = 1)
uni.exp.all = matrix(rep(1, nrow(x.areanorm.tc)),
ncol = 1)
all.sdm = uni.exp.all %*% all.sd
sdm2 = all.sdm^2
colm = matrix(colMeans(x.x), nrow = 1)
colm.m = uni.exp.all %*% colm
num = x.areanorm.tc * colm.m
vast = num/sdm2
write.csv(vast, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
else if (scaling == "Range" | scaling == "range" | scaling ==
"R" | scaling == "r") {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.areanorm.tc <- scale(x.x, center = TRUE, scale = FALSE)
range = c()
for (i in 1:ncol(x.x)) {
den = c()
den = max(x.x[, i]) - min(x.x[, i])
range = matrix(c(range, den), nrow = 1)
}
uni.exp.all = matrix(rep(1, nrow(x.areanorm.tc)),
ncol = 1)
range.m = uni.exp.all %*% range
all.range = x.areanorm.tc/range.m
write.csv(all.range, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
else if (scaling == "Median" | scaling == "median" |
scaling == "M" | scaling == "m") {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.areanorm.tc <- scale(x.x, center = TRUE, scale = FALSE)
all.med <- matrix(apply(x.areanorm.tc, 2, median),
nrow = 1)
uni.exp.all = matrix(rep(1, nrow(x.areanorm.tc)),
ncol = 1)
all.sdm = uni.exp.all %*% all.med
all.med <- x.areanorm.tc/all.sdm
write.csv(all.med, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
}
else {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.c = scale(x.x, scale = FALSE)
write.csv(x.c, paste(dirout, "/ProcessedTable.csv", sep = ""))
}
pwd.scal = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.scal, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
pc.all <- prcomp(x.x, center = FALSE, scale = FALSE)
p.v <- matrix(((pc.all$sdev^2)/(sum(pc.all$sdev^2))), ncol = 1)
p.i <- round(p.v * 100, 1)
p.z <- matrix(1, nrow(p.i), 1)
p.f <- cbind(p.i, p.z)
dirout.pca = paste(getwd(), "/PCA_Data_", scaling, noteOP,"/", sep = "")
dir.create(dirout.pca)
write.csv(p.f, paste(dirout.pca, "PCA_P", sep = ""))
write.csv(pc.all$x, paste(dirout.pca, "PCA_ScoreMatrix.csv",
sep = ""))
write.csv(pc.all$rotation, paste(dirout.pca, "PCA_LoadingsMatrix.csv",
sep = ""))
pwd.score = paste(getwd(), "/PCA_Data_", scaling, noteOP, "/", "PCA_ScoreMatrix.csv",
sep = "")
Score <- read.csv(pwd.score, sep = ",", header = TRUE)
Score.x <- Score[, 2:ncol(Score)]
rownames(Score.x) <- Score[, 1]
pwd.load = paste(getwd(), "/PCA_Data_", scaling, noteOP,"/", "PCA_LoadingsMatrix.csv",
sep = "")
Loading <- read.csv(pwd.load, sep = ",", header = TRUE)
Loading.x <- Loading[, 2:ncol(Loading)]
rownames(Loading.x) <- Loading[, 1]
pwd.pvar = paste(getwd(), "/PCA_Data_", scaling, noteOP,"/", "PCA_P",
sep = "")
Pvar <- read.csv(pwd.pvar, sep = ",", header = TRUE)
Pvar.x <- Pvar[, 2:ncol(Pvar)]
rownames(Pvar.x) <- Pvar[, 1]
barplot(Pvar.x[, 1], xlab = "Principal Components", ylab = "Proportion of Variance explained",
main = "Screeplot", ylim = c(0, 100))
scree = paste(dirout.pca, "Screeplot", scaling, noteOP, ".pdf", sep = "")
dev.copy2pdf(file = scree)
#tutticolors = matrix(c(1, 2, 3, 4, 5, 6, "rosybrown4",
# "green4", "navy", "purple2", "orange", "pink", "chocolate2",
# "coral3", "khaki3", "thistle", "turquoise3", "palegreen1",
# "moccasin", "olivedrab3", "azure4", "gold3", "deeppink",7, 8),
# ncol = 1)
tutticolors = as.matrix(colors2)
col = c()
for (i in 1:nrow(k)) {
col = c(col, tutticolors[k[i, ], ])
}
pairs = c()
if (ncol(Score.x) >= 10) {
pairs = c(10)
}
else {
pairs = c(ncol(Score.x))
}
pairs(Score.x[, 1:pairs], col = col)
#dev.new()
pairs = paste(dirout.pca, "First_10_Components_", scaling, noteOP,
".pdf", sep = "")
dev.copy2pdf(file = pairs)
K = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,"/class.csv",
sep = "")
write.csv(k, K)
x.nn = cbind(k, pc.all$x)
sorted = x.nn[order(x.nn[, 1]), ]
g = c()
for (i in 1:nrow(sorted)) {
if (any(g == sorted[i, 1])) {
g = g
}
else {
g = matrix(c(g, sorted[i, 1]), ncol = 1)
}
}
dirout.g = paste(getwd(), "/Groups_",noteOP, sep = "")
dir.create(dirout.g)
for (i in 1:nrow(g)) {
vuota <- c()
fin = matrix(rep(NA, ncol(sorted)), nrow = 1)
for (j in 1:nrow(sorted)) {
if (sorted[j, 1] == i) {
vuota <- matrix(sorted[j, ], nrow = 1)
rownames(vuota) = rownames(sorted)[j]
fin = rbind(fin, vuota)
}
}
nam = paste("r", i, sep = ".")
n = matrix(fin[-1, ], ncol = ncol(sorted))
n.x = matrix(n[, -1], ncol = ncol(sorted) - 1)
name = as.matrix(assign(nam, n.x))
outputfileg = paste("r.", i, ".csv", sep = "")
write.csv(name, paste(dirout.g, outputfileg, sep = "/"),
row.names = FALSE)
}
all.F = c()
NoF = nrow(g)
for (i in 1:NoF) {
for (j in 1:NoF) {
if (i < j) {
ni = paste("r.", i, ".csv", sep = "")
nj = paste("r.", j, ".csv", sep = "")
pwdi = paste(getwd(), "/Groups_",noteOP,"/", ni, sep = "")
pwdj = paste(getwd(), "/Groups_",noteOP,"/", nj, sep = "")
I = read.csv(pwdi, header = TRUE)
J = read.csv(pwdj, header = TRUE)
fin = ncol(I) - 1
ntest = factorial(fin)/(2 * (factorial(fin -
2)))
T2 = c()
nam = c()
for (k in 1:fin) {
for (l in 1:fin) {
if (k < l) {
Ikl = cbind(I[, k], I[, l])
Jkl = cbind(J[, k], J[, l])
t1 = matrix(T2.test(Ikl, Jkl)$statistic,
ncol = 2)
t2 = c(t1[, 1])
T2 = matrix(c(T2, t2), ncol = 1)
rownam = paste("PC", k, "vsPC", l, sep = "")
nam = matrix(c(nam, rownam), ncol = 1)
}
}
}
pair = paste("T2statistic_", i, "vs", j, sep = "")
rownames(T2) = nam
colnames(T2)[1] = pair
num = nrow(I) + nrow(J) - 3
den = 2 * (nrow(I) + nrow(J) - 2)
coeff = num/den
Fval = T2 * coeff
Fvalname = paste("F_statistic_", i, "vs", j,
sep = "")
colnames(Fval)[1] = Fvalname
Fpval = pf(Fval, 2, num)
Fname = paste("F_pvalue_", i, "vs", j, sep = "")
colnames(Fpval)[1] = Fname
Fpvalfin = 1 - Fpval
all.F = matrix(c(all.F, Fpvalfin))
}
}
}
varp = c()
for (k in 1:fin) {
for (l in 1:fin) {
if (k < l) {
varp = matrix(c(varp, p.f[k, 1] + p.f[l, 1]),
ncol = 1)
}
}
}
ncomparison = factorial(nrow(g))/(2 * (factorial(nrow(g) -
2)))
all.F = matrix(all.F, ncol = ncomparison)
rownames(all.F) = nam
allFpwd = paste(getwd(), "/PCA_Data_", scaling, noteOP, "/PCs_Fstatistic.csv",
sep = "")
write.csv(all.F, allFpwd, row.names = FALSE)
sum = matrix(rowSums(all.F), ncol = 1)
all = data.frame(nam, sum, varp)
colnames(all)[3] = "Variance(%)"
colnames(all)[2] = "Sum_p_values"
colnames(all)[1] = "Pair_of_PCs"
ord.sum = all[order(all[, 2]), ]
colnames(ord.sum)[3] = "Variance(%)"
colnames(ord.sum)[2] = "Sum_p_values(F_statistics)"
colnames(ord.sum)[1] = "Pair_of_PCs"
rownames(ord.sum) = 1:nrow(ord.sum)
rankFpwd = paste(getwd(), "/PCA_Data_", scaling, noteOP, "/PCs_ranked_Fpvalue.csv",
sep = "")
write.csv(ord.sum, rankFpwd, row.names = FALSE)
print("Pairs of Principal Components giving highest statistical cluster separation are:")
print(ord.sum[1:5, ])
}
##############################################################################################################################################
OPLSDA2=function (scaling,axx="",top="",qu="",color=colors2,marks,groupnames)
{
pwd.x = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP, "/ProcessedTable.csv",
sep = "")
x = read.csv(pwd.x, header = TRUE)
x.x = x[, 2:ncol(x)]
rownames(x.x) = x[, 1]
pwdK = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP, "/class.csv",
sep = "")
k = read.csv(pwdK, header = TRUE)
k.x = matrix(k[, -1], ncol = 1)
x.n = cbind(k.x, x.x)
sorted = x.n[order(x.n[, 1]), ]
k = matrix(sorted[, 1], ncol = 1)
g = c()
for (i in 1:nrow(sorted)) {
if (any(g == sorted[i, 1])) {
g = g
}
else {
g = matrix(c(g, sorted[i, 1]), ncol = 1)
}
}
Y = matrix(rep(NA, nrow(sorted)), ncol = 1)
for (i in 1:nrow(sorted)) {
for (l in 1:2) {
if (sorted[i, 1] == l) {
Y[i, ] = 0
}
else {
Y[i, ] = 1
}
}
}
X = as.matrix(sorted[, -1], ncol = ncol(sorted) - 1)
nf = 1
T = c()
P = c()
C = c()
W = c()
Tortho = c()
Portho = c()
Wortho = c()
Cortho = c()
for (j in 1:nf) {
w = (t(X) %*% Y) %*% solve(t(Y) %*% Y)
w1 = t(w) %*% w
w2 = abs(sqrt(w1))
w = w %*% solve(w2)
t = (X %*% w) %*% solve(t(w) %*% w)
t1 = t(t) %*% t
c = t(Y) %*% t %*% solve(t1)
c1 = t(c) %*% c
u = Y %*% c %*% solve(c1)
u1 = t(u) %*% u
u2 = abs(sqrt(u1))
p = (t(X) %*% t) %*% solve(t1)
wortho = p - w
wortho1 = t(wortho) %*% wortho
wortho2 = abs(sqrt(abs(wortho1)))
wortho = wortho %*% solve(wortho2)
tortho = X %*% wortho %*% solve(t(wortho) %*% wortho)
tortho1 = t(tortho) %*% tortho
portho = t(X) %*% tortho %*% solve(tortho1)
cortho = t(Y) %*% tortho %*% solve(tortho1)
X = X - tortho %*% t(portho)
T = matrix(c(T, t))
P = matrix(c(P, p))
C = matrix(c(C, c))
W = matrix(c(W, w))
Tortho = matrix(c(Tortho, tortho))
Portho = matrix(c(Portho, portho))
Wortho = matrix(c(Wortho, wortho))
Cortho = matrix(c(Cortho, cortho))
}
T = matrix(T, ncol = nf)
T = scale(T, scale = FALSE, center = TRUE)
P = matrix(P, ncol = nf)
C = matrix(C, ncol = nf)
W = matrix(W, ncol = nf)
Tortho = matrix(Tortho, ncol = nf)
Portho = matrix(Portho, ncol = nf)
Cortho = matrix(Cortho, ncol = nf)
Wortho = matrix(Wortho, ncol = nf)
Xortho = Tortho %*% t(Portho)
max.pc1 = 1.3 * (max(abs(T[, nf])))
max.pc2 = 1.3 * (max(abs(Tortho[, nf])))
lim = c()
if (max.pc1 > max.pc2) {
lim = c(-max.pc1, max.pc1)
}else{
lim = c(-max.pc2, max.pc2)
}
#tutticolors = matrix(c("darkgreen","blue","magenta","darkorange3", 5, 6, "rosybrown4",
# "green4", "navy", "purple2", "orange", "pink", "chocolate2",
# "coral3", "khaki3", "thistle", "turquoise3", "palegreen1",
# "moccasin", "olivedrab3", "azure4", "gold3", "deeppink",7, 8),
# ncol = 1)
tutticolors = as.matrix(colors2)
col = c()
for (i in 1:nrow(k)) {
col = c(col, tutticolors[k[i, ], ])
}
plot(T[, nf], Tortho[, 1], col = col, pch = marks2 , cex=2, xlim = lim,
ylim = lim, xlab = "T score [1]", ylab = "Orthogonal T score [1]",
main = "OPLS-DA score scatter plot")
text(T[, nf], Tortho[, 1], col = col, labels = rownames(sorted),
cex = 0.5, pos = 1)
axis(1, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
axis(2, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
legend("topleft",legend = groupnames2, pch = unique(marks2), col=color,cex=1)
dataEllipse(T[, nf], Tortho[, 1], levels = c(0.95), add = TRUE,
col = "black", lwd = 0.4, plot.points = FALSE, center.cex = 0.2)
dirout = paste(getwd(), "/OPLS-DA", scaling, " - ",noteOP,"/", sep = "")
dir.create(dirout)
pwdxdef = paste(dirout, "X_deflated.csv", sep = "")
write.csv(X, pwdxdef)
scor = paste(dirout, "ScorePlot_OPLS-DA_", scaling, " - ",noteOP, ".pdf",
sep = "")
dev.copy2pdf(file = scor)
pwdT = paste(dirout, "TScore_Matrix.csv", sep = "")
write.csv(T, pwdT)
pwdTortho = paste(dirout, "TorthoScore_Matrix.csv", sep = "")
write.csv(T, pwdTortho)
s = as.matrix(sorted[, -1], ncol = ncol(sorted) - 1)
p1 = c()
for (i in 1:ncol(s)) {
scov = cov(s[, i], T)
p1 = matrix(c(p1, scov), ncol = 1)
}
pcorr1 = c()
for (i in 1:nrow(p1)) {
den = apply(T, 2, sd) * sd(s[, i])
corr1 = p1[i, ]/den
pcorr1 = matrix(c(pcorr1, corr1), ncol = 1)
}
pwdp1 = paste(dirout, "p1_Matrix.csv", sep = "")
write.csv(p1, pwdp1)
pwdpcorr1 = paste(dirout, "pcorr1_Matrix.csv", sep = "")
write.csv(pcorr1, pwdpcorr1)
MSn=cbind(p1,abs(p1),pcorr1,abs(pcorr1))
rownames(MSn)=colnames(x.x) # shoud be same as dataSetM2
colnames(MSn)=c("p1","abs_p1","p1_corr","abs_p1_corr")
pwdpMSn = paste(dirout, "S-plot_list of mz - ",noteOP,".csv", sep = "")
write.table(MSn,pwdpMSn,sep=";")
#top:
axx=axx
top=top
sorMSnt=NULL
if(axx=="p1"){sorMSnt=MSn[order(-MSn[,2]),]}
if(axx=="p1corr"){sorMSnt=MSn[order(-MSn[,4]),]}
sortMSnt=sorMSnt[1:top,]
pwdpMSnsortt = paste(dirout, "S-plot_list of mz_top - ",noteOP,".csv", sep = "")
write.table(sortMSnt,pwdpMSnsortt,sep=";")
p1t=sortMSnt[,1]
pcorr1t=sortMSnt[,3]
#quantile:
qu=qu
MSnq=NULL
sorMSnq=NULL
if(axx=="p1"){MSnq=quantile(MSn[,2],qu)
sorMSnq=which(sorMSnt[,2]>=MSnq)}
if(axx=="p1corr"){MSnq=quantile(MSn[,4],qu)
sorMSnq=which(sorMSnt[,4]>=MSnq)}
sortMSnq=sorMSnt[sorMSnq,]
pwdpMSnsortq = paste(dirout, "S-plot_list of mz_quantile - ",noteOP,".csv", sep = "")
write.table(sortMSnq,pwdpMSnsortq,sep=";")
p1q=sortMSnq[,1]
pcorr1q=sortMSnq[,3]
#dev.new()
plot(p1, pcorr1, xlab = "p[1]", ylab = "p(corr)[1]", main = paste("S-plot (OPLS-DA) ",
scaling, sep = ""), pch=16, cex=0.5)
text(p1, pcorr1, labels = colnames(s), cex = 0.5, pos = 1, offset=0.2, col="navy")
splot = paste(dirout, "SPlot_OPLS-DA_", scaling," - ",noteOP, ".pdf",
sep = "")
dev.copy2pdf(file = splot,width=15, height=10)
#barevny S_plot - top:
#dev.new()
plot(p1, pcorr1, xlab = "p[1]", ylab = "p(corr)[1]", main = paste("S-plot (OPLS-DA) ",scaling, sep = ""), pch=16, cex=0.5)
points(p1t,pcorr1t,col="red",pch=16,cex=0.6)
legend("topleft",legend = paste("n=", top ),cex=1)
text(p1, pcorr1, labels = colnames(s), cex = 0.5, pos = 1, offset=0.2, col="navy")
text(p1t, pcorr1t, labels = rownames(sortMSnt), cex = 0.5, pos = 1, offset=0.2, col="red")
splotsortt = paste(dirout, "SPlot_OPLS-DA_top_", scaling," - ",noteOP, ".pdf",
sep = "")
dev.copy2pdf(file = splotsortt,width=15, height=10)
#barevny S_plot - quantile:
#dev.new()
plot(p1, pcorr1, xlab = "p[1]", ylab = "p(corr)[1]", main = paste("S-plot (OPLS-DA) ",scaling, sep = ""), pch=16, cex=0.5)
points(p1q,pcorr1q,col="red",pch=16,cex=0.6)
legend("topleft",legend = c(paste("n=", length(rownames(sortMSnq))),paste(qu*100,"% quantile")),cex=1)
text(p1, pcorr1, labels = colnames(s), cex = 0.5, pos = 1, offset=0.2, col="navy")
text(p1q, pcorr1q, labels = rownames(sortMSnq), cex = 0.5, pos = 1, offset=0.2, col="red")
splotsortq = paste(dirout, "SPlot_OPLS-DA_quantile_", scaling," - ",noteOP, ".pdf",
sep = "")
dev.copy2pdf(file = splotsortq,width=15, height=10)
pc.all <- prcomp(X, center = FALSE, scale = FALSE)
p.v <- matrix(((pc.all$sdev^2)/(sum(pc.all$sdev^2))), ncol = 1)
p.i <- round(p.v * 100, 1)
p.z <- matrix(1, nrow(p.i), 1)
p.f <- cbind(p.i, p.z)
dirout.pca = paste(dirout, "PCA_OPLS/", sep = "")
dir.create(dirout.pca)
write.csv(p.f, paste(dirout.pca, "PCA_P_OPLS", sep = ""))
write.csv(pc.all$x, paste(dirout.pca, "PCA_OPLS_ScoreMatrix.csv",
sep = ""))
write.csv(pc.all$rotation, paste(dirout.pca, "PCA_OPLS_LoadingsMatrix.csv",
sep = ""))
cum = p.f[1, 1] + p.f[2, 1]
lim = c()
max.pc1 = 1.3 * (max(abs(pc.all$x[, 1])))
max.pc2 = 1.3 * (max(abs(pc.all$x[, 2])))
if (max.pc1 > max.pc2) {
lim = c(-max.pc1, max.pc1)
}
else {
lim = c(-max.pc2, max.pc2)
}
pca <- paste("PC1 (", p.f[1, 1], ") %")
pcb <- paste("PC2 (", p.f[2, 1], ") %")
xlab = c(pca)
ylab = c(pcb)
D <- paste(dirout.pca, "PCA_OPLS_ScorePlot.pdf", sep = "")
pdf(D)
plot(pc.all$x[, 1], pc.all$x[, 2], col = col, xlab = xlab,
ylab = ylab, xlim = lim, ylim = lim, pch = marks2, cex=2, sub = paste("Cumulative Proportion of Variance Explained = ",
cum, "%", sep = ""), main = "PCA Score Plot on orthogonal-deflated X")
axis(1, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
axis(2, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
legend("topleft",legend = groupnames2, pch = unique(marks2), col = unique(col),cex=1)
dataEllipse(pc.all$x[, 1], pc.all$x[, 2], levels = c(0.95),
add = TRUE, col = "black", lwd = 0.4, plot.points = FALSE,
center.cex = 0.2)
text(pc.all$x[, 1], pc.all$x[, 2], col = col, cex = 0.5,
labels = rownames(sorted), pos = 1)
dev.off()
pca.load <- paste("Loading PC1 (", p.f[1, 1], ") %")
pcb.load <- paste("Loading PC2 (", p.f[2, 1], ") %")
Max.pc1 = 1.1 * (max(pc.all$rotation[, 1]))
Min.pc1 = 1.1 * (min(pc.all$rotation[, 1]))
Mpc1 = c(Min.pc1 * 2, Max.pc1 * 2)
Max.pc2 = 1.1 * (max(pc.all$rotation[, 2]))
Min.pc2 = 1.1 * (min(pc.all$rotation[, 2]))
Mpc2 = c(Min.pc2 * 2, Max.pc2 * 2)
E = paste(dirout.pca, "PCA_OPLS_LoadingPlot.pdf", sep = "")
pdf(E)
plot(pc.all$rotation[, 1], pc.all$rotation[, 2], xlab = pca.load,
ylab = pcb.load, xlim = c(Min.pc1, Max.pc1), ylim = c(Min.pc2,
Max.pc2), main = "PCA Loading Plot on orthogonal-deflated X",
sub = paste("Cumulative Proportion of Variance Explained = ",
cum, "%", sep = ""))
axis(1, at = Mpc1, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
axis(2, at = Mpc2, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
text(pc.all$rotation[, 1], pc.all$rotation[, 2], labels = rownames(pc.all$rotation),
cex = 0.6, pos = 1)
dev.off()
}
for (i in 1:(count-1)){
for (j in (i+1):count){
OP=c(rep(1,Group[i,3]),rep(2,Group[j,3]))
dataOP=cbind(OP,dataSetM2[c((Group[i,1]:Group[i,2]),(Group[j,1]:Group[j,2])),])
colors2=color[c(Group[i,1],Group[j,1])]
marks2=marks[c((Group[i,1]:Group[i,2]),(Group[j,1]:Group[j,2]))]
groupnames2=groupnames[c(i,j)]
r.names=row.names(dataOP)
dataOP=data.frame(cbind(r.names,dataOP))
row.names(dataOP)=NULL
noteOP=paste(name,"_",groupnames[i],"_",groupnames[j],sep="")
explore.data2(dataOP, scaling="p",scal=TRUE, normalize = FALSE, imputation = FALSE,color=colors2)
OPLSDA2("p",axx=axx,top=top,qu=qu,color=colors2,marks2,groupnames2)
#assign(paste("OP",i,j,sep=""),get("OP"), inherits=T)
#assign(paste("dataOP",i,j,sep=""),get("dataOP"), inherits=T)
remove(OP)
remove(dataOP)
remove(noteOP)
remove(r.names)
dev.off()
}
}
setwd(dirout)
}
AlzbetaG/Metabol documentation built on May 31, 2019, 12:39 a.m.
R Package Documentation
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#' Orthogonal partial least squares - discriminant analysis (OPLS-DA)
#'
#' Makes orthogonal partial least squares - discriminant analysis (OPLS-DA), displays score plots and s-plots.
#' @param data Data table with variables (metabolites) in columns. Samples in rows are sorted according to specific groups.
#' @param name A character string or expression indicating a name of data set. It occurs in names of every output.
#' @param groupnames A character vector defining specific groups in data. Every string must be specific for each group and they must not overlap.
#' @param tsf Data transformation must be defined by "clr" (default), "log", "log10", "PQN", "lnPQN", "pareto" or "none". See Details.
#' @param axx Which axis of S-plot is used to choose most important variables: p1 or p1corr (default).
#' @param top How many most important variables (in absolute values) should be highlighted in s-plot? The default is 30.
#' @param qu Which quantile of the important variables (in absolute values) should be highlighted in s-plot? The default is 0.75.
#' @param QCs logical. If FALSE (default) quality control samples (QCs) are automatically distinguished and skipped.
#' @details Data transformation: with "clr" clr trasformation is used (see References), with "log" natural logarithm is used, with "log10" decadic logarithm is used, with "pareto" data are only scaled by Pareto scaling, with "PQN" probabilistic quotient normalization is done, with "lnPQN" natural logarithm of PQN transformed data is done, with "none" no tranformation is done.
#' @details S-plots can be used only for comparison of two groups. If there is more groups in data, all possible combinations of pairs are evaluated.
#' @details Up to twenty different groups can be distinguished in data (including QCs).
#' @return Score plot and s-plots of OPLS-DA.
#' @return Excel file with s-plot summary for every variable.
#' @import car
#' @import rrcov
#' @import utils
#' @importFrom stats median prcomp pf cov
#' @importFrom robCompositions cenLR
#' @references Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman & Hall Ltd., London (UK). p. 416.
#' @references Gaude, E.et al. (2012) muma: Metabolomics Univariate and Multivariate Analysis. R package version 1.4. \url{https://CRAN.R-project.org/package=muma}
#' @export
GraphsOPLSDA=function(data,name,groupnames,tsf="clr",axx="p1corr",top=30,qu=0.75,QCs=FALSE){
dirout=getwd()
##########################################################################################################################
if (tsf=="clr"){
dataM=cenLR(data)$x.clr
}
if (tsf=="log"){
dataM=log(data)
}
if (tsf=="log10"){
dataM=log10(data)
}
if (tsf=="pareto"){
dataM=scale(data, scale=TRUE, center=FALSE)
}
PQN1=function (x){
xref=apply(x,2,median)
podil=x
for (i in 1:ncol(x)){
podil[,i]=x[,i]/xref[i]
}
s=apply(podil,1,median)
PQN=x
for (j in 1:nrow(x)){
PQN[j,]=x[j,]/s[j]
}
return(PQN)
}
if (tsf=="PQN"){
dataM=PQN1(data)
}
if (tsf=="lnPQN"){
dataM=log(PQN1(data))
}
if (tsf=="none"){
dataM=data
}
#################################################################################################
count=length(groupnames)
basecolor=c("blue","magenta","forestgreen","darkorange","deepskyblue","mediumaquamarine","lightslateblue","saddlebrown",
"gray40","darkslateblue","firebrick","darkcyan","darkmagenta", "deeppink1","limegreen","gold2","bisque2",
"lightcyan3","red","darkolivegreen3") # Basic colours from: http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
basemarks=c(15,17,18,8,11,2,0,16,5,6,4,10,3,7,9,12)
Group=matrix(rep(NA,count*3),ncol=3)
colnames(Group)=c("min","max","length")
rownames(Group)=groupnames
groups=NULL
marks=NULL
color=NULL
for (i in 1:count){
Gr=grep(groupnames[i],rownames(dataM))
Group[i,1]=min(Gr)
Group[i,2]=max(Gr)
Group[i,3]=length(Gr)
gr=rep(i,length(Gr))
groups=c(groups,gr)
zn=rep(basemarks[i],length(Gr))
marks=c(marks,zn)
cl=rep(basecolor[i],length(Gr))
color=c(color,cl)
}
##########################################################################################################################
if (QCs==FALSE){
QC=grep("QC",rownames(dataM))
if (length(QC)!=0){
dataM=dataM[-QC,]
color=color[-QC]
marks=marks[-QC]
groups=groups[-QC]
groupnames=groupnames[unique(groups)]
count=length(groupnames)
Group=matrix(rep(NA,count*3),ncol=3)
colnames(Group)=c("min","max","length")
rownames(Group)=groupnames
for (i in 1:count){
Gr=grep(groupnames[i],rownames(dataM))
Group[i,1]=min(Gr)
Group[i,2]=max(Gr)
Group[i,3]=length(Gr)
}
}
}
##########################################################################################################################
dataSetM2=dataM
colors=matrix(unique(color),ncol=1)
#################################################################################################
dirout2 = paste(dirout,"/","OPLSDA",sep = "")
dir.create(dirout2, recursive=TRUE, showWarnings = FALSE) # vytvori v zadane ceste slozku s nazvem "note"
setwd(dirout2)
#################################################################################################
explore.data2=function (file, scaling, scal = TRUE, normalize = FALSE, imputation = FALSE,
imput,color=colors2)
{
#comp = read.csv("OPLSDA_Data.csv", sep = ";", header = TRUE)
comp <- file
comp.x = comp[, 3:ncol(comp)]
comp.x = cbind(comp[, 2], comp[, 1], comp.x)
x <- comp.x
x.x <- x[, 3:ncol(x)]
rownames(x.x) <- x[, 2]
if (!scal) {
scaling = ""
}
dirout = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/", sep = "")
dir.create(dirout)
if (imputation) {
y = x.x
r = is.na(y)
for (k in 1:ncol(r)) {
vec = matrix(r[, k], ncol = 1)
who.miss.rows = which(apply(vec, 1, function(i) {
any(i)
}))
if (length(who.miss.rows) > nrow(y) * 0.8) {
warning(paste("The variable -", colnames(y)[k],
"- has a number of missing values > 80%, therefore has been eliminated",
sep = " "))
y = y[, -k]
}
}
r = is.na(y)
who.miss.columns = c()
for (i in 1:nrow(y)) {
for (j in 1:ncol(y)) {
if (r[i, j] == TRUE) {
if (imput == "mean") {
v2 = matrix(r[, j], ncol = 1)
who.miss.rows = which(apply(v2, 1, function(i) {
any(i)
}))
y[i, j] = mean(y[-who.miss.rows, j])
print(paste("Imputing missing value of variable -",
colnames(y)[j], "- for the observation -",
rownames(y)[i], "- with", imput, "value",
sep = " "))
}
else if (imput == "minimum") {
v2 = matrix(r[, j], ncol = 1)
who.miss.rows = which(apply(v2, 1, function(i) {
any(i)
}))
y[i, j] = min(y[-who.miss.rows, j])
print(paste("Imputing missing value of variable -",
colnames(y)[j], "- for the observation -",
rownames(y)[i], "- with", imput, "value",
sep = " "))
}
else if (imput == "half.minimum") {
v2 = matrix(r[, j], ncol = 1)
who.miss.rows = which(apply(v2, 1, function(i) {
any(i)
}))
y[i, j] = min(y[-who.miss.rows, j])/2
print(paste("Imputing missing value of variable -",
colnames(y)[j], "- for the observation -",
rownames(y)[i], "- with", imput, "value",
sep = " "))
}
else if (imput == "zero") {
v2 = matrix(r[, j], ncol = 1)
who.miss.rows = which(apply(v2, 1, function(i) {
any(i)
}))
y[i, j] = 0
print(paste("Imputing missing value of variable -",
colnames(y)[j], "- for the observation -",
rownames(y)[i], "- with", imput, "value",
sep = " "))
}
}
}
}
pwdi = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ImputedMatrix.csv", sep = "")
write.csv(y, pwdi)
x.x = y
}
#for (i in 1:nrow(x.x)) {
# for (j in 1:ncol(x.x)) {
# if (x.x[i, j] <= 0) {
# x.x[i, j] = runif(1, 0, 1e-10)
# }
# }
#}
x.x = cbind(comp[, 2], x.x)
write.csv(x.x, paste(dirout, "CorrectedTable.csv", sep = ""))
pwd.c = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP, "/CorrectedTable.csv",
sep = "")
x <- read.csv(pwd.c, sep = ",", header = TRUE)
x.x <- x[, 3:ncol(x)]
rownames(x.x) <- x[, 1]
k = matrix(x[, 2], ncol = 1)
if (normalize) {
x.t <- t(x.x)
x.s <- matrix(colSums(x.t), nrow = 1)
uni = matrix(rep(1, nrow(x.t)), ncol = 1)
area.uni <- uni %*% x.s
x.areanorm <- x.t/area.uni
x.areanorm = t(x.areanorm)
write.csv(x.areanorm, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
else {
write.csv(x.x, paste(dirout, "/ProcessedTable.csv", sep = ""))
}
if (scal) {
if (scaling == "Pareto" | scaling == "pareto" | scaling ==
"P" | scaling == "p") {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.areanorm.tc <- scale(x.x, center = TRUE, scale = FALSE)
all.sd <- matrix(apply(x.areanorm.tc, 2, sd), nrow = 1)
uni.exp.all = matrix(rep(1, nrow(x.areanorm.tc)),
ncol = 1)
all.sdm = uni.exp.all %*% all.sd
all.sqsd = sqrt(all.sdm)
all.pareto <- x.areanorm.tc/all.sqsd
write.csv(all.pareto, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
else if (scaling == "Auto" | scaling == "auto" | scaling ==
"A" | scaling == "a") {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.areanorm.tc <- scale(x.x, center = TRUE, scale = FALSE)
all.sd <- matrix(apply(x.areanorm.tc, 2, sd), nrow = 1)
uni.exp.all = matrix(rep(1, nrow(x.areanorm.tc)),
ncol = 1)
all.sdm = uni.exp.all %*% all.sd
all.auto <- x.areanorm.tc/all.sdm
write.csv(all.auto, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
else if (scaling == "Vast" | scaling == "vast" | scaling ==
"V" | scaling == "v") {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.areanorm.tc <- scale(x.x, center = TRUE, scale = FALSE)
all.sd <- matrix(apply(x.areanorm.tc, 2, sd), nrow = 1)
uni.exp.all = matrix(rep(1, nrow(x.areanorm.tc)),
ncol = 1)
all.sdm = uni.exp.all %*% all.sd
sdm2 = all.sdm^2
colm = matrix(colMeans(x.x), nrow = 1)
colm.m = uni.exp.all %*% colm
num = x.areanorm.tc * colm.m
vast = num/sdm2
write.csv(vast, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
else if (scaling == "Range" | scaling == "range" | scaling ==
"R" | scaling == "r") {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.areanorm.tc <- scale(x.x, center = TRUE, scale = FALSE)
range = c()
for (i in 1:ncol(x.x)) {
den = c()
den = max(x.x[, i]) - min(x.x[, i])
range = matrix(c(range, den), nrow = 1)
}
uni.exp.all = matrix(rep(1, nrow(x.areanorm.tc)),
ncol = 1)
range.m = uni.exp.all %*% range
all.range = x.areanorm.tc/range.m
write.csv(all.range, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
else if (scaling == "Median" | scaling == "median" |
scaling == "M" | scaling == "m") {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.areanorm.tc <- scale(x.x, center = TRUE, scale = FALSE)
all.med <- matrix(apply(x.areanorm.tc, 2, median),
nrow = 1)
uni.exp.all = matrix(rep(1, nrow(x.areanorm.tc)),
ncol = 1)
all.sdm = uni.exp.all %*% all.med
all.med <- x.areanorm.tc/all.sdm
write.csv(all.med, paste(dirout, "/ProcessedTable.csv",
sep = ""))
}
}
else {
pwd.n = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.n, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
x.c = scale(x.x, scale = FALSE)
write.csv(x.c, paste(dirout, "/ProcessedTable.csv", sep = ""))
}
pwd.scal = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,
"/ProcessedTable.csv", sep = "")
x <- read.csv(pwd.scal, sep = ",", header = TRUE)
x.x <- x[, 2:ncol(x)]
rownames(x.x) <- x[, 1]
pc.all <- prcomp(x.x, center = FALSE, scale = FALSE)
p.v <- matrix(((pc.all$sdev^2)/(sum(pc.all$sdev^2))), ncol = 1)
p.i <- round(p.v * 100, 1)
p.z <- matrix(1, nrow(p.i), 1)
p.f <- cbind(p.i, p.z)
dirout.pca = paste(getwd(), "/PCA_Data_", scaling, noteOP,"/", sep = "")
dir.create(dirout.pca)
write.csv(p.f, paste(dirout.pca, "PCA_P", sep = ""))
write.csv(pc.all$x, paste(dirout.pca, "PCA_ScoreMatrix.csv",
sep = ""))
write.csv(pc.all$rotation, paste(dirout.pca, "PCA_LoadingsMatrix.csv",
sep = ""))
pwd.score = paste(getwd(), "/PCA_Data_", scaling, noteOP, "/", "PCA_ScoreMatrix.csv",
sep = "")
Score <- read.csv(pwd.score, sep = ",", header = TRUE)
Score.x <- Score[, 2:ncol(Score)]
rownames(Score.x) <- Score[, 1]
pwd.load = paste(getwd(), "/PCA_Data_", scaling, noteOP,"/", "PCA_LoadingsMatrix.csv",
sep = "")
Loading <- read.csv(pwd.load, sep = ",", header = TRUE)
Loading.x <- Loading[, 2:ncol(Loading)]
rownames(Loading.x) <- Loading[, 1]
pwd.pvar = paste(getwd(), "/PCA_Data_", scaling, noteOP,"/", "PCA_P",
sep = "")
Pvar <- read.csv(pwd.pvar, sep = ",", header = TRUE)
Pvar.x <- Pvar[, 2:ncol(Pvar)]
rownames(Pvar.x) <- Pvar[, 1]
barplot(Pvar.x[, 1], xlab = "Principal Components", ylab = "Proportion of Variance explained",
main = "Screeplot", ylim = c(0, 100))
scree = paste(dirout.pca, "Screeplot", scaling, noteOP, ".pdf", sep = "")
dev.copy2pdf(file = scree)
#tutticolors = matrix(c(1, 2, 3, 4, 5, 6, "rosybrown4",
# "green4", "navy", "purple2", "orange", "pink", "chocolate2",
# "coral3", "khaki3", "thistle", "turquoise3", "palegreen1",
# "moccasin", "olivedrab3", "azure4", "gold3", "deeppink",7, 8),
# ncol = 1)
tutticolors = as.matrix(colors2)
col = c()
for (i in 1:nrow(k)) {
col = c(col, tutticolors[k[i, ], ])
}
pairs = c()
if (ncol(Score.x) >= 10) {
pairs = c(10)
}
else {
pairs = c(ncol(Score.x))
}
pairs(Score.x[, 1:pairs], col = col)
#dev.new()
pairs = paste(dirout.pca, "First_10_Components_", scaling, noteOP,
".pdf", sep = "")
dev.copy2pdf(file = pairs)
K = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP,"/class.csv",
sep = "")
write.csv(k, K)
x.nn = cbind(k, pc.all$x)
sorted = x.nn[order(x.nn[, 1]), ]
g = c()
for (i in 1:nrow(sorted)) {
if (any(g == sorted[i, 1])) {
g = g
}
else {
g = matrix(c(g, sorted[i, 1]), ncol = 1)
}
}
dirout.g = paste(getwd(), "/Groups_",noteOP, sep = "")
dir.create(dirout.g)
for (i in 1:nrow(g)) {
vuota <- c()
fin = matrix(rep(NA, ncol(sorted)), nrow = 1)
for (j in 1:nrow(sorted)) {
if (sorted[j, 1] == i) {
vuota <- matrix(sorted[j, ], nrow = 1)
rownames(vuota) = rownames(sorted)[j]
fin = rbind(fin, vuota)
}
}
nam = paste("r", i, sep = ".")
n = matrix(fin[-1, ], ncol = ncol(sorted))
n.x = matrix(n[, -1], ncol = ncol(sorted) - 1)
name = as.matrix(assign(nam, n.x))
outputfileg = paste("r.", i, ".csv", sep = "")
write.csv(name, paste(dirout.g, outputfileg, sep = "/"),
row.names = FALSE)
}
all.F = c()
NoF = nrow(g)
for (i in 1:NoF) {
for (j in 1:NoF) {
if (i < j) {
ni = paste("r.", i, ".csv", sep = "")
nj = paste("r.", j, ".csv", sep = "")
pwdi = paste(getwd(), "/Groups_",noteOP,"/", ni, sep = "")
pwdj = paste(getwd(), "/Groups_",noteOP,"/", nj, sep = "")
I = read.csv(pwdi, header = TRUE)
J = read.csv(pwdj, header = TRUE)
fin = ncol(I) - 1
ntest = factorial(fin)/(2 * (factorial(fin -
2)))
T2 = c()
nam = c()
for (k in 1:fin) {
for (l in 1:fin) {
if (k < l) {
Ikl = cbind(I[, k], I[, l])
Jkl = cbind(J[, k], J[, l])
t1 = matrix(T2.test(Ikl, Jkl)$statistic,
ncol = 2)
t2 = c(t1[, 1])
T2 = matrix(c(T2, t2), ncol = 1)
rownam = paste("PC", k, "vsPC", l, sep = "")
nam = matrix(c(nam, rownam), ncol = 1)
}
}
}
pair = paste("T2statistic_", i, "vs", j, sep = "")
rownames(T2) = nam
colnames(T2)[1] = pair
num = nrow(I) + nrow(J) - 3
den = 2 * (nrow(I) + nrow(J) - 2)
coeff = num/den
Fval = T2 * coeff
Fvalname = paste("F_statistic_", i, "vs", j,
sep = "")
colnames(Fval)[1] = Fvalname
Fpval = pf(Fval, 2, num)
Fname = paste("F_pvalue_", i, "vs", j, sep = "")
colnames(Fpval)[1] = Fname
Fpvalfin = 1 - Fpval
all.F = matrix(c(all.F, Fpvalfin))
}
}
}
varp = c()
for (k in 1:fin) {
for (l in 1:fin) {
if (k < l) {
varp = matrix(c(varp, p.f[k, 1] + p.f[l, 1]),
ncol = 1)
}
}
}
ncomparison = factorial(nrow(g))/(2 * (factorial(nrow(g) -
2)))
all.F = matrix(all.F, ncol = ncomparison)
rownames(all.F) = nam
allFpwd = paste(getwd(), "/PCA_Data_", scaling, noteOP, "/PCs_Fstatistic.csv",
sep = "")
write.csv(all.F, allFpwd, row.names = FALSE)
sum = matrix(rowSums(all.F), ncol = 1)
all = data.frame(nam, sum, varp)
colnames(all)[3] = "Variance(%)"
colnames(all)[2] = "Sum_p_values"
colnames(all)[1] = "Pair_of_PCs"
ord.sum = all[order(all[, 2]), ]
colnames(ord.sum)[3] = "Variance(%)"
colnames(ord.sum)[2] = "Sum_p_values(F_statistics)"
colnames(ord.sum)[1] = "Pair_of_PCs"
rownames(ord.sum) = 1:nrow(ord.sum)
rankFpwd = paste(getwd(), "/PCA_Data_", scaling, noteOP, "/PCs_ranked_Fpvalue.csv",
sep = "")
write.csv(ord.sum, rankFpwd, row.names = FALSE)
print("Pairs of Principal Components giving highest statistical cluster separation are:")
print(ord.sum[1:5, ])
}
##############################################################################################################################################
OPLSDA2=function (scaling,axx="",top="",qu="",color=colors2,marks,groupnames)
{
pwd.x = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP, "/ProcessedTable.csv",
sep = "")
x = read.csv(pwd.x, header = TRUE)
x.x = x[, 2:ncol(x)]
rownames(x.x) = x[, 1]
pwdK = paste(getwd(), "/Preprocessing_Data_", scaling, noteOP, "/class.csv",
sep = "")
k = read.csv(pwdK, header = TRUE)
k.x = matrix(k[, -1], ncol = 1)
x.n = cbind(k.x, x.x)
sorted = x.n[order(x.n[, 1]), ]
k = matrix(sorted[, 1], ncol = 1)
g = c()
for (i in 1:nrow(sorted)) {
if (any(g == sorted[i, 1])) {
g = g
}
else {
g = matrix(c(g, sorted[i, 1]), ncol = 1)
}
}
Y = matrix(rep(NA, nrow(sorted)), ncol = 1)
for (i in 1:nrow(sorted)) {
for (l in 1:2) {
if (sorted[i, 1] == l) {
Y[i, ] = 0
}
else {
Y[i, ] = 1
}
}
}
X = as.matrix(sorted[, -1], ncol = ncol(sorted) - 1)
nf = 1
T = c()
P = c()
C = c()
W = c()
Tortho = c()
Portho = c()
Wortho = c()
Cortho = c()
for (j in 1:nf) {
w = (t(X) %*% Y) %*% solve(t(Y) %*% Y)
w1 = t(w) %*% w
w2 = abs(sqrt(w1))
w = w %*% solve(w2)
t = (X %*% w) %*% solve(t(w) %*% w)
t1 = t(t) %*% t
c = t(Y) %*% t %*% solve(t1)
c1 = t(c) %*% c
u = Y %*% c %*% solve(c1)
u1 = t(u) %*% u
u2 = abs(sqrt(u1))
p = (t(X) %*% t) %*% solve(t1)
wortho = p - w
wortho1 = t(wortho) %*% wortho
wortho2 = abs(sqrt(abs(wortho1)))
wortho = wortho %*% solve(wortho2)
tortho = X %*% wortho %*% solve(t(wortho) %*% wortho)
tortho1 = t(tortho) %*% tortho
portho = t(X) %*% tortho %*% solve(tortho1)
cortho = t(Y) %*% tortho %*% solve(tortho1)
X = X - tortho %*% t(portho)
T = matrix(c(T, t))
P = matrix(c(P, p))
C = matrix(c(C, c))
W = matrix(c(W, w))
Tortho = matrix(c(Tortho, tortho))
Portho = matrix(c(Portho, portho))
Wortho = matrix(c(Wortho, wortho))
Cortho = matrix(c(Cortho, cortho))
}
T = matrix(T, ncol = nf)
T = scale(T, scale = FALSE, center = TRUE)
P = matrix(P, ncol = nf)
C = matrix(C, ncol = nf)
W = matrix(W, ncol = nf)
Tortho = matrix(Tortho, ncol = nf)
Portho = matrix(Portho, ncol = nf)
Cortho = matrix(Cortho, ncol = nf)
Wortho = matrix(Wortho, ncol = nf)
Xortho = Tortho %*% t(Portho)
max.pc1 = 1.3 * (max(abs(T[, nf])))
max.pc2 = 1.3 * (max(abs(Tortho[, nf])))
lim = c()
if (max.pc1 > max.pc2) {
lim = c(-max.pc1, max.pc1)
}else{
lim = c(-max.pc2, max.pc2)
}
#tutticolors = matrix(c("darkgreen","blue","magenta","darkorange3", 5, 6, "rosybrown4",
# "green4", "navy", "purple2", "orange", "pink", "chocolate2",
# "coral3", "khaki3", "thistle", "turquoise3", "palegreen1",
# "moccasin", "olivedrab3", "azure4", "gold3", "deeppink",7, 8),
# ncol = 1)
tutticolors = as.matrix(colors2)
col = c()
for (i in 1:nrow(k)) {
col = c(col, tutticolors[k[i, ], ])
}
plot(T[, nf], Tortho[, 1], col = col, pch = marks2 , cex=2, xlim = lim,
ylim = lim, xlab = "T score [1]", ylab = "Orthogonal T score [1]",
main = "OPLS-DA score scatter plot")
text(T[, nf], Tortho[, 1], col = col, labels = rownames(sorted),
cex = 0.5, pos = 1)
axis(1, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
axis(2, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
legend("topleft",legend = groupnames2, pch = unique(marks2), col=color,cex=1)
dataEllipse(T[, nf], Tortho[, 1], levels = c(0.95), add = TRUE,
col = "black", lwd = 0.4, plot.points = FALSE, center.cex = 0.2)
dirout = paste(getwd(), "/OPLS-DA", scaling, " - ",noteOP,"/", sep = "")
dir.create(dirout)
pwdxdef = paste(dirout, "X_deflated.csv", sep = "")
write.csv(X, pwdxdef)
scor = paste(dirout, "ScorePlot_OPLS-DA_", scaling, " - ",noteOP, ".pdf",
sep = "")
dev.copy2pdf(file = scor)
pwdT = paste(dirout, "TScore_Matrix.csv", sep = "")
write.csv(T, pwdT)
pwdTortho = paste(dirout, "TorthoScore_Matrix.csv", sep = "")
write.csv(T, pwdTortho)
s = as.matrix(sorted[, -1], ncol = ncol(sorted) - 1)
p1 = c()
for (i in 1:ncol(s)) {
scov = cov(s[, i], T)
p1 = matrix(c(p1, scov), ncol = 1)
}
pcorr1 = c()
for (i in 1:nrow(p1)) {
den = apply(T, 2, sd) * sd(s[, i])
corr1 = p1[i, ]/den
pcorr1 = matrix(c(pcorr1, corr1), ncol = 1)
}
pwdp1 = paste(dirout, "p1_Matrix.csv", sep = "")
write.csv(p1, pwdp1)
pwdpcorr1 = paste(dirout, "pcorr1_Matrix.csv", sep = "")
write.csv(pcorr1, pwdpcorr1)
MSn=cbind(p1,abs(p1),pcorr1,abs(pcorr1))
rownames(MSn)=colnames(x.x) # shoud be same as dataSetM2
colnames(MSn)=c("p1","abs_p1","p1_corr","abs_p1_corr")
pwdpMSn = paste(dirout, "S-plot_list of mz - ",noteOP,".csv", sep = "")
write.table(MSn,pwdpMSn,sep=";")
#top:
axx=axx
top=top
sorMSnt=NULL
if(axx=="p1"){sorMSnt=MSn[order(-MSn[,2]),]}
if(axx=="p1corr"){sorMSnt=MSn[order(-MSn[,4]),]}
sortMSnt=sorMSnt[1:top,]
pwdpMSnsortt = paste(dirout, "S-plot_list of mz_top - ",noteOP,".csv", sep = "")
write.table(sortMSnt,pwdpMSnsortt,sep=";")
p1t=sortMSnt[,1]
pcorr1t=sortMSnt[,3]
#quantile:
qu=qu
MSnq=NULL
sorMSnq=NULL
if(axx=="p1"){MSnq=quantile(MSn[,2],qu)
sorMSnq=which(sorMSnt[,2]>=MSnq)}
if(axx=="p1corr"){MSnq=quantile(MSn[,4],qu)
sorMSnq=which(sorMSnt[,4]>=MSnq)}
sortMSnq=sorMSnt[sorMSnq,]
pwdpMSnsortq = paste(dirout, "S-plot_list of mz_quantile - ",noteOP,".csv", sep = "")
write.table(sortMSnq,pwdpMSnsortq,sep=";")
p1q=sortMSnq[,1]
pcorr1q=sortMSnq[,3]
#dev.new()
plot(p1, pcorr1, xlab = "p[1]", ylab = "p(corr)[1]", main = paste("S-plot (OPLS-DA) ",
scaling, sep = ""), pch=16, cex=0.5)
text(p1, pcorr1, labels = colnames(s), cex = 0.5, pos = 1, offset=0.2, col="navy")
splot = paste(dirout, "SPlot_OPLS-DA_", scaling," - ",noteOP, ".pdf",
sep = "")
dev.copy2pdf(file = splot,width=15, height=10)
#barevny S_plot - top:
#dev.new()
plot(p1, pcorr1, xlab = "p[1]", ylab = "p(corr)[1]", main = paste("S-plot (OPLS-DA) ",scaling, sep = ""), pch=16, cex=0.5)
points(p1t,pcorr1t,col="red",pch=16,cex=0.6)
legend("topleft",legend = paste("n=", top ),cex=1)
text(p1, pcorr1, labels = colnames(s), cex = 0.5, pos = 1, offset=0.2, col="navy")
text(p1t, pcorr1t, labels = rownames(sortMSnt), cex = 0.5, pos = 1, offset=0.2, col="red")
splotsortt = paste(dirout, "SPlot_OPLS-DA_top_", scaling," - ",noteOP, ".pdf",
sep = "")
dev.copy2pdf(file = splotsortt,width=15, height=10)
#barevny S_plot - quantile:
#dev.new()
plot(p1, pcorr1, xlab = "p[1]", ylab = "p(corr)[1]", main = paste("S-plot (OPLS-DA) ",scaling, sep = ""), pch=16, cex=0.5)
points(p1q,pcorr1q,col="red",pch=16,cex=0.6)
legend("topleft",legend = c(paste("n=", length(rownames(sortMSnq))),paste(qu*100,"% quantile")),cex=1)
text(p1, pcorr1, labels = colnames(s), cex = 0.5, pos = 1, offset=0.2, col="navy")
text(p1q, pcorr1q, labels = rownames(sortMSnq), cex = 0.5, pos = 1, offset=0.2, col="red")
splotsortq = paste(dirout, "SPlot_OPLS-DA_quantile_", scaling," - ",noteOP, ".pdf",
sep = "")
dev.copy2pdf(file = splotsortq,width=15, height=10)
pc.all <- prcomp(X, center = FALSE, scale = FALSE)
p.v <- matrix(((pc.all$sdev^2)/(sum(pc.all$sdev^2))), ncol = 1)
p.i <- round(p.v * 100, 1)
p.z <- matrix(1, nrow(p.i), 1)
p.f <- cbind(p.i, p.z)
dirout.pca = paste(dirout, "PCA_OPLS/", sep = "")
dir.create(dirout.pca)
write.csv(p.f, paste(dirout.pca, "PCA_P_OPLS", sep = ""))
write.csv(pc.all$x, paste(dirout.pca, "PCA_OPLS_ScoreMatrix.csv",
sep = ""))
write.csv(pc.all$rotation, paste(dirout.pca, "PCA_OPLS_LoadingsMatrix.csv",
sep = ""))
cum = p.f[1, 1] + p.f[2, 1]
lim = c()
max.pc1 = 1.3 * (max(abs(pc.all$x[, 1])))
max.pc2 = 1.3 * (max(abs(pc.all$x[, 2])))
if (max.pc1 > max.pc2) {
lim = c(-max.pc1, max.pc1)
}
else {
lim = c(-max.pc2, max.pc2)
}
pca <- paste("PC1 (", p.f[1, 1], ") %")
pcb <- paste("PC2 (", p.f[2, 1], ") %")
xlab = c(pca)
ylab = c(pcb)
D <- paste(dirout.pca, "PCA_OPLS_ScorePlot.pdf", sep = "")
pdf(D)
plot(pc.all$x[, 1], pc.all$x[, 2], col = col, xlab = xlab,
ylab = ylab, xlim = lim, ylim = lim, pch = marks2, cex=2, sub = paste("Cumulative Proportion of Variance Explained = ",
cum, "%", sep = ""), main = "PCA Score Plot on orthogonal-deflated X")
axis(1, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
axis(2, at = lim * 2, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
legend("topleft",legend = groupnames2, pch = unique(marks2), col = unique(col),cex=1)
dataEllipse(pc.all$x[, 1], pc.all$x[, 2], levels = c(0.95),
add = TRUE, col = "black", lwd = 0.4, plot.points = FALSE,
center.cex = 0.2)
text(pc.all$x[, 1], pc.all$x[, 2], col = col, cex = 0.5,
labels = rownames(sorted), pos = 1)
dev.off()
pca.load <- paste("Loading PC1 (", p.f[1, 1], ") %")
pcb.load <- paste("Loading PC2 (", p.f[2, 1], ") %")
Max.pc1 = 1.1 * (max(pc.all$rotation[, 1]))
Min.pc1 = 1.1 * (min(pc.all$rotation[, 1]))
Mpc1 = c(Min.pc1 * 2, Max.pc1 * 2)
Max.pc2 = 1.1 * (max(pc.all$rotation[, 2]))
Min.pc2 = 1.1 * (min(pc.all$rotation[, 2]))
Mpc2 = c(Min.pc2 * 2, Max.pc2 * 2)
E = paste(dirout.pca, "PCA_OPLS_LoadingPlot.pdf", sep = "")
pdf(E)
plot(pc.all$rotation[, 1], pc.all$rotation[, 2], xlab = pca.load,
ylab = pcb.load, xlim = c(Min.pc1, Max.pc1), ylim = c(Min.pc2,
Max.pc2), main = "PCA Loading Plot on orthogonal-deflated X",
sub = paste("Cumulative Proportion of Variance Explained = ",
cum, "%", sep = ""))
axis(1, at = Mpc1, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
axis(2, at = Mpc2, pos = c(0, 0), labels = FALSE, col = "grey",
lwd = 0.7)
text(pc.all$rotation[, 1], pc.all$rotation[, 2], labels = rownames(pc.all$rotation),
cex = 0.6, pos = 1)
dev.off()
}
for (i in 1:(count-1)){
for (j in (i+1):count){
OP=c(rep(1,Group[i,3]),rep(2,Group[j,3]))
dataOP=cbind(OP,dataSetM2[c((Group[i,1]:Group[i,2]),(Group[j,1]:Group[j,2])),])
colors2=color[c(Group[i,1],Group[j,1])]
marks2=marks[c((Group[i,1]:Group[i,2]),(Group[j,1]:Group[j,2]))]
groupnames2=groupnames[c(i,j)]
r.names=row.names(dataOP)
dataOP=data.frame(cbind(r.names,dataOP))
row.names(dataOP)=NULL
noteOP=paste(name,"_",groupnames[i],"_",groupnames[j],sep="")
explore.data2(dataOP, scaling="p",scal=TRUE, normalize = FALSE, imputation = FALSE,color=colors2)
OPLSDA2("p",axx=axx,top=top,qu=qu,color=colors2,marks2,groupnames2)
#assign(paste("OP",i,j,sep=""),get("OP"), inherits=T)
#assign(paste("dataOP",i,j,sep=""),get("dataOP"), inherits=T)
remove(OP)
remove(dataOP)
remove(noteOP)
remove(r.names)
dev.off()
}
}
setwd(dirout)
}
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