Nothing
inst/app/stocsy_rt_server.R
In iSTATS: A Graphical Interface to Perform STOCSY Analyses on NMR Data
ncor_rt <<- 1
rr_rt <<- c()
chkzoom_stocsy_rt <<- 1
idb_stocsy_rt <<- 0
peran_stocsy_rt <<- 0
ysup_stocsy_rt <- max(NMRData_plot[1,])
yinf_stocsy_rt <- ysup_stocsy_rt*-0.03
ysup_stocsy_rt <- ysup_stocsy_rt + ysup_stocsy_rt*0.03
testy_stocsy_rt <- data.frame(Chemical_Shift=CS_values_real[1,])
ranges_stocsy_rt <- reactiveValues(x = c((min(testy_stocsy_rt$Chemical_Shift)), max(testy_stocsy_rt$Chemical_Shift)), y = c(yinf_stocsy_rt,ysup_stocsy_rt))
spectrums_stocsy_rt <- reactiveValues(dat = data.frame(Chemical_Shift=CS_values_real[1,],Spectrum=NMRData_plot[1,])) #, facs = data.frame(fac_stocsy_rt = rr_rt[])
facts_rt <<- reactiveValues(fac_stocsy_rt = c())
#Plot
output$plot_stocsy_rt <- renderPlot({
ggplot2::ggplot(spectrums_stocsy_rt$dat,ggplot2::aes(Chemical_Shift,Spectrum)) + ggplot2::geom_line(color='blue') +
ggplot2::geom_line(ggplot2::aes(colour=facts_rt$fac_stocsy_rt,group=1)) + ggplot2::guides(colour = FALSE) + ggplot2::scale_colour_manual(values = c("red","blue")) +
ggplot2::coord_cartesian(xlim = c(ranges_stocsy_i$rt[2],ranges_stocsy_i$rt[1]), ylim = ranges_stocsy_rt$y, expand = FALSE) +
ggplot2::scale_x_reverse() +
ggplot2::theme(axis.text.x = ggplot2::element_text(size = 12, color = "#000000"),
axis.text.y = ggplot2::element_text(size = 12, color = "#000000"),
title = ggplot2::element_text(face = "bold", color = "#000000", size = 17),
axis.title = ggplot2::element_text(face = "bold", color = "#000000", size = 15)
) +
ggplot2::labs(x = "Chemical Shift", y = "Intensity")
})
observeEvent(input$radio_rt, {
value <<- (input$radio_rt)
if (value == 1) {
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_rt) {
rr_rt[k] <<- "A"
}
else {
rr_rt[k] <<- "B"
}
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
}
if (value == 2) {
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_spearman[drv_pk,z] >= cor_cutoff_rt) {
rr_rt[k] <<- "A"
}
else {
rr_rt[k] <<- "B"
}
}
else {
rr_rt[k] <<- "B"
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
}
if (value == 3) {
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_rt) {
rr_rt[k] <<- "A"
}
else {
}
if (cor_spearman[drv_pk,z] >= cor_cutoff_rt) {
rr_rt[k] <<- "A"
}
else {
}
}
else {
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
}
})
observeEvent(input$plot_brush_stocsy_rt,{
brush <- input$plot_brush_stocsy_rt
if (!is.null(brush)) {
ranges_stocsy_rt$x <- c(brush$xmin, brush$xmax)
ranges_stocsy_rt$y <- c(brush$ymin, brush$ymax)
idb_stocsy_rt <<- 1
peran_stocsy_rt <<- (ranges_stocsy_rt$x[2] - ranges_stocsy_rt$x[1])*0.2
}
else {
ranges_stocsy_rt$x <- NULL
}
})
observeEvent(input$x2_stocsy_rt, {
tryton <<- facts_rt$fac_stocsy_rt
chkzoom_stocsy_rt <<- chkzoom_stocsy_rt*2
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum*2
})
observeEvent(input$x8_stocsy_rt, {
chkzoom_stocsy_rt <<- chkzoom_stocsy_rt*8
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum*8
})
observeEvent(input$q2_stocsy_rt, {
chkzoom_stocsy_rt <<- chkzoom_stocsy_rt/2
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum/2
})
observeEvent(input$q8_stocsy_rt, {
chkzoom_stocsy_rt <<- chkzoom_stocsy_rt/8
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum/8
})
observeEvent(input$all_stocsy_rt, {
ranges_stocsy_rt$x <- c((min(testy_stocsy_rt$Chemical_Shift)), max(testy_stocsy_rt$Chemical_Shift))
freshnum_stocsy_rt <- which(file_names[] == input$spectrum_list_stocsy_rt)
ysup_stocsy_rt <- max (NMRData_plot[freshnum_stocsy_rt,])
yinf_stocsy_rt <- ysup_stocsy_rt*-0.03
ysup_stocsy_rt <- ysup_stocsy_rt + ysup_stocsy_rt*0.03
ranges_stocsy_rt$y <- c(yinf_stocsy_rt,ysup_stocsy_rt)
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum/chkzoom_stocsy_rt
chkzoom_stocsy_rt <<- 1
idb_stocsy_rt <<- 0
})
observeEvent(input$s_left_stocsy_rt, {
if (!(ranges_stocsy_rt$x[1] <= min(testy_stocsy_rt$Chemical_Shift))) {
ranges_stocsy_rt$x[1] <<- (ranges_stocsy_rt$x[1] - peran_stocsy_rt)
ranges_stocsy_rt$x[2] <<- (ranges_stocsy_rt$x[2] - peran_stocsy_rt)
}
})
observeEvent(input$s_right_stocsy_rt, {
if (!(ranges_stocsy_rt$x[2] >= max(testy_stocsy_rt$Chemical_Shift))) {
ranges_stocsy_rt$x[1] <<- (ranges_stocsy_rt$x[1] + peran_stocsy_rt)
ranges_stocsy_rt$x[2] <<- (ranges_stocsy_rt$x[2] + peran_stocsy_rt)
}
})
observeEvent(input$s_left_f_stocsy_rt, {
if (!(ranges_stocsy_rt$x[1] <= min(testy_stocsy_rt$Chemical_Shift))) {
das_stocsy_rt <<- (ranges_stocsy_rt$x[2] - ranges_stocsy_rt$x[1])
ranges_stocsy_rt$x[1] <<- min(testy_stocsy_rt$Chemical_Shift)
ranges_stocsy_rt$x[2] <<- (min(testy_stocsy_rt$Chemical_Shift) + das_stocsy_rt)
}
})
observeEvent(input$s_right_f_stocsy_rt, {
if (!(ranges_stocsy_rt$x[2] >= max(testy_stocsy_rt$Chemical_Shift))) {
das_stocsy_rt <<- (ranges_stocsy_rt$x[2] - ranges_stocsy_rt$x[1])
ranges_stocsy_rt$x[2] <<- max(testy_stocsy_rt$Chemical_Shift)
ranges_stocsy_rt$x[1] <<- (max(testy_stocsy_rt$Chemical_Shift) - das_stocsy_rt)
}
})
observeEvent(input$spectrum_list_stocsy_rt,{
freshnum_stocsy_rt <- which(file_names[] == input$spectrum_list_stocsy_rt)
spectrums_stocsy_rt$dat <- data.frame(Chemical_Shift=CS_values_real[1,],Spectrum=NMRData_plot[freshnum_stocsy_rt,])
if (!idb_stocsy_rt && chkzoom_stocsy_rt == 1) {
ysup_stocsy_rt <- max (NMRData_plot[freshnum_stocsy_rt,])
yinf_stocsy_rt <- ysup_stocsy_rt*-0.03
ysup_stocsy_rt <- ysup_stocsy_rt + ysup_stocsy_rt*0.03
ranges_stocsy_rt$y <- c(yinf_stocsy_rt, ysup_stocsy_rt)
}
else {
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum*chkzoom_stocsy_rt
}
})
observeEvent(input$dblclick_stocsy_rt, {
drv_pk_ort <<- which(abs(CS_values_real[1,]-input$dblclick_stocsy_rt$x)==min(abs(CS_values_real[1,]-input$dblclick_stocsy_rt$x)))
if (drv_pk %in% col_select) {
drv_pk <<- which(col_select[] == drv_pk)
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_pearson[drv_pk,z] >= input$cutoff_stocsy_rt) {
rr_rt[k] <<- "A"
}
else {
rr_rt[k] <<- "B"
}
}
else {
rr_rt[k] <<- "B" #falta transformar 'rr_rt' em variavel reativa
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
}
else {
showModal(modalDialog(
title = "Warning!!!",
"The selected point is not inside the previously loaded regions. Please, click on another point or load a new group of signals!",
easyClose = TRUE,
footer = modalButton("Close"),
size = "l",
drv_pk <- drv_pk_ort
)
)
}
})
observeEvent(input$cutoff_stocsy_rt, {
cor_cutoff_rt <<- input$cutoff_stocsy_rt
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_rt) {
rr_rt[k] <<- "A"
}
else {
rr_rt[k] <<- "B"
}
}
else {
rr_rt[k] <<- "B" #falta transformar 'rr_rt' em variavel reativa
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
})
# Plot_PDF I-STOCSY
observeEvent(input$stocsy_rt_print_PDF, {
ggplot2::ggsave("stocsy-rt.pdf",path = choose.dir(),width=195, height=105, units="mm", dpi = 300)
})
observeEvent(input$hover_stocsy_rt, {
drv_pk <<- which(abs(CS_values_real[1,]-input$hover_stocsy_rt$x)==min(abs(CS_values_real[1,]-input$hover_stocsy_rt$x)))
if (drv_pk %in% col_select) {
drv_pk <<- which(col_select[] == drv_pk)
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_pearson[drv_pk,z] >= 0.8) {
rr_rt[k] <<- "A"
}
else {
rr_rt[k] <<- "B"
}
}
else {
rr_rt[k] <<- "B" #falta transformar 'rr_rt' em variavel reativa
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
}
else {
}
})
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iSTATS documentation built on Aug. 11, 2023, 5:08 p.m.
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ncor_rt <<- 1
rr_rt <<- c()
chkzoom_stocsy_rt <<- 1
idb_stocsy_rt <<- 0
peran_stocsy_rt <<- 0
ysup_stocsy_rt <- max(NMRData_plot[1,])
yinf_stocsy_rt <- ysup_stocsy_rt*-0.03
ysup_stocsy_rt <- ysup_stocsy_rt + ysup_stocsy_rt*0.03
testy_stocsy_rt <- data.frame(Chemical_Shift=CS_values_real[1,])
ranges_stocsy_rt <- reactiveValues(x = c((min(testy_stocsy_rt$Chemical_Shift)), max(testy_stocsy_rt$Chemical_Shift)), y = c(yinf_stocsy_rt,ysup_stocsy_rt))
spectrums_stocsy_rt <- reactiveValues(dat = data.frame(Chemical_Shift=CS_values_real[1,],Spectrum=NMRData_plot[1,])) #, facs = data.frame(fac_stocsy_rt = rr_rt[])
facts_rt <<- reactiveValues(fac_stocsy_rt = c())
#Plot
output$plot_stocsy_rt <- renderPlot({
ggplot2::ggplot(spectrums_stocsy_rt$dat,ggplot2::aes(Chemical_Shift,Spectrum)) + ggplot2::geom_line(color='blue') +
ggplot2::geom_line(ggplot2::aes(colour=facts_rt$fac_stocsy_rt,group=1)) + ggplot2::guides(colour = FALSE) + ggplot2::scale_colour_manual(values = c("red","blue")) +
ggplot2::coord_cartesian(xlim = c(ranges_stocsy_i$rt[2],ranges_stocsy_i$rt[1]), ylim = ranges_stocsy_rt$y, expand = FALSE) +
ggplot2::scale_x_reverse() +
ggplot2::theme(axis.text.x = ggplot2::element_text(size = 12, color = "#000000"),
axis.text.y = ggplot2::element_text(size = 12, color = "#000000"),
title = ggplot2::element_text(face = "bold", color = "#000000", size = 17),
axis.title = ggplot2::element_text(face = "bold", color = "#000000", size = 15)
) +
ggplot2::labs(x = "Chemical Shift", y = "Intensity")
})
observeEvent(input$radio_rt, {
value <<- (input$radio_rt)
if (value == 1) {
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_rt) {
rr_rt[k] <<- "A"
}
else {
rr_rt[k] <<- "B"
}
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
}
if (value == 2) {
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_spearman[drv_pk,z] >= cor_cutoff_rt) {
rr_rt[k] <<- "A"
}
else {
rr_rt[k] <<- "B"
}
}
else {
rr_rt[k] <<- "B"
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
}
if (value == 3) {
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_rt) {
rr_rt[k] <<- "A"
}
else {
}
if (cor_spearman[drv_pk,z] >= cor_cutoff_rt) {
rr_rt[k] <<- "A"
}
else {
}
}
else {
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
}
})
observeEvent(input$plot_brush_stocsy_rt,{
brush <- input$plot_brush_stocsy_rt
if (!is.null(brush)) {
ranges_stocsy_rt$x <- c(brush$xmin, brush$xmax)
ranges_stocsy_rt$y <- c(brush$ymin, brush$ymax)
idb_stocsy_rt <<- 1
peran_stocsy_rt <<- (ranges_stocsy_rt$x[2] - ranges_stocsy_rt$x[1])*0.2
}
else {
ranges_stocsy_rt$x <- NULL
}
})
observeEvent(input$x2_stocsy_rt, {
tryton <<- facts_rt$fac_stocsy_rt
chkzoom_stocsy_rt <<- chkzoom_stocsy_rt*2
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum*2
})
observeEvent(input$x8_stocsy_rt, {
chkzoom_stocsy_rt <<- chkzoom_stocsy_rt*8
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum*8
})
observeEvent(input$q2_stocsy_rt, {
chkzoom_stocsy_rt <<- chkzoom_stocsy_rt/2
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum/2
})
observeEvent(input$q8_stocsy_rt, {
chkzoom_stocsy_rt <<- chkzoom_stocsy_rt/8
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum/8
})
observeEvent(input$all_stocsy_rt, {
ranges_stocsy_rt$x <- c((min(testy_stocsy_rt$Chemical_Shift)), max(testy_stocsy_rt$Chemical_Shift))
freshnum_stocsy_rt <- which(file_names[] == input$spectrum_list_stocsy_rt)
ysup_stocsy_rt <- max (NMRData_plot[freshnum_stocsy_rt,])
yinf_stocsy_rt <- ysup_stocsy_rt*-0.03
ysup_stocsy_rt <- ysup_stocsy_rt + ysup_stocsy_rt*0.03
ranges_stocsy_rt$y <- c(yinf_stocsy_rt,ysup_stocsy_rt)
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum/chkzoom_stocsy_rt
chkzoom_stocsy_rt <<- 1
idb_stocsy_rt <<- 0
})
observeEvent(input$s_left_stocsy_rt, {
if (!(ranges_stocsy_rt$x[1] <= min(testy_stocsy_rt$Chemical_Shift))) {
ranges_stocsy_rt$x[1] <<- (ranges_stocsy_rt$x[1] - peran_stocsy_rt)
ranges_stocsy_rt$x[2] <<- (ranges_stocsy_rt$x[2] - peran_stocsy_rt)
}
})
observeEvent(input$s_right_stocsy_rt, {
if (!(ranges_stocsy_rt$x[2] >= max(testy_stocsy_rt$Chemical_Shift))) {
ranges_stocsy_rt$x[1] <<- (ranges_stocsy_rt$x[1] + peran_stocsy_rt)
ranges_stocsy_rt$x[2] <<- (ranges_stocsy_rt$x[2] + peran_stocsy_rt)
}
})
observeEvent(input$s_left_f_stocsy_rt, {
if (!(ranges_stocsy_rt$x[1] <= min(testy_stocsy_rt$Chemical_Shift))) {
das_stocsy_rt <<- (ranges_stocsy_rt$x[2] - ranges_stocsy_rt$x[1])
ranges_stocsy_rt$x[1] <<- min(testy_stocsy_rt$Chemical_Shift)
ranges_stocsy_rt$x[2] <<- (min(testy_stocsy_rt$Chemical_Shift) + das_stocsy_rt)
}
})
observeEvent(input$s_right_f_stocsy_rt, {
if (!(ranges_stocsy_rt$x[2] >= max(testy_stocsy_rt$Chemical_Shift))) {
das_stocsy_rt <<- (ranges_stocsy_rt$x[2] - ranges_stocsy_rt$x[1])
ranges_stocsy_rt$x[2] <<- max(testy_stocsy_rt$Chemical_Shift)
ranges_stocsy_rt$x[1] <<- (max(testy_stocsy_rt$Chemical_Shift) - das_stocsy_rt)
}
})
observeEvent(input$spectrum_list_stocsy_rt,{
freshnum_stocsy_rt <- which(file_names[] == input$spectrum_list_stocsy_rt)
spectrums_stocsy_rt$dat <- data.frame(Chemical_Shift=CS_values_real[1,],Spectrum=NMRData_plot[freshnum_stocsy_rt,])
if (!idb_stocsy_rt && chkzoom_stocsy_rt == 1) {
ysup_stocsy_rt <- max (NMRData_plot[freshnum_stocsy_rt,])
yinf_stocsy_rt <- ysup_stocsy_rt*-0.03
ysup_stocsy_rt <- ysup_stocsy_rt + ysup_stocsy_rt*0.03
ranges_stocsy_rt$y <- c(yinf_stocsy_rt, ysup_stocsy_rt)
}
else {
spectrums_stocsy_rt$dat$Spectrum <- spectrums_stocsy_rt$dat$Spectrum*chkzoom_stocsy_rt
}
})
observeEvent(input$dblclick_stocsy_rt, {
drv_pk_ort <<- which(abs(CS_values_real[1,]-input$dblclick_stocsy_rt$x)==min(abs(CS_values_real[1,]-input$dblclick_stocsy_rt$x)))
if (drv_pk %in% col_select) {
drv_pk <<- which(col_select[] == drv_pk)
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_pearson[drv_pk,z] >= input$cutoff_stocsy_rt) {
rr_rt[k] <<- "A"
}
else {
rr_rt[k] <<- "B"
}
}
else {
rr_rt[k] <<- "B" #falta transformar 'rr_rt' em variavel reativa
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
}
else {
showModal(modalDialog(
title = "Warning!!!",
"The selected point is not inside the previously loaded regions. Please, click on another point or load a new group of signals!",
easyClose = TRUE,
footer = modalButton("Close"),
size = "l",
drv_pk <- drv_pk_ort
)
)
}
})
observeEvent(input$cutoff_stocsy_rt, {
cor_cutoff_rt <<- input$cutoff_stocsy_rt
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_rt) {
rr_rt[k] <<- "A"
}
else {
rr_rt[k] <<- "B"
}
}
else {
rr_rt[k] <<- "B" #falta transformar 'rr_rt' em variavel reativa
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
})
# Plot_PDF I-STOCSY
observeEvent(input$stocsy_rt_print_PDF, {
ggplot2::ggsave("stocsy-rt.pdf",path = choose.dir(),width=195, height=105, units="mm", dpi = 300)
})
observeEvent(input$hover_stocsy_rt, {
drv_pk <<- which(abs(CS_values_real[1,]-input$hover_stocsy_rt$x)==min(abs(CS_values_real[1,]-input$hover_stocsy_rt$x)))
if (drv_pk %in% col_select) {
drv_pk <<- which(col_select[] == drv_pk)
for (k in 1:dim(NMRData)[2]) {
if (k %in% col_select) {
z <<- which(col_select[] == k)
if (cor_pearson[drv_pk,z] >= 0.8) {
rr_rt[k] <<- "A"
}
else {
rr_rt[k] <<- "B"
}
}
else {
rr_rt[k] <<- "B" #falta transformar 'rr_rt' em variavel reativa
}
}
facts_rt$fac_stocsy_rt <<- rr_rt[]
}
else {
}
})
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