Nothing
# Normalize test
norm_test <- function(p_value,m) {
p_value <- 0.05
norm_var <<- c()
count_norm_y <- 0
chknorm <- c()
dif <<- 0
## Shapiro normalization test
for (h in 1:dim(m)[2]) {
chknorm <- shapiro.test(m[,h])
if(is.null(chknorm$p.value)) {
print("merda")
}
if (chknorm$p.value <= p_value) {
norm_var[h] <<- "YES"
}
else{
norm_var[h] <<- "NO"
}
}
## Count variable normal
for (k in 1:length(norm_var)) {
if (norm_var[k]=='YES') {
count_norm_y <- count_norm_y + 1
}
}
dif <<- round((count_norm_y/length(norm_var))*100,2)
f <- 0
g <- 0
col_sel_norm <<- c()
col_sel_norm2 <<- c()
for (j in 1:length(norm_var)) {
if (norm_var[j] == "NO") {
f <- f + 1
col_sel_norm[f] <<- col_select[j]
}
else{
g <- g + 1
col_sel_norm2[g] <<- col_select[j]
}
}
rr_n$csranges <- CS_values_real[1,col_sel_norm]
rr_nn$csranges <- CS_values_real[1,col_sel_norm2]
if (stop_menssager != 1) {
if (dif <= 70) {
showModal(modalDialog(
title = "Warning!!!",
paste("Your data is ", dif,"% normalized, please apply the apodization
functions and do a pre-treatment before starting the STOCSY analysis or do not trust R critical "),
easyClose = FALSE,
# footer = modalButton("Close"),
footer = tagList(actionButton("pretreatment", "Normalize Selection"),
modalButton("Close")),
size = "l"
))
}
else{
showModal(modalDialog(
title = "Warning!!!",
paste("Your data is ", dif,"% normalized."),
easyClose = FALSE,
footer = modalButton("Close"),
size = "l"
))
}
}
# return(dif)
}
###############################################################################
# Calculate of R critical by test t-student table
r_critical <- function(p_value) {
if(is.null(file_names)){
}
else {
p_value <- p_value
df <- length(file_names) - 2
if(p_value != 0) {
tc <- qt(p_value,df)
R_CRITICAL <- round(as.numeric(tc/(sqrt(df+tc^2))),3)
R_CRITICAL <<- c(-abs(R_CRITICAL),abs(R_CRITICAL))
}}
}
###############################################################################
# Refresh variables
refreshval <- function() {
############# PLOT_INTERATIVO_SERVER.R RESET #############
peran_multi <- 0
chkzoom_multi <- 1
idb_multi <- 0
testy_multi <<- data.frame(Chemical_Shift=CS_values_real[1,],Spectrum=NMRData[1,])
ranges_multi$x <<- c(min(CS_values_real[1,]),(max(CS_values_real[1,])))
##############################################################################
############# SELECT_SIGNALS_SERVER.R RESET #############
# NMRData_plot <- NMRData
NMRData_Mean <- colMeans(NMRData_plot[,])
spectrums$dat <- data.frame(Chemical_Shift=CS_values_real[1,],Spectrum=(NMRData_Mean[]))
spectrums_sel$dat <- data.frame(Chemical_Shift=CS_values_real[1,],Spectrum=(NMRData_Mean[]))
CS_selection$vranges <- c(-13131313,-131313)
testy <<- data.frame(Chemical_Shift=CS_values_real[1,],Spectrum=NMRData_Mean[])
col_select <- c()
alr_click <- 0
sel_ind <- 0
peran <- 0
exran <- c()
exp_click <- 0
ysup <- max(testy$Spectrum)
yinf <- -1000000
ysup <- ysup + ysup*0.03
ranges_sel$y <- c(yinf, ysup)
ranges_sel$x <- c(min(CS_values_real[1,]),(max(CS_values_real[1,])))
ranges$y <- c(yinf, ysup)
ranges$x <- c(min(CS_values_real[1,]),(max(CS_values_real[1,])))
cor_cutoff <- 0.8
##############################################################################
############# STOCSY_I_SERVER.R RESET #############
ncor <- 1
rr <- c()
peran_stocsy_i <- 0
chkzoom_stocsy_i <- 1
idb_stocsy_i <- 0
testy_stocsy_i <<- data.frame(Chemical_Shift=CS_values_real[1,])
ranges_stocsy_i$x <- c(min(CS_values_real[1,]),(max(CS_values_real[1,])))
facts <<- reactiveValues(fac_stocsy_i = c())
##############################################################################
############# STOCSY_IS_SERVER.R RESET #############
ncor <- 1
Scaling_cor <- matrix()
rr_is <- c()
peran_stocsy_is <- 0
chkzoom_stocsy_is <- 1
idb_stocsy_is <- 0
testy_stocsy_is <<- data.frame(Chemical_Shift=CS_values_real[1,])
ranges_stocsy_is$x <- c(min(CS_values_real[1,]),(max(CS_values_real[1,])))
facts_is <<- reactiveValues(fac_stocsy_is = c())
##############################################################################
############# STOCSY_RT_SERVER.R RESET #############
ncor_rt <- 1
rr_rt <<- c()
peran_stocsy_rt <- 0
chkzoom_stocsy_rt <- 1
idb_stocsy_rt <- 0
testy_stocsy_rt <<- data.frame(Chemical_Shift=CS_values_real[1,])
ranges_stocsy_rt$x <- c(min(CS_values_real[1,]),(max(CS_values_real[1,])))
facts_rt <<- reactiveValues(fac_stocsy_rt = c())
##############################################################################
###################### Pretreatment_server ###################
rr_n <<- reactiveValues(csranges = c(-13131313,-131313))
rr_nn <<- reactiveValues(csranges = c(-13131313,-131313))
############################################################
}
####################################################################################
## Module Pré-treatment
###################### Mean centering ##################
.mean_centering <- function() {
if (!(sel_ind == 0)) {
# rr_n$csranges <<- c()
# rr_nn$csranges <- c()
CS_sel_real <<- CS_selection$vranges[order(CS_selection$vranges,decreasing = TRUE)]
matr_cor <<- matrix(data = NMRData[,col_select],dim(NMRData)[1], length(CS_sel_real))
NMR_MC <<- matrix(data = 0, nrow = dim(matr_cor)[1], ncol = dim(matr_cor)[2])
X_mean <- colMeans(matr_cor)
for (col in 1:dim(matr_cor)[2]){
for (lin in 1:dim(matr_cor)[1]){
NMR_MC[lin,col] <<- matr_cor[lin,col] - X_mean[col]
}
}
norm_test(p_value,m=NMR_MC)
output$norm_cond <- renderText(paste("Affter normalize: ", dif,"%"))
# f <- 0
# g <- 0
#
# for (j in 1:length(norm_var)) {
#
# if (norm_var[j] == "NO") {
# f <- f + 1
# col_sel_norm[f] <<- col_select[j]
# }
# else{
# g <- g + 1
# col_sel_norm2[g] <<- col_select[j]
# }
# }
# rr_n$csranges <- CS_values_real[1,col_sel_norm]
# rr_nn$csranges <- CS_values_real[1,col_sel_norm2]
}
else{
showModal(modalDialog(
title = "Warning!!!",
"No region was selected. You must first select the desired region(s) before to start Normalize section!",
easyClose = TRUE,
footer = modalButton("Close"),
size = "l"
))
}
}
###################### Scaling #########################
.autoscaling <- function() {
CS_sel_real <<- CS_selection$vranges[order(CS_selection$vranges,decreasing = TRUE)]
matr_cor <<- matrix(data = NMRData[,col_select],dim(NMRData)[1], length(CS_sel_real))
NMR_ASC <<- matrix(data = 0, nrow = dim(matr_cor)[1], ncol = dim(matr_cor)[2])
rows_temp <<- vector(mode="numeric", length=0)
NMR_MC <<- matrix(data = 0, nrow = dim(matr_cor)[1], ncol = dim(matr_cor)[2])
X_mean <- colMeans(matr_cor)
for (col in 1:dim(matr_cor)[2]){
for (lin in 1:dim(matr_cor)[1]){
NMR_MC[lin,col] <<- matr_cor[lin,col] - X_mean[col]
}
}
for (col in 1:dim(NMR_MC)[2]){
for (lin in 1:dim(NMR_MC)[1]) {
rows_temp[lin] <<- (NMR_MC[lin,col])^2
}
sum_MC <- sum(rows_temp)
sum_MC <- (sum_MC)/(dim(NMR_MC)[1]-1)
s <- sqrt(sum_MC)
for (lin in 1:dim(NMR_MC)[1]) {
NMR_ASC[lin,col] <<- (NMR_MC[lin,col])/(s)
}
}
norm_test(p_value,m=NMR_ASC)
}
##################### Pareto ##########################
.pareto <- function() {
CS_sel_real <<- CS_selection$vranges[order(CS_selection$vranges,decreasing = TRUE)]
matr_cor <<- matrix(data = NMRData[,col_select],dim(NMRData)[1], length(CS_sel_real))
NMR_Pareto <<- matrix(data = 0, nrow = dim(matr_cor)[1], ncol = dim(matr_cor)[2])
rows_temp <<- vector(mode="numeric", length=0)
NMR_MC <<- matrix(data = 0, nrow = dim(matr_cor)[1], ncol = dim(matr_cor)[2])
X_mean <- colMeans(matr_cor)
for (col in 1:dim(matr_cor)[2]){
for (lin in 1:dim(matr_cor)[1]){
NMR_MC[lin,col] <<- matr_cor[lin,col] - X_mean[col]
}
}
for (col in 1:dim(NMR_MC)[2]){
for (lin in 1:dim(NMR_MC)[1]) {
rows_temp[lin] <<- (NMR_MC[lin,col])^2
}
sum_MC <- sum(rows_temp)
sum_MC <- (sum_MC)/(dim(NMR_MC)[1]-1)
s <- sqrt(sum_MC)
s <- sqrt(s)
for (lin in 1:dim(NMR_MC)[1]) {
NMR_Pareto[lin,col] <<- (NMR_MC[lin,col])/(s)
}
}
norm_test(p_value,m=NMR_Pareto)
}
##################### Square root ##########################
.sqr_norm <- function() {
CS_sel_real <<- CS_selection$vranges[order(CS_selection$vranges,decreasing = TRUE)]
matr_cor <<- matrix(data = NMRData[,col_select],dim(NMRData)[1], length(CS_sel_real))
NMR_sqr <<- sqrt(matr_cor)
norm_test(p_value,m=NMR_sqr)
}
##################### Inverse Square root ##########################
.isqr_norm <- function() {
CS_sel_real <<- CS_selection$vranges[order(CS_selection$vranges,decreasing = TRUE)]
matr_cor <<- matrix(data = NMRData[,col_select],dim(NMRData)[1], length(CS_sel_real))
NMR_i_sqr <<- 1/sqrt(matr_cor)
norm_test(p_value,m=NMR_i_sqr)
}
##################### Log ##########################
.log_norm <- function() {
CS_sel_real <<- CS_selection$vranges[order(CS_selection$vranges,decreasing = TRUE)]
matr_cor <<- matrix(data = NMRData[,col_select],dim(NMRData)[1], length(CS_sel_real))
NMR_log <<- log(matr_cor)
norm_test(p_value,m=NMR_log)
}
##################### Start STOCSY-I #################
.s_stocsy <- function(matr) {
cor_cutoff_p <<- 0.9
cor_cutoff_n <<- -0.9
if (!(sel_ind == 0)) {
col_select <<- col_select[order(col_select)]
cor_pearson <<- cor(matr[,])
drv_pk <<- which.max(matr[1,])
rr <<- vector(mode="character")
norm_test(p_value,matr)
r_critical(p_value)
updateSliderInput(session, "cutoff_stocsy_i", min = -1,
max = 1, value = c(-0.9,0.9) , step= 0.01)
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_pearson[drv_pk,z] >= cor_cutoff_p) {
rr[k] <<- "A"
}
else {
rr[k] <<- "B"
}
if (cor_pearson[drv_pk,z] <= cor_cutoff_n) {
rr[k] <<- "C"
}
}
else {
rr[k] <<- "B"
}
}
facts$fac_stocsy_i <<- rr[]
facts_is$fac_stocsy_is <<- rr[]
facts_rt$fac_stocsy_rt <<- rr[]
updateTabsetPanel(session, "main_bar", "STOCSY-I")
}
else {
showModal(modalDialog(
title = "Warning!!!",
"No region was selected. You must first select the desired region(s) before to start STOSCY analysis!",
easyClose = TRUE,
footer = modalButton("Close"),
size = "l"
))
}
}
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