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server.R
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server.R
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#Define server logic to read selected file ----
server <- function(input, output, session) {
options(shiny.maxRequestSize=100*1024^2)## Set maximum upload size to 100MB
# Show elements on clicking Start analysis button
observeEvent(input$analyze ,{
if(input$analyze==0){
return()
}
shinyjs::hide("quickstart_info")
shinyjs::show("downloadbox")
})
observeEvent(input$analyze ,{
if(input$analyze==0 ){
return()
}
shinyjs::show("results_tab")
})
observeEvent(input$analyze ,{
if(input$analyze==0 ){
return()
}
shinyjs::show("qc_tab")
})
observeEvent(input$analyze ,{
if(input$analyze==0 ){
return()
}
shinyjs::show("enrichment_tab")
})
# observeEvent(input$analyze,{
# shinyjs::hide("howto")
# })
# observe({
# if(input$body=="info"){
# output$howto<-renderUI({
# box(width=12,
# includeMarkdown("www/Info.Rmd")
# )
# })
#
# observeEvent(input$analyze,{
# output$howto<-renderUI({NULL})
# })
# }
# else{
# output$howto<-renderUI({NULL})
# }
# })
## Shinyalert
observeEvent(input$analyze ,{
if(input$analyze==0 ){
return()
}
shinyalert("In Progress!", "Data analysis has started, wait until table and plots
appear on the screen", type="info",
closeOnClickOutside = TRUE,
closeOnEsc = TRUE,
timer = 10000) # timer in miliseconds (10 sec)
})
observe({
if (input$tabs_selected=="demo"){
shinyalert("Demo results loading!...", "Wait until table and plots
appear on the screen", type="info",
closeOnClickOutside = TRUE,
closeOnEsc = TRUE,
timer = 6000)
}
})
####======= Render Functions
output$volcano_cntrst <- renderUI({
if (!is.null(comparisons())) {
df <- SummarizedExperiment::rowData(dep())
cols <- grep("_significant$",colnames(df))
selectizeInput("volcano_cntrst",
"Comparison",
choices = gsub("_significant", "", colnames(df)[cols]))
}
})
##comparisons
output$contrast <- renderUI({
if (!is.null(comparisons())) {
df <- SummarizedExperiment::rowData(dep())
cols <- grep("_significant$",colnames(df))
selectizeInput("contrast",
"Comparison",
choices = gsub("_significant", "", colnames(df)[cols]))
}
})
output$contrast_1 <- renderUI({
if (!is.null(comparisons())) {
df <- SummarizedExperiment::rowData(dep())
cols <- grep("_significant$",colnames(df))
selectizeInput("contrast",
"Comparison",
choices = gsub("_significant", "", colnames(df)[cols]))
}
})
output$downloadTable <- renderUI({
if(!is.null(dep())){
selectizeInput("dataset",
"Choose a dataset to save" ,
c("Results","Original_matrix",
"Imputed_matrix",
"Full_dataset"))
}
})
output$downloadButton <- renderUI({
if(!is.null(dep())){
downloadButton('downloadData', 'Save')
}
})
output$downloadZip <- renderUI({
if(!is.null(dep())){
downloadButton('downloadZip1', 'Download result plots')
}
})
output$downloadreport <- renderUI({
if(!is.null(dep())){
downloadButton('downloadReport', 'Download Report')
}
})
output$downloadPlots <- renderUI({
if(!is.null(dep())){
downloadButton('downloadPlots1', 'Download Plots')
}
})
## Read input files on shiny server
## NOTE: have to use reactive framework, otherwise throws out error
# observeEvent(input$analyze,{
# maxquant_data<-reactive({
# inFile<-input$file1
# if(is.null(inFile))
# return(NULL)
# read.table(inFile$datapath,
# header = TRUE,
# fill= TRUE, # to fill any missing data
# sep = "\t"
# )
# })
# })
## make reactive elements
maxquant_data_input<-reactive({NULL})
exp_design_input<-reactive({NULL})
exp_design_example<-reactive({NULL})
maxquant_data_example<-reactive({NULL})
maxquant_data_input<-eventReactive(input$analyze,{
inFile<-input$file1
if(is.null(inFile))
return(NULL)
temp_data<-read.table(inFile$datapath,
header = TRUE,
fill= TRUE, # to fill any missing data
sep = "\t",
quote = ""
)
validate(maxquant_input_test(temp_data))
return(temp_data)
})
# observeEvent(input$analyze,{
# exp_design<-reactive({
# inFile<-input$file2
# if (is.null(inFile))
# return(NULL)
# temp_df<-read.table(inFile$datapath,
# header = TRUE,
# sep="\t",
# stringsAsFactors = FALSE)
# exp_design_test(temp_df)
# temp_df$label<-as.character(temp_df$label)
# return(temp_df)
# })
# })
exp_design_input<-eventReactive(input$analyze,{
inFile<-input$file2
if (is.null(inFile))
return(NULL)
temp_df<-read.table(inFile$datapath,
header = TRUE,
sep="\t",
stringsAsFactors = FALSE)
exp_design_test(temp_df)
temp_df$label<-as.character(temp_df$label)
temp_df$condition<-trimws(temp_df$condition, which = "left")
temp_df$condition <- temp_df$condition %>% gsub("[^[:alnum:]|_]+", "_",.) # auto fix special characters
return(temp_df)
})
### Load data from Rdata
# observeEvent(input$load_data,{
# example_data<-reactive({
# load("data/example_data.RData", envir = .GlobalEnv)
# })
# maxquant_data<-reactive({example_data[1]})
# exp_design<-reactive({example_data[2]})
# env<-reactive({
# LoadToEnvironment("data/example_data.RData", env = globalenv())
# })
#
# observeEvent(input$load_data,{
# # message(env()[["exp_design"]])
# maxquant_data_example<-reactive({
# env()[["maxquant_output"]]
# })
# })
# observeEvent(input$load_data,{
# exp_design_example<-reactive({
# env()[["exp_design"]]
# })
# })
# }) ## leave this commented
#
# maxquant_data<-eventReactive(input$load_data,{
# env()[['maxquant_output']]
# })
#
# exp_design<-eventReactive(input$load_data,{
# env()[['exp_design']]
# })
### Reactive components
processed_data<- reactive({
## check which dataset
if(!is.null (maxquant_data_input() )){
maxquant_data <- reactive({maxquant_data_input()})
}
if(!is.null (exp_design_input() )){
exp_design<-reactive({exp_design_input()})
}
message(exp_design())
if(any(grepl('+',maxquant_data()$Reverse))){
filtered_data<-dplyr::filter(maxquant_data(),Reverse!="+")
}
else{filtered_data<-maxquant_data()}
if(any(grepl('+',filtered_data$Potential.contaminant))){
filtered_data<-dplyr::filter(filtered_data,Potential.contaminant!="+")
}
if(any(grepl('+',filtered_data$Only.identified.by.site))){
filtered_data<-dplyr::filter(filtered_data,Only.identified.by.site!="+")
}
if(input$single_peptide==TRUE){
filtered_data <-filtered_data
}
else{filtered_data<-dplyr::filter(filtered_data,as.numeric(Razor...unique.peptides)>=2)}
filtered_data<-ids_test(filtered_data)
data_unique<- DEP::make_unique(filtered_data,"Gene.names","Protein.IDs",delim=";")
lfq_columns<-grep("LFQ.", colnames(data_unique))
# ensure all intensity columns are numeric type
data_unique[,lfq_columns] <- sapply(data_unique[,lfq_columns],as.numeric)
## Check for matching columns in maxquant and experiment design file
test_match_lfq_column_design(data_unique,lfq_columns, exp_design())
data_se<-DEP:::make_se(data_unique,lfq_columns,exp_design())
# # Check number of replicates
# if(max(exp_design()$replicate)<3){
# threshold<-0
# } else if(max(exp_design()$replicate)==3){
# threshold<-1
# } else if(max(exp_design()$replicate)<6 ){
# threshold<-2
# } else if (max(exp_design()$replicate)>=6){
# threshold<-trunc(max(exp_design()$replicate)/2)
# }
#
#
# filter_missval(data_se,thr = threshold)
# filter_missval(data_se,thr = threshold)
exp_df <- exp_design() %>% dplyr::count(condition)
exp_df <- exp_df %>% dplyr::mutate(thr = lapply(exp_df$n, threshold_detect)) # function:threshold_detect
condition_list <- exp_df$condition
data_filtered <- filter_missval_new(data_se,condition_list,exp_df)
return(data_filtered)
})
unimputed_table<-reactive({
temp<-assay(processed_data())
temp1<-2^(temp)
colnames(temp1)<-paste(colnames(temp1),"original_intensity",sep="_")
temp1<-cbind(ProteinID=rownames(temp1),temp1)
#temp1$ProteinID<-rownames(temp1)
return(as.data.frame(temp1))
})
normalised_data<-reactive({
normalize_vsn(processed_data())
})
imputed_data<-reactive({
DEP::impute(processed_data(),input$imputation)
})
imputed_table<-reactive({
temp<-assay(imputed_data())
#tibble::rownames_to_column(temp,var = "ProteinID")
temp1<-2^(temp)
colnames(temp1)<-paste(colnames(temp1),"imputed_intensity",sep="_")
temp1<-cbind(ProteinID=rownames(temp1),temp1) #temp1$ProteinID<-rownames(temp1)
return(as.data.frame(temp1))
})
diff_all<-reactive({
test_diff(imputed_data(),type = 'all')
})
dep<-reactive({
if(input$fdr_correction=="BH"){
diff_all<-test_limma(imputed_data(),type='all', paired = input$paired)
add_rejections(diff_all,alpha = input$p, lfc= input$lfc)
}
else{
diff_all<-test_diff(imputed_data(),type='all')
add_rejections(diff_all,alpha = input$p, lfc= input$lfc)
}
})
comparisons<-reactive ({
temp<-capture.output(DEP::test_diff(imputed_data(),type='all'),type = "message")
gsub(".*: ","",temp)
## Split conditions into character vector
unlist(strsplit(temp,","))
## Remove leading and trailing spaces
trimws(temp)
})
## Select point on volcano plot
# protein_graph_selected<- reactive({
# protein_row<-nearPoints(data_result(), input$protein_click,
# maxpoints = 1)
# # as.character(protein_row$name)
# })
#
## Results plot inputs
## PCA Plot
pca_input<-eventReactive(input$analyze ,{
if(input$analyze==0 ){
return()
}
if (num_total()<=500){
if(length(levels(as.factor(colData(dep())$replicate))) <= 6){
pca_plot<-DEP::plot_pca(dep(), n=num_total(), point_size = 4)
pca_plot<-pca_plot + labs(title = "PCA Plot")
return(pca_plot)
}
else{
pca_plot<-DEP::plot_pca(dep(), n=num_total(), point_size = 4, indicate = "condition")
pca_plot<-pca_plot + labs(title = "PCA Plot")
return(pca_plot)
}
}
else{
if(length(levels(as.factor(colData(dep())$replicate))) <= 6){
pca_plot<-DEP::plot_pca(dep(), point_size = 4)
pca_plot<-pca_plot + labs(title = "PCA Plot")
return(pca_plot)
}
else{
#pca_label<-SummarizedExperiment::colData(dep())$replicate
pca_plot<-DEP::plot_pca(dep(), point_size = 4, indicate = "condition")
#pca_plot<-pca_plot + geom_point()
pca_plot<-pca_plot + ggrepel::geom_text_repel(aes(label=factor(rowname)),
size = 4,
box.padding = unit(0.1, 'lines'),
point.padding = unit(0.1, 'lines'),
segment.size = 0.5)
pca_plot<-pca_plot + labs(title = "PCA Plot")
# pca_plot<-DEP::plot_pca(dep(), point_size = 4, indicate = "condition")
# pca_plot + ggrepel::geom_text_repel(aes(label=SummarizedExperiment::colData(dep())$replicate),
# size = 5,
# box.padding = unit(0.1, 'lines'),
# point.padding = unit(0.1, 'lines'),
# segment.size = 0.5)
return(pca_plot)
}
}
})
### Heatmap Differentially expressed proteins
heatmap_cluster <- eventReactive(input$analyze ,{
if(input$analyze==0 ){
return()
}
heatmap_list <- get_cluster_heatmap(dep(),
type="centered",kmeans = TRUE,
k=input$k_number, col_limit = 6,
indicate = "condition"
)
return(heatmap_list)
})
heatmap_input <- reactive({
heatmap_list <- heatmap_cluster()
heatmap_list[[1]]
})
# heatmap_input<-eventReactive(input$analyze ,{
# if(input$analyze==0 ){
# return()
# }
#
# heatmap_list <-get_cluster_heatmap(dep(),
# type="centered",kmeans = TRUE,
# k=input$k_number, col_limit = 6,
# indicate = "condition"
# )
# heatmap_list[[1]]
# })
### Volcano Plot
volcano_input <- reactive({
if(!is.null(input$volcano_cntrst)) {
plot_volcano_new(dep(),
input$volcano_cntrst,
input$fontsize,
input$check_names,
input$p_adj)
}
})
volcano_df<- reactive({
if(!is.null(input$volcano_cntrst)) {
get_volcano_df(dep(),
input$volcano_cntrst,
input$p_adj
)
}
})
volcano_input_selected<-reactive({
if(!is.null(input$volcano_cntrst)){
if (!is.null(input$contents_rows_selected)){
proteins_selected<-data_result()[c(input$contents_rows_selected),]## get all rows selected
}
else if(!is.null(input$protein_brush)){
proteins_selected<-data_result()[data_result()[["Gene Name"]] %in% protein_name_brush(), ]
}
## convert contrast to x and padj to y
diff_proteins <- grep(paste("^",input$volcano_cntrst, "_log2", sep = ""),
colnames(proteins_selected))
if(input$p_adj=="FALSE"){
padj_proteins <- grep(paste("^",input$volcano_cntrst, "_p.val", sep = ""),
colnames(proteins_selected))
}
else{
padj_proteins <- grep(paste("^",input$volcano_cntrst, "_p.adj", sep = ""),
colnames(proteins_selected))
}
df_protein <- data.frame(x = proteins_selected[, diff_proteins],
y = -log10(as.numeric(proteins_selected[, padj_proteins])),#)#,
name = proteins_selected$`Gene Name`)
#print(df_protein)
p<-plot_volcano_new(dep(),
input$volcano_cntrst,
input$fontsize,
input$check_names,
input$p_adj)
p + geom_point(data = df_protein, aes(x, y), color = "maroon", size= 3) +
ggrepel::geom_text_repel(data = df_protein,
aes(x, y, label = name),
size = 4,
box.padding = unit(0.1, 'lines'),
point.padding = unit(0.1, 'lines'),
segment.size = 0.5)## use the dataframe to plot points
}
})
protein_input<-reactive({
# protein_selected <- data_result()[input$contents_rows_selected,1]
df<- data_result()
if(!is.null(input$protein_brush)){
df<- df[df[["Gene Name"]] %in% protein_name_brush(), ]
}
if(!is.null(input$protein_click)){
df<- df[df[["Gene Name"]] %in% protein_name_click(), ]
}
protein_selected <- df[input$contents_rows_selected,1]
if(length(levels(as.factor(colData(dep())$replicate))) <= 8){
plot_protein(dep(), protein_selected, input$type)
}
else{
protein_plot<-plot_protein(dep(), protein_selected, input$type)
protein_plot + scale_color_brewer(palette = "Paired")
}
})
## QC Inputs
norm_input <- reactive({
plot_normalization(processed_data(),
normalised_data())
})
missval_input <- reactive({
plot_missval(processed_data())
})
detect_input <- reactive({
plot_detect(processed_data())
})
imputation_input <- reactive({
plot_imputation(normalised_data(),
diff_all())
})
p_hist_input <- reactive({
plot_p_hist(dep())
})
numbers_input <- reactive({
plot_numbers(normalised_data())
})
coverage_input <- reactive({
plot_coverage(normalised_data())
})
correlation_input<-reactive({
plot_cor(dep(),significant = FALSE)
})
cvs_input<-reactive({
plot_cvs(dep())
})
num_total<-reactive({
dep() %>%
nrow()
})
## Enrichment inputs
go_input<-eventReactive(input$go_analysis,{
withProgress(message = 'Gene ontology enrichment is in progress',
detail = 'Please wait for a while', value = 0, {
for (i in 1:15) {
incProgress(1/15)
Sys.sleep(0.25)
}
})
if(!is.null(input$contrast)){
enrichment_output_test(dep(), input$go_database)
go_results<- test_gsea_mod(dep(), databases = input$go_database, contrasts = TRUE)
null_enrichment_test(go_results)
plot_go<- plot_enrichment(go_results, number = 5, alpha = 0.05, contrasts =input$contrast,
databases = input$go_database, nrow = 2, term_size = 8) + aes(stringr::str_wrap(Term, 60)) +
xlab(NULL)
go_list<-list("go_result"=go_results, "plot_go"=plot_go)
return(go_list)
}
})
pathway_input<-eventReactive(input$pathway_analysis,{
progress_indicator("Pathway Analysis is running....")
enrichment_output_test(dep(), input$pathway_database)
pathway_results<- test_gsea_mod(dep(), databases=input$pathway_database, contrasts = TRUE)
null_enrichment_test(pathway_results)
plot_pathway<-plot_enrichment(pathway_results, number = 5, alpha = 0.05, contrasts =input$contrast_1,
databases=input$pathway_database, nrow = 3, term_size = 8) + aes(stringr::str_wrap(Term, 30)) +
xlab(NULL)
pathway_list<-list("pa_result"=pathway_results, "plot_pa"=plot_pathway)
return(pathway_list)
})
#### Interactive UI
output$significantBox <- renderInfoBox({
num_total <- dep() %>%
nrow()
num_signif <- dep() %>%
.[SummarizedExperiment::rowData(.)$significant, ] %>%
nrow()
frac <- num_signif / num_total
info_box <- infoBox("Significant proteins",
paste0(num_signif,
" out of ",
num_total),
paste0(signif(frac * 100, digits = 3),
"% of proteins differentially expressed across all conditions"),
icon = icon("stats", lib = "glyphicon"),
color = "olive",
# fill = TRUE,
width = 4)
return(info_box)
})
##### Get results dataframe from Summarizedexperiment object
data_result<-reactive({
get_results_proteins(dep())
#get_results(dep())
})
#### Data table
output$contents <- DT::renderDataTable({
df<- data_result()
return(df)
},
options = list(scrollX = TRUE,
autoWidth=TRUE,
columnDefs= list(list(width = '400px', targets = c(-1))))
)
## Deselect all rows button
proxy <- dataTableProxy("contents")
observeEvent(input$clear,{
proxy %>% selectRows(NULL)
})
observeEvent(input$original,{
output$contents <- DT::renderDataTable({
df<- data_result()
return(df)
},
options = list(scrollX = TRUE,
autoWidth=TRUE,
columnDefs= list(list(width = '400px', targets = c(-1))))
)
})
protein_name_brush<- reactive({
#protein_tmp<-nearPoints(volcano_df(), input$protein_click, maxpoints = 1)
protein_tmp<-brushedPoints(volcano_df(), input$protein_brush,
xvar = "diff", yvar = "p_values")
protein_selected<-protein_tmp$name
})
protein_name_click<- reactive({
protein_tmp<-nearPoints(volcano_df(), input$protein_click, maxpoints = 1)
# protein_tmp<-brushedPoints(volcano_df(), input$protein_brush,
#xvar = "diff", yvar = "p_values")
protein_selected<-protein_tmp$name
})
# observeEvent(input$protein_brush,{
# output$protein_info<-renderPrint({
# # protein_selected()
# #nearPoints(rowData(dep()), input$protein_click, maxpoints = 1)
# brushedPoints(volcano_df(), input$protein_brush,
# xvar = "diff", yvar = "p_values")
# # head(volcano_df())
# #input$protein_click
# # str(input$protein_hover)
# })
# })
## Select rows dynamically
brush <- NULL
makeReactiveBinding("brush")
observeEvent(input$protein_brush,{
output$contents <- DT::renderDataTable({
df<- data_result()[data_result()[["Gene Name"]] %in% protein_name_brush(), ]
return(df)
},
options = list(scrollX= TRUE)
)
# proteins_selected<-data_result()[data_result()[["Gene Name"]] %in% protein_name_brush(), ] ## get all rows selected
# ## convert contrast to x and padj to y
# diff_proteins <- grep(paste("^",input$volcano_cntrst, "_log2", sep = ""),
# colnames(proteins_selected))
# if(input$p_adj=="FALSE"){
# padj_proteins <- grep(paste("^",input$volcano_cntrst, "_p.val", sep = ""),
# colnames(proteins_selected))
# }
# else{
# padj_proteins <- grep(paste("^",input$volcano_cntrst, "_p.adj", sep = ""),
# colnames(proteins_selected))
# }
# df_protein <- data.frame(x = proteins_selected[, diff_proteins],
# y = -log10(as.numeric(proteins_selected[, padj_proteins])),#)#,
# name = proteins_selected$`Gene Name`)
# #print(df_protein)
#
# p<-plot_volcano_new(dep(),
# input$volcano_cntrst,
# input$fontsize,
# input$check_names,
# input$p_adj)
#
# p<- p + geom_point(data = df_protein, aes(x, y), color = "maroon", size= 3) +
# ggrepel::geom_text_repel(data = df_protein,
# aes(x, y, label = name),
# size = 4,
# box.padding = unit(0.1, 'lines'),
# point.padding = unit(0.1, 'lines'),
# segment.size = 0.5)
#
# output$volcano <- renderPlot({
# withProgress(message = 'Volcano Plot calculations are in progress',
# detail = 'Please wait for a while', value = 0, {
# for (i in 1:15) {
# incProgress(1/15)
# Sys.sleep(0.25)
# }
# })
# p
# })
# return(p)
})
observeEvent(input$resetPlot,{
session$resetBrush("protein_brush")
brush <<- NULL
output$contents <- DT::renderDataTable({
df<- data_result()
return(df)
},
options = list(scrollX = TRUE,
autoWidth=TRUE,
columnDefs= list(list(width = '400px', targets = c(-1))))
)
})
observeEvent(input$protein_click,{
output$contents <- DT::renderDataTable({
df<- data_result()[data_result()[["Gene Name"]] %in% protein_name_click(), ]
return(df)
},
options = list(scrollX= TRUE,
autoWidth=TRUE,
columnDefs= list(list(width = '400px', targets = c(-1))))
)
})
## Render Result Plots
output$pca_plot<-renderPlot({
pca_input()
})
output$heatmap<-renderPlot({
withProgress(message = 'Heatmap rendering is in progress',
detail = 'Please wait for a while', value = 0, {
for (i in 1:15) {
incProgress(1/15)
Sys.sleep(0.25)
}
})
heatmap_input()
})
output$volcano <- renderPlot({
withProgress(message = 'Volcano Plot calculations are in progress',
detail = 'Please wait for a while', value = 0, {
for (i in 1:15) {
incProgress(1/15)
Sys.sleep(0.25)
}
})
if(is.null(input$contents_rows_selected) & is.null(input$protein_brush)){
volcano_input()
}
else if(!is.null(input$volcano_cntrst)){
volcano_input_selected()
}# else close
})
output$protein_plot<-renderPlot({
if(!is.null(input$contents_rows_selected)){
protein_input()
}
})
### QC Outputs
output$sample_corr <-renderPlot({
correlation_input()
})
output$sample_cvs <- renderPlot({
cvs_input()
})
output$norm <- renderPlot({
norm_input()
})
output$missval <- renderPlot({
missval_input()
})
output$detect <- renderPlot({
detect_input()
})
output$imputation <- renderPlot({
imputation_input()
})
output$p_hist <- renderPlot({
p_hist_input()
})
output$numbers <- renderPlot({
numbers_input()
})
output$coverage <- renderPlot({
coverage_input()
})
## Enrichment Outputs
output$spinner_go <- renderUI({
req(input$go_analysis)
shinycssloaders::withSpinner(plotOutput("go_enrichment"), color = "#3c8dbc")
})
output$go_enrichment<-renderPlot({
Sys.sleep(2)
go_input()$plot_go
})
output$spinner_pa <- renderUI({
req(input$pathway_analysis)
shinycssloaders::withSpinner(plotOutput("pathway_enrichment"), color = "#3c8dbc")
})
output$pathway_enrichment<-renderPlot({
Sys.sleep(2)
pathway_input()$plot_pa
})
##### Download Functions
datasetInput <- reactive({
switch(input$dataset,
"Results" = get_results_proteins(dep()),
"Original_matrix"= unimputed_table(),
# "significant_proteins" = get_results(dep()) %>%
# filter(significant) %>%
# select(-significant),
"Imputed_matrix" = imputed_table(),
"Full_dataset" = get_df_wide(dep()))
})
output$downloadData <- downloadHandler(
filename = function() { paste(input$dataset, ".csv", sep = "") }, ## use = instead of <-
content = function(file) {
write.table(datasetInput(),
file,
col.names = TRUE,
row.names = FALSE,
sep =",") }
)
### === Cluster Download ==== ####
individual_cluster <- reactive({
cluster_number <- input$cluster_number
cluster_all <-heatmap_cluster()[[2]]
single_cluster <- cluster_all[names(cluster_all)==cluster_number] %>% unlist()
df <- data_result()[single_cluster,]
return(df)
})
# output$text1 <- renderPrint({
# paste(individual_cluster())
# })
output$downloadCluster <- downloadHandler(
filename = function() { paste("Cluster_info_",input$cluster_number, ".csv", sep = "") }, ## use = instead of <-
content = function(file) {
write.table(individual_cluster(),
file,
col.names = TRUE,
row.names = FALSE,
sep =",") }
)
output$downloadVolcano <- downloadHandler(
filename = function() {
paste0("Volcano_", input$volcano_cntrst, ".pdf")
},
content = function(file) {
pdf(file)
if(is.null(input$contents_rows_selected) & is.null(input$protein_brush)){
print(volcano_input())
dev.off()
}
else{
# observeEvent(input$protein_brush,{
# print(p)
# })
print(volcano_input_selected())
dev.off()
}
}
)
## Protein plot download
output$downloadProtein <- downloadHandler(
filename = function() {
paste0(input$type,".pdf")
},
content = function(file) {
pdf(file)
print(protein_input())
dev.off()
}
)
###### ==== DOWNLOAD GO TABLE ==== ####
output$downloadGO <- downloadHandler(
filename = function() { paste("GO_enrichment_",input$go_database, ".csv", sep = "") }, ## use = instead of <-
content = function(file) {
write.table(go_input()$go_result,
file,
col.names = TRUE,
row.names = FALSE,
sep =",") }
)
###### ==== DOWNLOAD PATHWAY TABLE ==== ####
output$downloadPA <- downloadHandler(
filename = function() { paste("Pathway_enrichment_",input$pathway_database, ".csv", sep = "") },
## use = instead of <-
content = function(file) {
write.table(pathway_input()$pa_result,
file,
col.names = TRUE,
row.names = FALSE,
sep =",") }
)
output$download_hm_svg<-downloadHandler(
filename = function() { "heatmap.svg" },
## use = instead of <-
content = function(file) {
# heatmap_plot<-DEP::plot_heatmap(dep(),"centered", k=6, indicate = "condition")
svg(file)
print(heatmap_input())
dev.off()
}
)
#####===== Download Report =====#####
output$downloadReport <- downloadHandler(
# For PDF output, change this to "report.pdf"
filename = "LFQ-Analyst_report.pdf",
content = function(file) {
# Copy the report file to a temporary directory before processing it, in
# case we don't have write permissions to the current working dir (which
# can happen when deployed).
tempReport <- file.path(tempdir(), "LFQ_report.Rmd")
file.copy("LFQ_report.Rmd", tempReport, overwrite = TRUE)
sig_proteins<-dep() %>%
.[SummarizedExperiment::rowData(.)$significant, ] %>%