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server.R
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server.R
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library(shiny)
library("ggplot2")
function(input, output, session) {
######### DOWNLOAD EXAMPLE FILE ###############
info <- reactiveValues(data = NULL)
data1 <- read.csv("./Data/hsf_vs_all_tf.csv",header=FALSE)
colnames(data1) <- c("gene", "RNA", "group")
output$downloadData1 <- downloadHandler(
filename = function() {
paste("upload_file_example_1", ".csv", sep="")
},
content = function(filename) {
write.table(data1, filename, sep=",", col.names=FALSE,row.names = F,quote=F)
}
)
data2 <- read.csv("./Data/hsf_vs_all_tf_normal.csv",header=FALSE)
colnames(data2) <- c("gene", "RNA", "group")
output$downloadData2 <- downloadHandler(
filename = function() {
paste("upload_file_example_2", ".csv", sep="")
},
content = function(filename) {
write.table(data2, filename, sep=",", col.names=FALSE,row.names = F,quote=F)
}
)
######## aditional test data ###########
data3 <- read.csv("./Data/Gramene-UV-stress.csv",header=FALSE)
output$downloadData3 <- downloadHandler(
filename = function() {
paste("Gramene-UV-stress", ".csv", sep="")
},
content = function(filename) {
write.table(data3, filename, sep=",", col.names=FALSE,row.names = F,quote=F)
}
)
data4 <- read.csv("./Data/maize-GAMER-UV-stress.csv",header=FALSE)
output$downloadData4 <- downloadHandler(
filename = function() {
paste("maize-GAMER-UV-stress", ".csv", sep="")
},
content = function(filename) {
write.table(data4, filename, sep=",", col.names=FALSE,row.names = F,quote=F)
}
)
data5 <- read.csv("./Data/Gramene-non-stress.csv",header=FALSE)
output$downloadData5 <- downloadHandler(
filename = function() {
paste("Gramene-non-stress", ".csv", sep="")
},
content = function(filename) {
write.table(data5, filename, sep=",", col.names=FALSE,row.names = F,quote=F)
}
)
data6 <- read.csv("./Data/maize-GAMER-non-stress.csv",header=FALSE)
output$downloadData6 <- downloadHandler(
filename = function() {
paste("maize-GAMER-non-stress", ".csv", sep="")
},
content = function(filename) {
write.table(data6, filename, sep=",", col.names=FALSE,row.names = F,quote=F)
}
)
data7 <- read.csv("./Data/Gramene-non-stress-biological-replicate-1.csv",header=FALSE)
output$downloadData7 <- downloadHandler(
filename = function() {
paste("Gramene-non-stress-biological-replicate-1", ".csv", sep="")
},
content = function(filename) {
write.table(data7, filename, sep=",", col.names=FALSE,row.names = F,quote=F)
}
)
data8 <- read.csv("./Data/Gramene-non-stress-biological-replicate-2.csv",header=FALSE)
output$downloadData8 <- downloadHandler(
filename = function() {
paste("Gramene-non-stress-biological-replicate-2", ".csv", sep="")
},
content = function(filename) {
write.table(data8, filename, sep=",", col.names=FALSE,row.names = F,quote=F)
}
)
data9 <- read.csv("./Data/Gramene-non-stress-biological-replicate-1.csv",header=FALSE)
output$downloadData9 <- downloadHandler(
filename = function() {
paste("maize-GAMER-non-stress-biological-replicate-1", ".csv", sep="")
},
content = function(filename) {
write.table(data9, filename, sep=",", col.names=FALSE,row.names = F,quote=F)
}
)
data10 <- read.csv("./Data/maize-GAMER-non-stress-biological-replicate-2.csv",header=FALSE)
output$downloadData10 <- downloadHandler(
filename = function() {
paste("maize-GAMER-non-stress-biological-replicate-2", ".csv", sep="")
},
content = function(filename) {
write.table(data10, filename, sep=",", col.names=FALSE,row.names = F,quote=F)
}
)
######################################## submit user data #################
observeEvent(input$submit,
{
withProgress(message = 'Reading data in progress',
detail = 'This may take a while...', value = 1, {
inFile <- input$file
# Instead # if (is.null(inFile)) ... use "req"
req(inFile)
# Changes in read.table
data1 <- read.csv(inFile$datapath, header = FALSE)
colnames(data1) <- c("gene", "RNA", "group")
# Update select input immediately after clicking on the action button.
updateSelectInput(session, "group1", "Select Gene Group 1", choices = data1$group)
updateSelectInput(session, "group2", "Select Gene Group 2", choices = data1$group)
info$data1 <- data1
})
})
observeEvent(input$submitexam1,
{
withProgress(message = 'Reading data in progress',
detail = 'This may take a while...', value = 1, {
updateSelectInput(session, "group1", "Select Gene Group 1", choices = data1$group)
updateSelectInput(session, "group2", "Select Gene Group 2", choices = data1$group)
info$data1 <- data1
})
})
output$distr <- renderPlot({
if (is.null(info$data1)) return()
withProgress(message = 'Calculation in progress',
detail = 'This may take a while...', value = 1, {
f <- info$data1
sub1<-subset(f,f[,3]==input$group1)
sub2<-subset(f,f[,3]==input$group2)
all<-rbind(sub1,sub2)
normal_hk <-
subset(f, f[, 3] == "housekeeping genes")
normal_mean <- mean(log(normal_hk[, 2] + 1))
ggplot(all, aes(log(RNA+1) / normal_mean,color=group)) +
geom_density(alpha = 0.5) + theme(axis.text = element_text(size =
20),
axis.title = element_text(size = 20),
legend.title = element_text(colour="black", size=20),
legend.text = element_text(colour="black", size = 20)) +
theme(
panel.background = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(
colour = "black",
fill = NA,
size = 1.5
)
) +
xlab("RNA expression level") + ylab("Percentage of genes %")+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
})
})
output$qqtitle1 <- renderText({
if (is.null(info$data1)) return()
input$group1
})
output$qqplot1 <- renderPlot({
if (is.null(info$data1)) return()
withProgress(message = 'Calculation in progress',
detail = 'This may take a while...', value = 1, {
f <- info$data1
sub<-subset(f,f[,3]==input$group1)
qqnorm(log(sub[,2]+1))
qqline(log(sub[,2]+1))
})
})
output$qqtitle2 <- renderText({
input$group2
})
output$qqplot2 <- renderPlot({
if (is.null(info$data1)) return()
withProgress(message = 'Calculation in progress',
detail = 'This may take a while...', value = 1, {
f <- info$data1
sub<-subset(f,f[,3]==input$group2)
normal_hk<-subset(f, f[, 3] == "housekeeping genes")
normal_mean <- mean(log(normal_hk[, 2] + 1))
qqnorm(log(sub[,2]+1)/normal_mean)
qqline(log(sub[,2]+1)/normal_mean)
})
})
output$ttest<-renderText({
if (is.null(info$data1)) return()
withProgress(message = 'Student T-test in progress',
detail = 'This may take a while...',
value = 1,
{
f <- info$data1
sub1 <- subset(f, f[, 3] == input$group1)
sub2 <- subset(f, f[, 3] == input$group2)
normal_hk <- subset(f, f[, 3] == "housekeeping genes")
normal_mean <- mean(log(normal_hk[, 2] + 1))
p<-t.test(log(sub1[, 2] + 1) / normal_mean,
log(sub2[, 2] + 1) / normal_mean)$p.value
if(p>=0.05)
{
result<-p
}
if(p<0.05 && p>=0.01)
{
result<-"p<0.05"
}
if(p<0.01 && p>=0.001)
{
result<-"p<0.01"
}
if(p<0.001 && p>=0.0001)
{
result<-"p<0.001"
}
if(p<0.0001)
{
result<-"p<0.0001"
}
result
})
})
output$parametric<-renderTable({
if (is.null(info$data1)) return()
withProgress(message = 'Statistics summury in progress',
detail = 'This may take a while...',
value = 1,
{
f <- info$data1
sub1<-subset(f,f[,3]==input$group1)
sub2<-subset(f,f[,3]==input$group2)
normal_hk<-subset(f, f[,3] == "housekeeping genes")
normal_mean <- mean(log(normal_hk[,2] + 1))
data.frame(
mean1 = mean(log(sub1[,2]+1))/normal_mean,
mean2 = mean(log(sub2[,2]+1))/normal_mean,
sd1=sd(log(sub1[,2]+1)/normal_mean),
sd2=sd(log(sub2[,2]+1)/normal_mean)
)
})
})
output$wilcox<-renderText({
if (is.null(info$data1)) return()
withProgress(message = 'Wilcoxon rank-sum test in progress',
detail = 'This may take a while...',
value = 1,
{
f <- info$data1
sub1<-subset(f,f[,3]==input$group1)
sub2<-subset(f,f[,3]==input$group2)
normal_hk<-subset(f, f[, 3] == "housekeeping genes")
normal_mean <- mean(log(normal_hk[, 2] + 1))
p<-wilcox.test(log(sub1[,2]+1)/normal_mean, log(sub2[,2]+1)/normal_mean,paired = FALSE)$p.value
if(p>=0.05)
{
result<-p
}
if(p<0.05 && p>=0.01)
{
result<-"p<0.05"
}
if(p<0.01 && p>=0.001)
{
result<-"p<0.01"
}
if(p<0.001 && p>=0.0001)
{
result<-"p<0.001"
}
if(p<0.0001)
{
result<-"p<0.0001"
}
result
})
})
######################### ACROSS SAMPLE COMPARISON ###############################
observeEvent(input$submit2, {
inFile <- input$file2_1
# Instead # if (is.null(inFile)) ... use "req"
req(inFile)
# Changes in read.table
f <- read.csv(inFile$datapath, header = FALSE)
colnames(f) <- c("gene", "RNA", "group")
# Update select input immediately after clicking on the action button.
updateSelectInput(session, "group2_1", "Select Gene Group 1 from file 1", choices = f$group)
info$data2<-f
})
observeEvent(input$submit2, {
inFile <- input$file2_2
# Instead # if (is.null(inFile)) ... use "req"
req(inFile)
# Changes in read.table
f <- read.csv(inFile$datapath, header = FALSE)
colnames(f) <- c("gene", "RNA", "group")
# Update select input immediately after clicking on the action button.
updateSelectInput(session, "group2_2", "Select Gene Group 2 from file 2", choices = f$group)
info$data3<-f
})
observeEvent(input$submitexam2, {
data2 <- read.csv("./Data/maize-GAMER-UV-stress.csv",header=FALSE)
colnames(data2) <- c("gene", "RNA", "group")
# Update select input immediately after clicking on the action button.
updateSelectInput(session, "group2_1", "Select Gene Group 1", choices = data2$group)
info$data2<-data2
data3 <- read.csv("./Data/maize-GAMER-non-stress.csv",header=FALSE)
colnames(data3) <- c("gene", "RNA", "group")
# Update select input immediately after clicking on the action button.
updateSelectInput(session, "group2_2", "Select Gene Group 2", choices = data3$group)
info$data3<-data3
})
output$distr2 <- renderPlot({
if (is.null(info$data2)) return()
withProgress(message = 'Plotting in progress',
detail = 'This may take a while...', value = 0.1, {
f1 <- info$data2
sub1<-subset(f1,f1[,3]==input$group2_1)
new_name_1<-paste(input$group2_1," from file 1")
sub1[,3]<-new_name_1
f2 <- info$data3
sub2<-subset(f2,f2[,3]==input$group2_2)
new_name_2<-paste(input$group2_2," from file 2")
sub2[,3]<-new_name_2
normal_hk_1 <-
subset(f1, f1[, 3] == "housekeeping genes")
normal_hk_2 <-
subset(f2, f2[, 3] == "housekeeping genes")
normal_mean_1 <- mean(log(normal_hk_1[, 2] + 1))
normal_mean_2 <- mean(log(normal_hk_2[, 2] + 1))
# normalize before rbind ####
sub1$RNA_normalized<-log(sub1$RNA+1)/normal_mean_1
sub2$RNA_normalized<-log(sub2$RNA+1)/normal_mean_2
all<-rbind(sub1,sub2)
ggplot(all, aes(RNA_normalized,color=group)) +
geom_density(alpha = 0.5) + theme(axis.text = element_text(size =
20),
axis.title = element_text(size = 20),
legend.title = element_text(colour="black", size=20),
legend.text = element_text(colour="black", size = 20)) +
theme(
panel.background = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(
colour = "black",
fill = NA,
size = 1.5
)
) +
xlab("RNA expression level") + ylab("Percentage of genes %")+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
})
})
output$qqtitle2_1 <- renderText({
if (is.null(info$data2)) return()
input$group2_1
})
output$qqplot2_1 <- renderPlot({
if (is.null(info$data2)) return()
withProgress(message = 'Calculation in progress',
detail = 'This may take a while...', value = 1, {
f <- info$data2
sub<-subset(f,f[,3]==input$group2_1)
qqnorm(log(sub[,2]+1))
qqline(log(sub[,2]+1))
})
})
output$qqtitle2_2 <- renderText({
if (is.null(info$data2)) return()
input$group2_2
})
output$qqplot2_2 <- renderPlot({
if (is.null(info$data2)) return()
withProgress(message = 'Calculation in progress',
detail = 'This may take a while...', value = 1, {
f <- info$data3
sub<-subset(f,f[,3]==input$group2_2)
qqnorm(log(sub[,2]+1))
qqline(log(sub[,2]+1))
})
})
output$ttest2<-renderText({
if (is.null(info$data2)) return()
f1 <- info$data2
sub2_1<-subset(f1,f1[,3]==input$group2_1)
f2 <- info$data3
sub2_2<-subset(f2,f2[,3]==input$group2_2)
if(nrow(sub2_1) ==nrow(sub2_2))
{
num2<-TRUE
}
if(nrow(sub2_1) !=nrow(sub2_2))
{
num2<-FALSE
}
normal_hk_1 <-
subset(f1, f1[, 3] == "housekeeping genes")
normal_hk_2 <-
subset(f2, f2[, 3] == "housekeeping genes")
normal_mean_1 <- mean(log(normal_hk_1[, 2] + 1))
normal_mean_2 <- mean(log(normal_hk_2[, 2] + 1))
# paired t-test is over powerful to distinguish small random changes even between biological replicates
p<-t.test(log(sub2_1[,2]+1)/normal_mean_1,log(sub2_2[,2]+1)/normal_mean_2)$p.value
if(p>=0.05)
{
result<-p
}
if(p<0.05 && p>=0.01)
{
result<-"p<0.05"
}
if(p<0.01 && p>=0.001)
{
result<-"p<0.01"
}
if(p<0.001 && p>=0.0001)
{
result<-"p<0.001"
}
if(p<0.0001)
{
result<-"p<0.0001"
}
result
})
output$parametric2<-renderTable({
if (is.null(info$data2)) return()
f1 <- info$data2
sub2_1<-subset(f1,f1[,3]==input$group2_1)
f2 <- info$data3
sub2_2<-subset(f2,f2[,3]==input$group2_2)
normal_hk_1 <-
subset(f1, f1[, 3] == "housekeeping genes")
normal_hk_2 <-
subset(f2, f2[, 3] == "housekeeping genes")
normal_mean_1 <- mean(log(normal_hk_1[,2] + 1))
normal_mean_2 <- mean(log(normal_hk_2[,2] + 1))
data.frame(
mean1 = mean(log(sub2_1[,2]+1))/normal_mean_1,
mean2 = mean(log(sub2_2[,2]+1))/normal_mean_2,
sd1=sd(log(sub2_1[,2]+1)/normal_mean_1),
sd2=sd(log(sub2_2[,2]+1)/normal_mean_2)
)
})
output$wilcox2<-renderText({
if (is.null(info$data2)) return()
f1 <- info$data2
sub2_1<-subset(f1,f1[,3]==input$group2_1)
f2 <- info$data3
sub2_2<-subset(f2,f2[,3]==input$group2_2)
normal_hk_1 <-
subset(f1, f1[, 3] == "housekeeping genes")
normal_hk_2 <-
subset(f2, f2[, 3] == "housekeeping genes")
normal_mean_1 <- mean(log(normal_hk_1[,2] + 1))
normal_mean_2 <- mean(log(normal_hk_2[,2] + 1))
p<-wilcox.test(log(sub2_1[,2]+1)/normal_mean_1, log(sub2_2[,2]+1)/normal_mean_2,paired=TRUE)$p.value
if(p>=0.05)
{
result<-p
}
if(p<0.05 && p>=0.01)
{
result<-"p<0.05"
}
if(p<0.01 && p>=0.001)
{
result<-"p<0.01"
}
if(p<0.001 && p>=0.0001)
{
result<-"p<0.001"
}
if(p<0.0001)
{
result<-"p<0.0001"
}
result
})
output$wilcox3<-renderText({
if (is.null(info$data2)) return()
f1 <- info$data2
sub2_1<-subset(f1,f1[,3]==input$group2_1)
f2 <- info$data3
sub2_2<-subset(f2,f2[,3]==input$group2_2)
normal_hk_1 <-
subset(f1, f1[, 3] == "housekeeping genes")
normal_hk_2 <-
subset(f2, f2[, 3] == "housekeeping genes")
normal_mean_1 <- mean(log(normal_hk_1[,2] + 1))
normal_mean_2 <- mean(log(normal_hk_2[,2] + 1))
p<-wilcox.test(log(sub2_1[,2]+1)/normal_mean_1, log(sub2_2[,2]+1)/normal_mean_2,paired = FALSE)$p.value
if(p>=0.05)
{
result<-p
}
if(p<0.05 && p>=0.01)
{
result<-"p<0.05"
}
if(p<0.01 && p>=0.001)
{
result<-"p<0.01"
}
if(p<0.001 && p>=0.0001)
{
result<-"p<0.001"
}
if(p<0.0001)
{
result<-"p<0.0001"
}
result
})
email_address_1 <- eventReactive(input$email_click_1, {
print("[email protected]")
})
output$email_out_1 <- renderText({
email_address_1()
})
email_address_2 <- eventReactive(input$email_click_2, {
print("[email protected]")
})
output$email_out_2 <- renderText({
email_address_2()
})
}