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server.R
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#Installing packages
library(data.table)
library(shiny)
library(shinyjs)
library(shinyBS)
library(httr)
library(DT)
library(RCurl)
library(BiocManager)
library(GEOquery)
library(limma)
library(oligo)
library(annotate)
library(ggplot2)
source('helpers.R')
#setwd('../GSE')
shinyServer(function(input, output) {
shinyjs:: useShinyjs() # Include shinyjs
shinyjs::showElement("wait_msg5")
#Deleting files from working directory
print(paste0("working directory is ", getwd()))
currentDir<- "./"
ProjectDir<- dir (path = currentDir, pattern ="^Project", full.names = TRUE)
#unlink(paste0(ProjectDir,"*"), recursive = TRUE)
unlink(file.path(getwd(),c("*.CEL", "*.gz", "*.txt.gz","*.txt","*.jpg", "*.png", "*.html", "*.tar", "*.zip")))
print("*.CEL, *.tar, *.gz, *.jpg,*.png, *.html, *.txt.gz, *.txt, and *.zip are gone")
print(list.files(path = getwd(), all.files = T, full.names = T, include.dirs = T))
wwwDir<- "./www"
unlink(file.path(wwwDir,c ("*.html")))
unlink(file.path(wwwDir,c ("*.zip")))
#changing the file size limit
options(shiny.maxRequestSize = 50*2048^2) #25MB per file is allowed
#preventing the shiny app from being grayed out
autoInvalidate <- reactiveTimer(10000)
observe({
autoInvalidate()
cat(".")
})
#View user's guide
output$users_guide_msg <- renderUI({tagList(tags$p(tags$b(tags$h2("User's guide"))))})
output$users_guide_view <- renderUI({ tags$iframe(style="height:800px; width:100%", src="Users_guide.pdf")})
#Links to GitHub and DockerHub
GitHub <- a ("GitHub link", href="https://github.com/annakatsiki/dexplore", target='_blank')
output$GitHub <- renderUI({tagList(tags$p(GitHub))})
DockerHub <- a ("DockerHub link", href="https://hub.docker.com/r/akatsiki/dexplore", target='_blank')
output$DockerHub <- renderUI({tagList(tags$p(DockerHub))})
#About us
output$about <- renderUI({tagList(tags$p(tags$b("DExplore: an online tool for detecting differentially expressed genes from mRNA microarray experiments")),
tags$p(tags$u("Anna D. Katsiki"), tags$sup("1"),", Pantelis E. Karatzas", tags$sup("2"), ", Alexandros A. Georgakilas", tags$sup("3"), "and Constantinos E. Vorgias", tags$sup("1")),
tags$p(tags$sup("1"), "National and Kapodistrian University of Athens, Faculty of Biology, Department of Biochemistry and Molecular Biology, Panepistimiopolis Athens, Greece"),
tags$p(tags$sup("2"),"National Technical University of Athens (NTUA), School of Chemical Engineering, Department of Process Analysis and Plant Design, Unit of Process Control & Informatics, Zografou Campus, Athens, Greece"),
tags$p(tags$sup("3"),"National Technical University of Athens (NTUA), School of Applied Mathematical and Physical Sciences, Physics Department, DNA Damage Laboratory, Zografou Campus, Athens, Greece"))})
#check if all mandatory fields have a value
observe({
mandatoryFilled <- vapply(fieldsMandatory, function(x) {!is.null(input[[x]]) && input[[x]] != ""}, logical(1))
mandatoryFilled <- all(mandatoryFilled)
mandatory_upload_Filled <- vapply(fieldsMandatory_up, function(x) {!is.null(input[[x]])},logical(1))
mandatory_upload_Filled <- all(mandatory_upload_Filled)
#enable/disable the submit button
shinyjs::toggleState(id = "go", condition = mandatoryFilled)
shinyjs::toggleState(id = "up_go", condition = mandatory_upload_Filled)
})
#after submission of a GSE accession number
observeEvent (input$go, {
print("submit")
shinyjs::hideElement ("firstInput1")
shinyjs::showElement ("wait_msg4")
shinyjs::hideElement ("firstInput2")
shinyjs::showElement ("firstRun")
shinyjs::disable ("GSE")
shinyjs::disable ("go")
shinyjs::disable("up_go")
shinyjs::showElement ("submit_msg")
shinyjs::hideElement ("error")
shinyjs::showElement ("wait_msg")
shinyjs::showElement("wait_msg5")
#GSE accession number validation and downloading
accession<- input$GSE
print(paste0("The length of input$GSE is ", nchar(accession)))
ftp<- createFtp(accession)
if (validation(ftp)) {
link <-createLink(input$GSE)
gse<- createGSE(input$GSE)
output$url <- renderUI({tagList(tags$p("URL link:", link))})
shinyjs::showElement ("goToNextTab")
#Choosing platform
plat_choices <- c()
choose_platform <- FALSE
shinyjs::showElement("choosing_platform")
shinyjs::hideElement ("wait_msg4")
if (length(gse)>0) {
choose_platform <- TRUE
for (i in 1:length(gse)) { plat_choices <- c(plat_choices, annotation(gse[[i]])) } #class(plat_choices) >>> character
}
plat_df <- as.data.frame(plat_choices)
for (i in 1:length(plat_choices)) { row.names(plat_df)[i]<- plat_choices[i] }
output$choosePlatform<- renderUI({
selectInput ( inputId = "choosePlatform",
label = "Choose the platform for the analysis",
choices = row.names(plat_df),
selected = row.names(plat_df)[1])
})
observeEvent(input$submit_platform, {
shinyjs::disable("submit_platform")
cross<- FALSE
if (length(gse)>1) {
cross <- TRUE
idx <- match(input$choosePlatform, plat_choices)
cat("Selected platform: ")
cat(idx)
cat("\n")
}
if (length(gse)==1) {
idx<-1
}
phenodata<- pData(gse[[idx]])
df<- data.frame(sample= phenodata$geo_accession, treatment=phenodata$title)
gse_names <- paste0("GSE",input$GSE)
dir <- paste0("./",gse_names)
shinyjs::showElement ("data_table")
#pdata, pDataRel, newpdata
for (i in 1:ncol(phenodata)) {
name <- paste(colnames(phenodata[i]))
if (name == "source_name_ch1") {
colnames(phenodata)[i] <- "source"
name <- "source"
}
col <- phenodata[i]
grp <- grep("characteristics", name)
if (i == 1) {
if (length(grp) == 0) {
col <- sub("^", paste(name, ": ", sep = ""), col[, c(name)])
}
pdata <- as.data.frame(col)
colnames(pdata)[i] <- name
}
else {
if (length(grp) == 0) {
col <- sub("^", paste(name, ": ", sep = ""), col[, c(name)])
}
pdata <- cbind(pdata, col)
colnames(pdata)[i] <- name
}
}
pdata <- as.data.frame(pdata)
pdataRel <- pdata #relevant P data to be formed
nameVec = c()
cat("************************************************* \n")
show(head(pdata))
for (i in names(pdata)) {
col <- pdata[, c(i)]
title <- strsplit(paste(col[1]), ":")[[1]][1]
numGroups <- length(unique(col))
if ((numGroups < length(col) && numGroups > 1 && !(title %in% nameVec)) || title == "title") {
nameVec <- c(nameVec, title)
cat(
"Column name: ",
title,
"\n",
"Number of different groups: ",
numGroups,
"\n",
sep = ""
)
if (numGroups < 21) {
cat("Here is a list of groups and their frequencies: ",
"\n",
"\n")
}
if (numGroups >= 21) {
cat("Too many groups to display. Here is a sample of the groups: ",
"\n")
}
freq_table <- as.data.frame(table(col))
freq_table <- freq_table[order(-freq_table$Freq), c(1, 2)]
colnames(freq_table) <- c(title, "Freq")
for (j in 1:21) {
if (j > numGroups) {
break
}
group <- paste(freq_table[j, 1])
cat(group, ": ", paste(freq_table[j, 2]), "\n")
}
cat("===============================================",
"\n")
}
else {
pdataRel[, c(i)] <- NULL
}
}
colnames(pdataRel) <- nameVec
pdata <- as.data.frame(pdataRel, stringsAsFactors = FALSE)
newpdata <- sapply(pdata, as.character)
show(head(pdata))
if (ncol(pdata) == 0) {
pdata = as.data.frame(matrix(
data = "",
nrow = nrow(pdata),
ncol = 1
))
newpdata = pdata
colnames(pdata[1]) = "No characteristics found"
colnames(newpdata[1]) = "No characteristics found"
}
else {
for (i in 1:(ncol(pdata))) {
for (j in 1:(nrow(pdata))) {cont <- as.character(strsplit(paste(pdata[j, i]), ":")[[1]])
newpdata[j, i] <- cont[length(cont)]}
}
}
rownames(newpdata) <- phenodata$geo_accession
newpdata <- gsub("^ ", "", newpdata)
output$df<- renderDataTable(newpdata, server = TRUE)
#Choosing the comparison to be performed
shinyjs::showElement ("comparison")
output$compBut <- renderUI ({
radioButtons ( inputId = "comp2",
label = "Select the criterion of the comparison",
choices = (colnames(newpdata))) })
shinyjs::enable("submit_comp")
observeEvent(input$submit_comp, {
shinyjs::disable("submit_comp")
shinyjs::showElement ("ctr")
output$comp_ctr <- renderUI ({
radioButtons ( inputId = "ctr",
label = "Select which samples should be treated as control",
choices = (unique(newpdata[,input$comp2]))
)
})
shinyjs::enable("submit_ctr")
observeEvent(input$submit_ctr, {
shinyjs::disable("submit_ctr")
shinyjs::showElement ("tr")
output$comp_tr <- renderUI ({
radioButtons ( inputId = "tr",
label = "Select the samples to which control are to be compared",
choices = (unique(newpdata[which(!(newpdata[,input$comp2] %in% input$ctr)),input$comp2]))
)
})
shinyjs::enable("submit_tr")
observeEvent(input$submit_tr, {
shinyjs::disable("submit_tr")
print("comp_submitted")
index <- which(colnames(pdataRel)==input$comp2)
print(paste0("index is ",index))
shinyjs::disable("submit_comp")
shinyjs::showElement ("btns")
shinyjs::showElement("run")
if ("replicate" %in% colnames(newpdata)) { }
else {
replicate<- matrix(data = integer(), nrow = dim(newpdata)[1], ncol = length(unique(newpdata[, index])))
row.names(replicate) <- row.names(newpdata)
colnames(replicate) <- unique(newpdata[, index])
replicate[1,1]<-1
for (i in 2:dim(newpdata)[1]){
c<- which(colnames(replicate)==newpdata[i, index], arr.ind = T)
if (! is.na(replicate[i-1,c])) {replicate[i,c]<- replicate[i-1,c]+1}
else {replicate[i,c]<-1}
}
replicate
replicate<- as.integer(replicate)
replicate<- replicate[!is.na(replicate)]
newpdata <- cbind(newpdata, replicate= replicate)
}
###Creating targets.txt
targets<-data.frame(newpdata)
#run the analysis
observeEvent(input$anls,{
print("running the analysis")
shinyjs::disable ("changes_done")
shinyjs::disable ("anls")
shinyjs::showElement ("wait_msg2")
shinyjs::hideElement ("firstRun")
shinyjs::showElement ("wait_msg3")
### downloading and untarring RAW files
getGEOSuppFiles(gse_names, makeDirectory = FALSE)
tar_file <- paste0(gse_names,"_RAW.tar")
untar(tar_file, exdir = getwd())
cF<- data.frame(list.files(pattern = "CEL[.]gz$"), stringsAsFactors = FALSE)
print(cF)
i<-1
while (i<= dim(cF)[1]) {
gunzip(cF[i,], remove = FALSE, overwrite = T)
print(i)
i = i+1
}
###Reading targets.txt
print(targets)
###Reading Affy
lc<- list.celfiles(path = getwd(), full.names = TRUE)
celFiles<- c()
for (i in 1:length(phenodata$geo_accession)) {
celFiles<- c(celFiles, grep(phenodata$geo_accession[i], lc, value = T))
}
Data<- read.celfiles(celFiles)
print("Data is ready")
Data_rma<-rma(Data)
print("Data_rma is ready")
e <- exprs(Data_rma)
print("e is ready")
print(dim(e))
###NonSpecific Filtering-pOverA
num_ctr <- length(which(newpdata[,index] %in% input$ctr)) ###number of control samples
num_tr <- length(which(newpdata[,index] %in% input$tr)) ###number of treated samples
prop<- (num_tr/(num_tr+num_ctr))
print(paste("num_ctr is", num_ctr, sep = " "))
print(paste("num_tr is", num_tr, sep = " "))
print(paste("prop is", prop, sep = " "))
f2_Data_rma<-filter1(Data_rma,prop,log2(100)) ###prop=number of treated/sum of samples
dim(f2_Data_rma)
f2_e<- filter2(e, prop, log2(100))
dim(f2_e)
###NonSpecific Filtering-shorth
row.mean <- esApply(Data_rma,1,mean)
sh <- shorth(row.mean)
#hist(e)
#abline(v=sh, col="red")
f3_Data_rma <- Data_rma[row.mean >=sh,]
dim(f3_Data_rma)
###Filtering Results - Comparison
v<- c(dim(Data_rma)[1], dim(f2_Data_rma)[1], dim(f3_Data_rma)[1])
filt.matrix<- data.frame(features=v, filt.method = c("before filt", "pOverA", "shorth"))
print(filt.matrix)
###Linear Modelling
cond<-pdataRel[,index]
batch<-as.factor(targets$replicate)
design<-model.matrix(~0 + cond + batch)
print("the design matrix before:")
print (design)
x_index <- grep(input$ctr, colnames(design), ignore.case = T) ###
y_index <- grep(input$tr, colnames(design), ignore.case = T) ###
colnames(design)<-gsub("cond","",colnames(design))
colnames(design)<-gsub("atch","",colnames(design))
colnames(design)<- make.names(colnames(design))
print("the design matrix after:")
print (design)
fit<- lmFit(f2_e, design)
print(head(fit$coefficients))
x<- colnames(design)[x_index] ###
y<- colnames(design)[y_index] ###
s<- paste0(x,y,"=",y,"-",x)
contr<- do.call(makeContrasts, c(s,list(levels = design)))
fit2<-contrasts.fit(fit, contr)
fit3<-eBayes(fit2,trend=T, robust = T)
top<-topTable(fit3, number = nrow(fit3), adjust.method = input$adjMet, sort="none")
head(top)
dim(top)
###Significant DE genes
print(input$adjMet)
print(input$adjPval)
print(input$logFC)
res<-decideTests(fit3,method = "separate", adjust.method = input$adjMet, p.value = input$adjPval, lfc = input$logFC)
# PLOTS
# Histogram
png("histogram.png")
par(font.lab=2, font.axis=2)
hist(top$adj.P.Val, col = "darkgrey", border = "white",
breaks = 100,
xlab = "P-adj", ylab = "Number of probes",
main = paste0("GSE", input$GSE, " adjusted P-value distribution"),
xaxt= "n"
)
axis(side = 1, at = seq(0, 1, length.out = 5),
labels = seq(0, 1, length.out = 5), tck= 0)
dev.off()
# Boxplot
png("boxplot.png")
ctr_index<-which(newpdata[,index] %in% input$ctr)
tr_index<-which(newpdata[,index] %in% input$tr)
groups<- c("Control", "Treated")
test_f2_e<-f2_e
sample_tags <- rep("Unknown", ncol(test_f2_e))
for (i in ctr_index) {
sample_tags[i] <- "Control"
}
for (i in tr_index) {
sample_tags[i] <- "Treated"
}
others <- setdiff(colnames(test_f2_e), c("Control", "Treated"))
colnames(test_f2_e)<-sample_tags
unknown_v<-(which(colnames(test_f2_e)=="Unknown"))
cols<-ifelse(colnames(test_f2_e)=="Control", "lightgreen",
ifelse(colnames(test_f2_e)=="Treated", "red", "lightgrey"))
xlabels<-rownames(targets)
par(mar=c(5.1, 4.1, 4.1, 8.1), xpd=TRUE, bty="l", font.lab=2, font.axis=2)
boxplot(f2_e, ylab="Expression Value", xaxt="n",
col=cols,
main = paste0("Boxplot of GSE", input$GSE, axes=0)
)
text(x = 1:length(xlabels), y = par("usr")[3] - 0.5,
labels = xlabels, srt=45, font=2,
adj = c(1, 1), xpd = TRUE, cex = 1)
if (length(unknown_v)>0){
leg <- legend("topright", pch = 19, col = c("lightgreen", "red", "lightgray"),
legend = c(input$ctr, input$tr, "other"),
fill = c("lightgreen", "red", "lightgray"),
plot = FALSE)
legend(x = (leg$rect$left + leg$rect$w) * 0.95, y = leg$rect$top,
pch = 19, col = c("lightgreen", "red", "lightgray"),
legend = c(input$ctr, input$tr, "other"),
bg= "transparent", border = NA,
horiz = FALSE, box.lwd = 0, box.col = "white")
} else{
leg <- legend("topright", pch = 19, col = c("lightgreen", "red"),
legend = c(input$ctr, input$tr),
fill = c("lightgreen", "red"),
plot = FALSE)
legend(x = (leg$rect$left + leg$rect$w) * 0.95, y = leg$rect$top,
pch = 19, col = c("lightgreen", "red"),
legend = c(input$ctr, input$tr),
bg= "transparent", border = NA,
horiz = FALSE, box.lwd = 0, box.col = "white")
}
dev.off()
###Keeping DE genes to a data.frame
sign<-subset(top, abs(top$logFC)> input$logFC & top$adj.P.Val < input$adjPval)
dim(sign)
print(paste0("Before annotation: nrow(sign) is ", nrow(sign)))
print("head of sign is: ")
print(head(sign))
###Message for zero DE genes
if (nrow(sign)==0){
print("nrow of sign is 0 - no DEGs")
shinyjs::hideElement ("wait_msg3")
shinyjs::hideElement ("wait_msg2")
shinyjs::showElement ("msg")
shinyjs::showElement ("noDEGs")
}
else {
###Annotation using annotate
dict <- read.table("./plat_dict_full.tsv",
header = T, sep = "\t", colClasses = "character")
gse_acc <- paste0("GSE", input$GSE)
gse<- getGEO(gse_acc, destdir = getwd(), GSEMatrix = T, getGPL = F)
a <- annotation(gse[[idx]])
p<- grep(a, dict$Accession, fixed = T) #ignore.case = T,
x<- dict$db[p]
y<-x[1]
set<- c("-", "?", "")
if(!is.element(y,set)){ #checking if annotation file exists
if(!require(y, character.only = T)) {
BiocManager::install(y, character.only = T, dependencies = T, ask=FALSE)
library(y, character.only = T)
}
sign<- annotf(sign, y)
top<- annotf(top,y)
# PCA
tr_f2_e <- t(f2_e)
# Create a vector indicating the treatment group for each sample
sample_groups <- newpdata[, index, drop = FALSE]
# Use the ctr_index and tr_index to subset the columns of tr_f2_e
ctr_index<-which(newpdata[,index] %in% input$ctr)
tr_index<-which(newpdata[,index] %in% input$tr)
ctr_samples <- tr_f2_e[ctr_index, ]
trt_samples <- tr_f2_e[tr_index, ]
# Combine the control and treatment samples
selected_samples <- rbind(ctr_samples, trt_samples)
# Subset the sample groups vector accordingly
selected_sample_groups <- c(sample_groups[ctr_index], sample_groups[tr_index])
# Perform PCA
pca_result <- prcomp(selected_samples)
#print(summary(pca_result))
### Scree plot
#Calculate variance explained as percentage and cumulative variance
variance_explained_percent <- pca_result$sdev^2 / sum(pca_result$sdev^2) * 100
cumulative_variance <- cumsum(pca_result$sdev^2) / sum(pca_result$sdev^2)
# Create data frame for scree plot
scree_data <- data.frame(
PC = 1:length(pca_result$sdev),
Variance_Explained = variance_explained_percent,
Cumulative_Variance = cumulative_variance
)
# Plot scree plot
scree_plot <- ggplot(scree_data, aes(x = PC)) +
geom_line(aes(y = Variance_Explained, color = "Variance Explained")) +
geom_line(aes(y = Cumulative_Variance, color = "Cumulative Variance Explained")) +
scale_color_manual(values = c("black", "red")) +
labs(title = paste0("Scree Plot - GSE", input$GSE), x = "Principal Component", y = "Variance Explained (%)") +
theme_minimal() + ### change for uploaded data
theme(
plot.background = element_rect(fill = "white", color = NA), # Set entire plot background to white
panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank(), # Remove minor grid lines
panel.border = element_blank(), # Remove border
axis.line = element_line(color = "black"), # Add axis lines with black color
axis.text = element_text(color = "black"), # Set axis text color to black
axis.title = element_text(color = "black"), # Set axis title color to black
plot.title = element_text(color = "black"), # Set plot title color to black
plot.margin = margin(1, 1, 1, 1, "cm") # Set plot margins
) +
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
axis.title = element_text(face = "bold"), # Bold axis labels
axis.title.x = element_text(hjust = 0.5), # Centered x-axis label
axis.title.y = element_text(hjust = 0.5) # Centered y-axis label
)
# Save scree plot
ggsave("scree_plot.png", plot = scree_plot, width = 8, height = 6, units = "in", dpi = 300)
### PCA_scores_plot
# Extract the scores (PC1 and PC2) and combine with sample names
pc_df <- data.frame(
PC1 = pca_result$x[,1],
PC2 = pca_result$x[,2],
PC3 = pca_result$x[,3],
Sample_Group = ifelse(seq_along(rownames(pca_result$x)) %in% ctr_index, "Control", "Treated")
)
# Plot PCA scores with sample groups
pca_scores_plot <- ggplot(pc_df, aes(x = PC1, y = PC2, color = Sample_Group)) +
geom_point() +
labs(title = paste0("PCA Plot with Sample Groups - GSE", input$GSE), x = "PC1", y = "PC2") + ### change for uploaded data
scale_color_manual(values = c("Control" = "blue", "Treated" = "red")) + ### group names
theme_minimal() +
theme(
plot.background = element_rect(fill = "white", color = NA), # Set entire plot background to white
panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank(), # Remove minor grid lines
panel.border = element_blank(), # Remove border
axis.line = element_line(color = "black"), # Add axis lines with black color
axis.text = element_text(color = "black"), # Set axis text color to black
axis.title = element_text(color = "black"), # Set axis title color to black
plot.title = element_text(color = "black"), # Set plot title color to black
plot.margin = margin(1, 1, 1, 1, "cm") # Set plot margins
) +
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
axis.title = element_text(face = "bold"), # Bold axis labels
axis.title.x = element_text(hjust = 0.5), # Centered x-axis label
axis.title.y = element_text(hjust = 0.5) # Centered y-axis label
)
# Save PCA scores plot
ggsave("pca_scores_plot.png", plot = pca_scores_plot +
theme(panel.background = element_rect(fill = "white")),
width = 8, height = 6, units = "in", dpi = 300)
### Biplot
# Extract PCA scores and loadings
pc_scores <- as.data.frame(pca_result$x)
pc_loadings <- as.data.frame(pca_result$rotation)
annotated_pc_loadings <- annotf(pc_loadings, y)
# Filter out NAs
pc_loadings_no_na <- no_annotf(annotated_pc_loadings)
# Filter out duplicates
pc_loadings_filtered <- unFilt(annotated_pc_loadings$gene_symbol, pc_loadings_no_na)
# Choose top genes contributing to PC1 and PC2
top_genes <- 10 # Number of top genes to select
top_genes_pc1 <- order(abs(pc_loadings_filtered$PC1), decreasing = TRUE)[1:top_genes]
top_genes_pc2 <- order(abs(pc_loadings_filtered$PC2), decreasing = TRUE)[1:top_genes]
Sample_Group = ifelse(seq_along(rownames(pca_result$x)) %in% ctr_index, "Control", "Treated")
# Extract gene symbols
top_genes_symbols_pc1 <- pc_loadings_filtered$gene_symbol[top_genes_pc1]
top_genes_symbols_pc2 <- pc_loadings_filtered$gene_symbol[top_genes_pc2]
# Calculate the percentage of variance explained for PC1 and PC2
variance_explained <- round(pca_result$sdev^2 / sum(pca_result$sdev^2) * 100, 2)
# Plot PCA loadings with biplot
biplot <- ggplot() +
labs(title = paste0("Biplot: PCA Loadings - GSE", input$GSE), ### change for uploaded data
x = paste0("PC1 (", round(variance_explained[1], 2), "%)"),
y = paste0("PC2 (", round(variance_explained[2], 2), "%)")) +
theme_minimal() +
theme(
plot.background = element_rect(fill = "white", color = NA), # Set entire plot background to white
panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank(), # Remove minor grid lines
panel.border = element_blank(), # Remove border
axis.line = element_line(color = "black"), # Add axis lines with black color
axis.text = element_text(color = "black"), # Set axis text color to black
axis.title = element_text(color = "black"), # Set axis title color to black
plot.title = element_text(color = "black"), # Set plot title color to black
plot.margin = margin(0.1, 0.1, 0.1, 0.1, "cm") # Set plot margins
) +
scale_x_continuous(expand = expansion(mult = c(0.1, 0.1))) +
scale_y_continuous(expand = expansion(mult = c(0.1, 0.1))) +
theme(panel.border = element_rect(color = "black", fill = NA, linewidth = 1),
plot.margin = margin(0.1, 0.1, 0.1, 0.1, "cm")) +
coord_fixed(ratio = 1)
# Add arrows for top genes contributing to PC1
biplot <- biplot +
geom_segment(
data = data.frame(
x = 0, y = 0,
xend = pc_loadings_filtered[top_genes_pc1, "PC1"],
yend = pc_loadings_filtered[top_genes_pc1, "PC2"],
gene_symbol = top_genes_symbols_pc1
),
aes(x = 0, y = 0, xend = xend, yend = yend),
arrow = arrow(length = unit(0.3, "cm")),
color = "darkgreen"
) +
geom_text(
data = data.frame(
x = pc_loadings_filtered[top_genes_pc1, "PC1"],
y = pc_loadings_filtered[top_genes_pc1, "PC2"],
gene_symbol = top_genes_symbols_pc1
),
aes(x = x, y = y, label = gene_symbol),
vjust = 1.5,
hjust = -0.5,
color = "darkgreen",
size = 2
)
# Add arrows for top genes contributing to PC2
biplot <- biplot +
geom_segment(
data = data.frame(
x = 0, y = 0,
xend = pc_loadings_filtered[top_genes_pc2, "PC1"],
yend = pc_loadings_filtered[top_genes_pc2, "PC2"],
gene_symbol = top_genes_symbols_pc2
),
aes(x = 0, y = 0, xend = xend, yend = yend),
arrow = arrow(length = unit(0.3, "cm")),
color = "darkorange"
) +
geom_text(
data = data.frame(
x = pc_loadings_filtered[top_genes_pc2, "PC1"],
y = pc_loadings_filtered[top_genes_pc2, "PC2"],
gene_symbol = top_genes_symbols_pc2
),
aes(x = x, y = y, label = gene_symbol),
vjust = 1.5,
hjust = -0.5,
color = "darkorange",
size = 2
)
# Customize axis labels to be bold and centered
biplot <- biplot +
theme(axis.title.x = element_text(face = "bold", hjust = 0.5),
axis.title.y = element_text(face = "bold", hjust = 0.5),
plot.title = element_text(face = "bold", hjust = 0.5))
# Save biplot
ggsave("biplot.png", plot = biplot +
theme(panel.background = element_rect(fill = "white")),
width = 15, height = 8, units = "in", dpi = 300)
#Volcano plot
library(plotly)
# Plot each dataset with its corresponding color
# Create separate datasets for each category
upregulated <- top[top$logFC > input$logFC & top$adj.P.Val < input$adjPval, ]
downregulated <- top[top$logFC < -input$logFC & top$adj.P.Val < input$adjPval, ]
not_significant <- top[!(top$logFC > input$logFC & top$adj.P.Val < input$adjPval) & !(top$logFC < -input$logFC & top$adj.P.Val < input$adjPval), ]
max_y <- max(-log10(c(upregulated$adj.P.Val, downregulated$adj.P.Val, not_significant$adj.P.Val)))
# Create custom hover text including gene symbol, log2FC, and adjusted p-value
up_text <- paste("Gene Symbol: ", upregulated$gene_symbol, "<br>",
"log2FC: ", format(round(upregulated$logFC, 2), nsmall = 2), "<br>",
"Adjusted P-value: ", format(round(upregulated$adj.P.Val, 3), nsmall = 3))
down_text <- paste("Gene Symbol: ", downregulated$gene_symbol, "<br>",
"log2FC: ", format(round(downregulated$logFC, 2), nsmall = 2), "<br>",
"Adjusted P-value: ", format(round(downregulated$adj.P.Val, 3), nsmall = 3))
not_significant_text <- paste("Gene Symbol: ", not_significant$gene_symbol, "<br>",
"log2FC: ", format(round(not_significant$logFC, 2), nsmall = 2), "<br>",
"Adjusted P-value: ", format(round(not_significant$adj.P.Val, 3), nsmall = 3))
# Create the plot with custom hover text
plot <- plot_ly() %>%
add_trace(data = upregulated, x = ~logFC, y = ~-log10(adj.P.Val), type = 'scatter', mode = 'markers',
marker = list(size = 4, color = 'red'), name = 'Upregulated', text = up_text, hoverinfo = "text") %>%
add_trace(data = downregulated, x = ~logFC, y = ~-log10(adj.P.Val), type = 'scatter', mode = 'markers',
marker = list(size = 4, color = 'blue'), name = 'Downregulated', text = down_text, hoverinfo = "text") %>%
add_trace(data = not_significant, x = ~logFC, y = ~-log10(adj.P.Val), type = 'scatter', mode = 'markers',
marker = list(size = 2, color = 'black'), name = 'Not significant', text = not_significant_text, hoverinfo = "text") %>%
layout(
title = list(text = paste0("Interactive Volcano Plot", input$GSE), y = 0.97),
xaxis = list(title = "log2 Fold Change", showgrid = FALSE, zeroline = FALSE),
yaxis = list(title = "-log10(Adjusted P-value)", showgrid = FALSE, linecolor= "black"),
hovermode = "closest",
legend = list(
orientation = "v",
x = 0.9,
y = 0.2,
xanchor = "left",
yanchor = "top",
bgcolor = "rgba(0,0,0,0)",
bordercolor = "rgba(0,0,0,0)",
borderwidth = 2
),
margin = list(l = 50, r = 50, b = 50, t = 50),
font = list(family = "Arial", size = 14, color = "black", weight = "bold"),
showlegend = TRUE,
yaxis2 = list(range = c(0, max_y + 1)),
print("volcano is ready")
)
htmlwidgets::saveWidget(plot, file = "volcanoplot.html")
# Heatmap
library(RColorBrewer)
library(heatmaply)
sel.top <- subset(top, top$adj.P.Val< input$adjPval & abs(top$logFC)> input$logFC)
#sel.top<- top[which(abs(top$logFC)> input$logFC & top$adj.P.Val < input$adjPval),]
sel.top<- annotf(sel.top, y)
sel.top<- unFilt(sel.top$gene_symbol, sel.top)
sel.top<- sel.top[is.na(sel.top$gene_symbol)==FALSE,]
sel.top<- sel.top[order(sel.top$logFC, decreasing = TRUE),]
f2_e.sel<- f2_e[rownames(sel.top), ]
keys<-sel.top$probeID
values<-sel.top$gene_symbol
l<- list()
for(i in 1:length(keys)){
l[keys[i]]<- values[i]
}
rownames(f2_e.sel)<- unlist(l[rownames(f2_e.sel)], use.names = F)
yellowblackblue <- colorRampPalette(c("dodgerblue", "black", "gold"))(n = 100)
data<- matrix(data = f2_e.sel, nrow = nrow(f2_e.sel))
hover_text <- matrix(paste("AveExpr:", round(data, 4)),
nrow = nrow(data), ncol = ncol(data))
#subsetting the expression value matrix to view only the ctr and treated samples
selcol<- c (which(newpdata[, index] %in% input$ctr),which(newpdata[, index] %in% input$tr))
heatdata <- f2_e.sel[,selcol]
heat<- heatmaply(heatdata, distfun = "pearson", hclust_method = NA,
colors = yellowblackblue, row_dend_left = T,
margins = c(50,50,70,0),
main = paste0("GSE",input$GSE), scale = "row", Colv = F,
label_names = c("Gene", "Sample", "Value"),
custom_hovertext = hover_text,
file = "./heatmap.html")
#browseURL("./heatmap.html")
files_to_zip<- c("volcanoplot.html",
"boxplot.png",
"histogram.png",
"scree_plot.png",
"pca_scores_plot.png",
"biplot.png",
"heatmap.html"
)
zip_filename<- paste0("GSE", input$GSE,"_plots.zip")
zip(zip_filename, files_to_zip)
output$down_plots <- downloadHandler(
filename = function() {
zip_filename
},
content = function(file) {
path_to_zip <- zip_filename
file.copy(path_to_zip, file)
}
)
print(paste0("nrow(sign) after annotf is ",nrow(sign)))
###Filtering for NA geneNames
sign<- sign[is.na(sign$gene_symbol)==FALSE,]
print(paste0("nrow(sign) after removing NAs is ",nrow(sign)))
###Filtering for unique geneNames
sign_un<- unFilt(sign$gene_symbol, sign)
print(paste0("DEGs table after unFilt:",nrow(sign_un)))
if (nrow(sign_un)==0){
###Message for zero DE genes
print("nrow of sign_un is 0 - no DEGs")
shinyjs::hideElement ("wait_msg3")
shinyjs::hideElement ("wait_msg2")
shinyjs::showElement ("msg")
shinyjs::showElement ("noDEGs")
}
else {
###Rendering and downloading DE genes list
print(paste0("If the number of rows of sign_un is >=1: ", nrow(sign_un)))
sign_un <- sign_un[order(sign_un$adj.P.Val),]
degs <- sign_un
degs_t<- sign_un
sign_un$gene_symbol <- paste0("<a href='", paste0("https://www.ncbi.nlm.nih.gov/gene?term=", sign_un$gene_symbol,
"[Gene Name] AND ", as.character(unique(phenodata$organism_ch1)),
"[Organism]"),
"' target='_blank'>", sign_un$gene_symbol,"</a>")
shinyjs::showElement ("DEGs")
sign_un<- DT::datatable( data = sign_un,
editable = FALSE,
colnames = c("probeID","gene symbol","logFC","AveExpr",
"t","P.Value","adj.P.Val","B"),
rownames = FALSE,
escape = FALSE,
extensions = c('Scroller' , 'Buttons'),
options = list (
autoWidth = FALSE,
dom = 'Blftrip',
deferRender = T,
scrollY = 750,
scroller = T,
buttons = list(list(extend = 'colvis', columns = c(2, 3, 4, 5, 6, 7)),
"copy", "print")
)
)%>% formatSignif(c('logFC','AveExpr','t','P.Value','adj.P.Val','B'),3)
gene_link <- paste0("<a href='", paste0("https://www.ncbi.nlm.nih.gov/gene?term=", degs$gene_symbol,
"[Gene Name] AND ", as.character(unique(phenodata$organism_ch1)),
"[Organism]"),
"' target='_blank'>", degs$gene_symbol,"</a>")
degs$gene_symbol <- gene_link
degs <- cbind(degs, url = gene_link)
output$DEGs <- DT::renderDataTable(sign_un, server = FALSE)
output$down_csv <- downloadHandler(
filename = function (){ paste0("GSE",input$GSE,"_DE_genes.csv")},
content = function (temp) { write.csv(degs, temp, row.names = FALSE)})
output$down_tsv <- downloadHandler(
filename = function (){ paste0("GSE",input$GSE,"_DE_genes.tsv")},
content = function (temp) { write.table(degs, temp, quote = FALSE, sep = "\t",row.names = FALSE) })
shinyjs::hideElement ("wait_msg2")
shinyjs::showElement ("msg")
shinyjs::hideElement ("wait_msg3")
shinyjs::showElement ("download")
shinyjs::enable ("download")
shinyjs::enable ("changes_done")
shinyjs::enable ("submit_comp")
shinyjs::enable ("anls")
shinyjs::hideElement ("wait_msg5")
shinyjs::showElement ("WebGestaltR_but")
library(WebGestaltR)
observeEvent(input$WebGestaltR,{
shinyjs::showElement("WebGestaltR_but")
shinyjs::disable("WebGestaltR")
shinyjs::showElement("choosing_organism")
output$chooseOrganism<- renderUI({
selectInput( inputId= "chooseOrganism",
label = "Choose the organism for the analysis",
choices = listOrganism()
)
})
shinyjs::enable("submit_Organism")
print("Choosing organism")
observeEvent(input$submit_Organism,{
shinyjs::disable("submit_Organism")
shinyjs::showElement("choosing_RefSet")
organismWebGestaltR<- input$chooseOrganism
output$chooseRefSet<- renderUI({
selectInput( inputId = "chooseRefSet",
label = "Choose the reference set for the analysis",
choices = listReferenceSet(organism = organismWebGestaltR))})
print("Choosing RefSet")
observeEvent(input$submit_RefSet,{
shinyjs::disable("submit_RefSet")
shinyjs::hideElement("wait_msg5")
shinyjs::showElement("wait_msg6")
shinyjs::hideElement("WebGestalt_down")
shinyjs::hideElement("WebGestalt_res")
shinyjs::hideElement("WebGestaltR_but")
shinyjs::hideElement("choosing_organism")
shinyjs::hideElement("choosing_RefSet")
library(zip)
#}
print("Running WebGestaltR")
genesymbol <- degs_t$gene_symbol
genesymbol <- as.character(genesymbol)
projectName <- paste0("GSE", input$GSE,"_WebGestalt")
outputDir <- currentDir
WebGestaltR(enrichMethod = "ORA", organism = input$chooseOrganism,
enrichDatabase = c("geneontology_Biological_Process", "geneontology_Molecular_Function",
"geneontology_Cellular_Component"),
interestGene= genesymbol, interestGeneType="genesymbol",
collapseMethod="mean", referenceSet= input$chooseRefSet,
minNum=10, maxNum=500, sigMethod="fdr", fdrMethod="BH", fdrThr=0.05,
reportNum=20, isOutput= T, outputDirectory=outputDir,
projectName=projectName, hostName= "https://www.webgestalt.org" )
HTMLfile<- list.files(path = paste0(outputDir,"/Project_",projectName), pattern = ".html")
print(paste0("HTML file is ",HTMLfile))
projectPath<- paste0(outputDir,"/","Project_", projectName)
print(paste0("project path is ", projectPath))
file.copy(from = paste0(projectPath,"/",HTMLfile),
to = paste0(outputDir,"/www"))
shinyjs::hideElement("wait_msg6")
shinyjs::showElement("WebGestalt_down")
shinyjs::showElement("WebGestalt_res")
output$WebGestalt <- renderUI({
tags$iframe(style="height:800px; width:100%", src=HTMLfile)
})
Webgestalt_files<- list.files(path = paste0(outputDir,"/Project_", projectName),
recursive = TRUE, full.names = TRUE)
zip::zipr(zipfile = "WebGestalt.zip", files = Webgestalt_files, include_directories=TRUE)
if (file.exists("WebGestalt.zip")) {print ("zip is ready!!!")}
# the zip file is in working directory, here C:/Users/Anna/Desktop/PhD/DExplore/GSE
else {print("Problem while zipping")}
})
output$down_WebGestalt <- downloadHandler(
filename = function() {paste0("Project_GSE", input$GSE, "_WebGestalt.zip")},
content = function(temp) {file.copy(from = paste0(getwd(),"/WebGestalt.zip"), to = temp)} )
}) #submit_Organism
}) #WebGestaltR
} #Rendering and downloading DE genes list
} # if (!is.element(y,set))
else { ### sto if(!is.element(y,set))
shinyjs::showElement("no_annotation")
###Rendering and downloading DE genes list WITHOUT ANNOTATION
sign<- no_annotf(sign)
dim(sign)
sign2 <- sign[order(sign$adj.P.Val),]
degs2<- sign2
shinyjs::showElement ("DEGs")
sign2<- DT::datatable( data = sign2,
editable = FALSE,
colnames = c("probeID","logFC","AveExpr","t","P.Value","adj.P.Val","B"),
rownames = FALSE,
extensions = c('Scroller' , 'Buttons'),
options = list (
autoWidth = FALSE,
dom = 'Blftrip',
deferRender = T,
scrollY = 750,
scroller = T,
buttons = list(list(extend = 'colvis', columns = c( 1, 2, 3, 4, 5, 6)),
"copy", "print")
)
)%>% formatSignif(c('logFC','AveExpr','t','P.Value','adj.P.Val','B'),3)
output$DEGs <- DT::renderDataTable(sign2, server = FALSE)
output$down_csv <- downloadHandler(
filename = function (){ paste0("GSE",input$GSE,"_DE_genes.csv")},
content = function (temp) { write.csv(degs2, temp, row.names = FALSE)})
output$down_tsv <- downloadHandler(
filename = function (){ paste0("GSE",input$GSE,"_DE_genes.tsv")},
content = function (temp) { write.table(degs2, temp, quote = FALSE, sep = "\t",row.names = FALSE) })
output$down_pdf <- downloadHandler(
filename = function (){ paste0("GSE",input$GSE,"_DE_genes.pdf")},
content = function ( temp ) {
createPdf(degs2)
file.copy ("test.pdf", temp)},
contentType = "application/pdf")