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server.R
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server.R
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options(repos = c(getOption("repos"), BiocManager::repositories()))
getOption("repos")
library(shiny)
library(rsconnect)
library(plyr)
library(data.table)
library(genefilter)
library(Biobase)
library(lmQCM)
library(WGCNA)
library(GEOquery)
library(dplyr)
library(enrichR)
library(DT)
library(plotly)
library(openxlsx)
library(survival)
library(naturalsort)
library(shinyWidgets)
library(progress)
# circos plot
library(circlize)
# library(topGO) # Somehow conflict with WGCNA and GEOquery. Deprecated.
options(shiny.maxRequestSize=300*1024^2) # to the top of server.R would increase the limit to 300MB
options(shiny.sanitize.errors = FALSE)
options(stringAsFactors = FALSE)
source("utils.R")
# setwd("/Users/zhi/Desktop/GeneCoexpression/RGUI"); #mac #remove when deploy to shinyapps.io
# source("./lmQCM/GeneCoExpressionAnalysis.R")
# ----------------------------------------------------
setClass("QCMObject", representation(clusters.id = "list", clusters.names = "list",
eigengene.matrix = "data.frame"))
# listEnrichrDbs()
# print(list.files())
# print(list.files("./data"))
path2TCGAdata <<- "./data/20190303_TCGA_gdac_mRNAseq/"#"http://web.ics.purdue.edu/~huang898/TSUNAMI_data/20190303_TCGA_gdac_mRNAseq/"
enrichr_dbs <<- c("GO_Biological_Process_2018",
"GO_Molecular_Function_2018",
"GO_Cellular_Component_2018",
"Jensen_DISEASES",
"Reactome_2016",
"KEGG_2016",
"Transcription_Factor_PPIs",
"Genome_Browser_PWMs",
"TRANSFAC_and_JASPAR_PWMs",
"ENCODE_TF_ChIP-seq_2015",
"Chromosome_Location",
"miRTarBase_2017",
"TargetScan_microRNA_2017",
"ChEA_2016")
TCGA.gdac.list <<- read.csv("./data/TCGA_gdac_metadata.csv", header = T)
server <- function(input, output, session) {
data <- reactiveVal(0)
data_final <- reactiveVal(0)
GEO <- reactiveVal(0)
GSE_name_title <- reactiveVal(0)
mygset <- reactiveVal(0)
finalExp <- reactiveVal(0)
finalSym <- reactiveVal(0)
sampleID <- reactiveVal(0)
finalSymChar <- reactiveVal(0)
text.final <- reactiveVal(0)
geneCharVector_global <- reactiveVal(0)
eigengene_matrix <- reactiveVal(0)
fname <- reactiveVal(0) # e.g. 12345_at
enriched <- reactiveVal(0) # all enrichr results
final_genes_str <- reactiveVal(0)
data.name <- reactiveVal(0)
###################### Website migration ######################
# reactiveValues object for storing current data set.
redirect.time <- 10
EventTime <- Sys.time() + redirect.time
# Return the UI for a modal dialog with data selection input. If 'failed' is
# TRUE, then display a message that the previous value was invalid.
dataModal <- function(failed = FALSE) {
modalDialog(
title = "Web server migration notice",
"Dear TSUNAMI user: We are switching from R shiny server page to Biolearns TSUNAMI. If you would like to use legacy R shiny server page, you can click stay button. Otherwise, click Redirect now button if you want to proceed now.",
HTML("<br><br>"),
output$eventTimeRemaining <- renderText({
invalidateLater(1000, session)
paste("Redirect to Biolearns in:",
round(difftime(EventTime, Sys.time(), units='secs')), 'secs')
}),
footer = tagList(
actionButton("stay", "Stay on this site"),
actionButton("redirect", "Redirect now")
)
)
}
showModal(dataModal())
# When OK button is pressed, attempt to load the data set. If successful,
# remove the modal. If not show another modal, but this time with a failure
# message.
observeEvent(input$stay, {
removeModal()
})
observeEvent(input$redirect, {
session$sendCustomMessage("redirectmessage", "redirectmessage")
})
dt <- 0 # Nb seconds
# Timer one second
autoInvalidate <- reactiveTimer(1000)
observe({
dt <<- dt + 1 # Increment one second
someCondition = FALSE
if(dt >= redirect.time){ # After 30 seconds
someCondition = TRUE
}
if(someCondition)
session$sendCustomMessage("redirectmessage", "redirectmessage")
else
autoInvalidate() # Restart timer
})
observeEvent(input$action1,{
print('tab1')
session$sendCustomMessage("myCallbackHandler", "tab1")
})
output$NCBI_GEO_Release_Date <- renderPlotly({
# Create a Progress object
progress <- shiny::Progress$new(session)
progress$set(message = "Loading NCBI GEO database. Last fetched version: 03/03/2019", value = 100)
# Close the progress when this reactive exits (even if there's an error)
on.exit(progress$close())
# NCBI GEO
load("./data/GEO_20190303.Rdata")
GEO.temp <- GEO
GEO.temp[["Actions"]] <- paste0('<div><button type="button" class="btn-analysis" id=GEO_dataset_analysis_',1:nrow(GEO.temp),'>Analyze</button></div>')
colnames(GEO.temp)[which(names(GEO.temp) == "Sample.Count")] <- "Samples"
GEO.temp <- GEO.temp %>% select("Samples", everything())
GEO.temp <- GEO.temp %>% select("Actions", everything())
GEO(GEO.temp)
# Basic process
TCGA.gdac.list[["Actions"]] <- paste0('<div><button type="button" class="btn-analysis" id=TCGA_dataset_analysis_',1:nrow(TCGA.gdac.list),'>Analyze</button></div>')
TCGA.gdac.list <- TCGA.gdac.list %>% select("Actions", everything())
output$mytable0 <- DT::renderDataTable({
DT::datatable(TCGA.gdac.list, extensions = 'Responsive', escape=F, selection = 'none',
options = list(paging = F, searching = T, dom='t',ordering=T), rownames = F)
})
output$mytable1 <- DT::renderDataTable({
DT::datatable(GEO(), extensions = 'Responsive', escape=F, selection = 'none', rownames = F)
})
# Other fancy processes
# number of samples
output$NCBI_GEO_Sample_Histogram <- renderPlotly({
GEO_sample_number <- GEO()$Samples
plot_ly(x = GEO_sample_number, type = "histogram") %>%
layout(title = "Samples Histogram (log scale)",font=list(size = 10),
xaxis = list(title = "Number of Samples"),
yaxis = list(title = "Occurence", type = "log")) %>%
layout(plot_bgcolor='transparent') %>%
layout(paper_bgcolor='transparent') %>% config(displayModeBar = F)
})
#release date
GEO_release_date <- as.Date(GEO()$Release.Date,format = "%B %d, %Y")
GEO_release_date_unique = cumsum(table(GEO_release_date)) #cummulative sum
font <- list(family = "Courier New, monospace", size = 12, color = "black")
x_axis <- list(title = "", titlefont = font, tickangle = 45, zeroline = TRUE)
y_axis <- list(title = "Number of GSE data", titlefont = font)
plot_ly(x = names(GEO_release_date_unique), y = as.numeric(GEO_release_date_unique),
type = 'scatter', mode = 'lines') %>%
layout(title = "Number of GSE Data Growing",font=list(size = 10), xaxis = x_axis, yaxis = y_axis) %>%
layout(plot_bgcolor='transparent') %>%
layout(paper_bgcolor='transparent') %>% config(displayModeBar = F)
})
observeEvent(input$dataset_lastClickId_mytable0,{
if (input$dataset_lastClickId_mytable0%like%"TCGA_dataset_analysis"){
row_of_TCGA <- as.numeric(gsub("TCGA_dataset_analysis_","",input$dataset_lastClickId_mytable0))
print(TCGA.gdac.list[row_of_TCGA,])
cancer.name = TCGA.gdac.list[row_of_TCGA,2]
data.name(cancer.name)
smartModal(error=F, title = sprintf("Loading TCGA %s Data", cancer.name),
content = sprintf("Loading TCGA %s Data ...", cancer.name))
load(paste0(path2TCGAdata, cancer.name, ".Rdata"))
# TCGA.mRNAseq = data.frame(cbind(rownames(TCGA.mRNAseq), TCGA.mRNAseq))
# colnames(TCGA.mRNAseq)[1] = "Gene"
data(TCGA.mRNAseq)
updateTextInput(session, "platform_text", value = "")
output$summary <- renderPrint({
print(sprintf("Data: %s", data.name() ))
print(sprintf("Number of Genes: %d",dim(data())[1]))
print(sprintf("Number of Samples: %d",dim(data())[2]))
})
print("GEO file is downloaded to server and processed.")
output$mytable4 <- DT::renderDataTable({
# Expression Value
DT::datatable(data(),extensions = 'Responsive', escape=F, selection = 'none')
})
output$mytable5 <- DT::renderDataTable({
# Expression Value
verified_data = data()[ifelse(is.na(input$starting_row),1,input$starting_row):dim(data())[1],
ifelse(is.na(input$starting_col),1,input$starting_col):dim(data())[2]]
# colnames(verified_data)[1] <- "Gene"
DT::datatable(verified_data,extensions = 'Responsive', escape=F, selection = 'none')
})
print('tab2')
removeModal()
session$sendCustomMessage("myCallbackHandler", "tab2")
}
})
observeEvent(input$dataset_lastClickId_mytable1,{
if (input$dataset_lastClickId_mytable1%like%"GEO_dataset_analysis"){
row_of_GEO <- as.numeric(gsub("GEO_dataset_analysis_","",input$dataset_lastClickId_mytable1))
myGSE <- GEO()$Accession[row_of_GEO]
data.name(myGSE)
GSE_name_title(GEO()$Title[row_of_GEO])
message(GSE_name_title())
print(myGSE)
smartModal(error=F, title = sprintf("Loading %s file from NCBI GEO Database", myGSE),
content = "We are currently loading your selected file from NCBI GEO Database ...")
t <- try(gset <- getGEO(myGSE, GSEMatrix=TRUE, AnnotGPL=FALSE)) #AnnotGPL default is FALSE
if("try-error" %in% class(t)) {
removeModal()
print("HTTP error 404")
smartModal(error=T, title = "HTTP error 404", content = sprintf("%s is not available in NCBI GEO Database, please try other available GSE data (e.g., GSE17537, GSE73119). (Hint: Maybe bad Internet connection)",myGSE))
return()
}
# if (length(gset) > 1) idx <- grep("GPL90", attr(gset, "names")) else idx <- 1
if (length(gset) == 0){
removeModal()
print("This GSE accession doesn't contain any series matrix.")
smartModal(error=T, title = "This GSE accession doesn't contain any series matrix.", content = sprintf("%s doesn't contain any series matrix. Please try other available GSE data (e.g., GSE17537, GSE73119).",myGSE))
return()
}
mygset(gset[[1]])
# select index
if (length(gset) > 1){
gset.names = names(gset)
print(gset.names)
gset.names = paste0(rep(1:length(gset.names)), ". ", gset.names)
gset.names = paste0(gset.names, collapse = "\n")
removeModal()
inputSweetAlert(session, inputId = "whichgset", title = "This data contains multiple serie matrices.\nInput the ID (e.g. 1) to select desired one.",
text = gset.names,
type = "info", btn_labels = "Ok")
observeEvent(input$whichgset, {
mygset(gset[[as.numeric(input$whichgset)]])
})
}
}
})
observeEvent(data(),{
if (typeof(data()) == "double"){
return()
}
samples = colnames(data())[input$starting_col:dim(data())[2]]
output$data_sample_subgroup_ui <- renderUI({
checkboxGroupInput("data_sample_subgroup", "Choose samples:",
choiceNames = samples,
choiceValues = samples,
selected = samples
)
})
})
observeEvent(mygset(),{
if (typeof(mygset()) == "double"){
return()
}
smartModal(error=F, title = sprintf("Selecting table from NCBI GEO Database"),
content = "We are currently loading your selected file from NCBI GEO Database ...")
edata <- exprs(mygset()) #This is the expression matrix
if (dim(edata)[1] == 0 || is.null(dim(edata))){
removeModal()
print("No expression data")
smartModal(error=T, title = "Important message", content = sprintf("%s doesn't contain any expression data, please try other available GSE data (e.g., GSE17537, GSE73119).", myGSE))
return()
}
# pdata <- pData(mygset()) # data.frame of phenotypic information.
fname(featureNames(mygset())) # e.g. 12345_at
# data(cbind(fname(), edata))
# data.temp <- data()
# row.names(data.temp) <- seq(1, length(fname()))
# data(data.temp)
row.names(edata) <- fname()
data(edata)
updateTextInput(session, "platform_text", value = paste(annotation(mygset()), input$controller))
output$summary <- renderPrint({
print(sprintf("Data: %s", data.name() ))
print(sprintf("Number of Genes: %d",dim(edata)[1]))
print(sprintf("Number of Samples: %d",dim(edata)[2]))
print(sprintf("Annotation Platform: %s",annotation(mygset())))
})
print("GEO file is downloaded to server and processed.")
output$mytable4 <- DT::renderDataTable({
# Expression Value
DT::datatable(data(),extensions = 'Responsive', escape=F, selection = 'none')
})
output$mytable5 <- DT::renderDataTable({
# Expression Value
verified_data = data()[ifelse(is.na(input$starting_row),1,input$starting_row):dim(data())[1],
ifelse(is.na(input$starting_col),1,input$starting_col):dim(data())[2]]
# colnames(verified_data)[1] <- "Gene"
DT::datatable(verified_data,extensions = 'Responsive', escape=F, selection = 'none')
})
print('tab2')
removeModal()
session$sendCustomMessage("myCallbackHandler", "tab2")
})
output$readingcsv <- reactive({
print("readingcsv = 1")
return(is.null(input$csvfile))
})
observeEvent(input$action2,{
options(stringsAsFactors = FALSE)
if(is.null(input$csvfile)){
print("no files!")
smartModal(error=T, title = "Important message", content = "No file uploaded! Please retry!")
return(NULL)
}
else {
print("Reading file.")
smartModal(error=F, title = "Intepreting uploaded file in progress", content = "Intepreting ...")
fileExtension <- getFileNameExtension(input$csvfile$datapath)
if(fileExtension == "csv"){
data.temp <- read.csv(input$csvfile$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
data(data.temp)
print("csv file Processed.")
}
else if(fileExtension == "txt"){
data_temp = as.matrix(readLines(input$csvfile$datapath), sep = '\n')
data_temp = strsplit(data_temp, split=input$sep)
max.length <- max(sapply(data_temp, length))
data_temp <- lapply(data_temp, function(v) { c(v, rep(NA, max.length-length(v)))})
data_temp <- data.frame(do.call(rbind, data_temp))
colnames(data_temp) = data_temp[1,]
data_temp = data_temp[2:dim(data_temp)[1],]
if (is.null(dim(data_temp))){
removeModal()
sendSweetAlert(session, title = "Error", text = "Input file extention TXT found, but cannot construct matrix. Please confirm your data format and separator.", type = "error",
btn_labels = "Ok", html = FALSE, closeOnClickOutside = TRUE)
return()
}
if(data_temp[dim(data_temp)[1],1] == "!series_matrix_table_end"){
print("remove last row with \"!series_matrix_table_end\" ")
data_temp = data_temp[-dim(data_temp)[1],]
}
# data_temp <- print.data.frame(data.frame(data_temp), quote=FALSE)
data(data_temp)
print("txt file Processed.")
} else if(fileExtension == "xlsx"){
data_temp <- read.xlsx(input$csvfile$datapath, sheet = 1, startRow = 1, colNames = TRUE)
if(data_temp[dim(data_temp)[1],1] == "!series_matrix_table_end"){
print("remove last row with \"!series_matrix_table_end\" ")
data_temp = data_temp[-dim(data_temp)[1],]
}
# data_temp <- print.data.frame(data.frame(data_temp), quote=FALSE)
data(data_temp)
print("xlsx file Processed.")
} else if(fileExtension == "xls"){
sendSweetAlert(session, title = "Error", text = "We discontinued to support XLS format. Please resubmit with another file format.", type = "error",
btn_labels = "Ok", html = FALSE, closeOnClickOutside = TRUE)
}
removeModal()
# first column as gene
data.temp = data()#[,2:dim(data())[2]]
rownames(data.temp) = data()[,1]
data(data.temp)
data.name('Uploaded Data')
output$summary <- renderPrint({
print(sprintf("Data: %s", data.name() ))
print(sprintf("Number of Genes: %d",dim(data())[1]))
print(sprintf("Number of Samples: %d",(dim(data())[2]-1)))
print("Annotation Platform: Unknown")
}, quoted = FALSE)
if ((dim(data())[2]-1) == 0){
print("Number of samples 0!")
smartModal(error=T, title = "Important message", content = sprintf("Target file contains no sample. This problem could because user pick a not matched separator (default: Comma), please try another seperator (e.g. Tab or Space)."))
return()
}
output$mytable4 <- DT::renderDataTable({
# Expression Value
DT::datatable(data(),extensions = 'Responsive', escape=F, selection = 'none')
})
output$mytable5 <- DT::renderDataTable({
# Verified data
verified_data = data()[ifelse(is.na(input$starting_row),1,input$starting_row):dim(data())[1],
ifelse(is.na(input$starting_col),1,input$starting_col):dim(data())[2]]
# colnames(verified_data)[1] <- "Gene"
DT::datatable(verified_data,extensions = 'Responsive', escape=F, selection = 'none')
})
print('tab2')
session$sendCustomMessage("myCallbackHandler", "tab2")
}
})
# +------------------------------------------------------------+
# |
# |
# | Platform Convert
# |
# |
# +--------------------------------
observeEvent(input$action_platform,{
if(is.null(data())){
smartModal(error=T, title = "Operation Failed", content = "You have not selected any data. Please go to previous section.")
return()
}
smartModal(error=F, title = "Converting...", content = "We are currently converting probe ID to Gene Symbol...")
platform_name <- gsub(" ", "", input$platform_text, fixed = TRUE)
print(sprintf("Platform: %s",platform_name))
t <- try(gpl <- getGEO(platform_name))
if("try-error" %in% class(t)) {
removeModal()
print("HTTP error 404")
smartModal(error=c(T,F), title = "HTTP error 404", content = sprintf("Platform %s is not available in NCBI GEO Database, please try another!", platform_name))
return()
}
print("Platform Loaded.")
#https://www.rdocumentation.org/packages/GEOquery/versions/2.38.4/topics/GEOData-class
gpltable <- Table(gpl)
if (is.null(fname())){
# data is not from GEO
print("data is self-uploaded, so no fname defined.")
fname(data()[ifelse(is.na(input$starting_row),1,input$starting_row):dim(data())[1], 1])# Gene ID
fname.temp <- fname()
fname.temp <- noquote(fname.temp) # convert "\"1553418_a_at\"" to "1553418_a_at" (safer)
fname.temp <- gsub("\"","",fname.temp) # convert "\"1553418_a_at\"" to "1553418_a_at"
fname(fname.temp)
# save(fname,file="/Users/zhi/Desktop/fname.Rdata")
}
fname2 <- fname()
if (!is.null(gpltable$`Gene Symbol`)){
print("load GPL table with name \"Gene Symbol\"")
fname2 <- gpltable$`Gene Symbol`[match(fname(), gpltable$ID)]
}
else if (!is.null(gpltable$`GENE_SYMBOL`)){
print("load GPL table with name \"GENE_SYMBOL\"")
fname2 <- gpltable$`GENE_SYMBOL`[match(fname(), gpltable$ID)]
}
else if (!is.null(gpltable$`CLONE_ID`)){
print("load GPL table with name \"CLONE_ID\"")
fname2 <- gpltable$`CLONE_ID`[match(fname(), gpltable$ID)]
}
else {
removeModal()
print("HTTP error 404")
smartModal(error=T, title = "Important message", content = sprintf("Error occured while loading %s. This issue could be different name defined on Gene Symbol (GENE_SYMBOL or others) in the platform.", platform_name))
return()
}
print(dim(data()))
print(length(fname2))
data.temp <- data()
# data.temp[ifelse(is.na(input$starting_row),1,input$starting_row):dim(data.temp)[1],1] <- fname2
rownames(data.temp)[ifelse(is.na(input$starting_row),1,input$starting_row):dim(data.temp)[1]] <- fname2
data(data.temp)
# row.names(data) <- seq(1, length(fname2))
output$mytable4 <- DT::renderDataTable({
# Expression Value
DT::datatable(data(),extensions = 'Responsive', escape=F, selection = 'none')
})
output$mytable5 <- DT::renderDataTable({
# Expression Value
verified_data = data()[ifelse(is.na(input$starting_row),1,input$starting_row):dim(data())[1],
ifelse(is.na(input$starting_col),2,input$starting_col):dim(data())[2]]
# colnames(verified_data)[1] <- "Gene"
DT::datatable(verified_data,extensions = 'Responsive', escape=F, selection = 'none')
})
print("Platform Conversion finished")
removeModal()
})
# +------------------------------------------------------------+
# |
# |
# | Cleaning the Data
# |
# |
# +--------------------------------
observe({
if(typeof(data()) != "double"){
samples = colnames(data())[input$starting_col:dim(data())[2]]
if(input$data_sample_subgroup_selectall == 0) return(NULL)
else if (input$data_sample_subgroup_selectall%%2 == 1)
{
updateCheckboxGroupInput(session,"data_sample_subgroup",choices=samples)
}
else
{
updateCheckboxGroupInput(session,"data_sample_subgroup",choices=samples, selected=samples)
}
}
})
observeEvent(input$action3,{
if(is.null(data())){
smartModal(error=T, title = "Operation Failed", content = "You have not selected any data. Please go to previous section.")
return()
}
smartModal(error=F, title = "Preprocessing input data", content = "Preprocessing ...")
RNA = data()
if(!is.null(input$data_sample_subgroup)){
RNA = RNA[, colnames(RNA) %in% input$data_sample_subgroup]
}
print(dim(RNA))
withProgress(message = 'Preprocessing input data', value = 0, {
# Step 0
# Increment the progress bar, and update the detail text.
incProgress(1/5, detail = "Parsing Input Data")
# if (!is.null(input$starting_col) && input$starting_col >= 2){
# geneID <- data.frame(RNA[,input$starting_col-1])
# } else{ # use rownames as gene ID
# geneID <- data.frame(rownames(RNA)[ifelse(is.na(input$starting_row),1,input$starting_row):dim(RNA)[1]])
# }
geneID <- data.frame(rownames(RNA)[ifelse(is.na(input$starting_row),1,input$starting_row):dim(RNA)[1]])
if(!is.null(input$data_sample_subgroup)){
RNA <- as.matrix(RNA[ifelse(is.na(input$starting_row),1,input$starting_row):dim(RNA)[1],])
} else{
RNA <- as.matrix(RNA[ifelse(is.na(input$starting_row),1,input$starting_row):dim(RNA)[1], ifelse(is.na(input$starting_col),2,input$starting_col):dim(RNA)[2]])
}
class(RNA) <- "numeric"
print(dim(RNA))
print(dim(geneID))
# convert na to 0
if (input$checkbox_NA){RNA[is.na(RNA)] <- 0}
# Remove data with lowest 20% mean exp value shared by all samples
percentile <- ifelse(is.na(input$mean_expval),0,input$mean_expval)/100.
percentile.mean <- percentile
print(sprintf("percentile 1: %f",percentile))
# save(RNA, file="~/Desktop/RNA.Rdata")
# save(geneID, file="~/Desktop/geneID.Rdata")
if (percentile > 0){
RNAmean = apply(RNA,1,mean)
RNA_filtered1 = RNA[RNAmean > quantile(RNAmean, percentile)[[1]], ]
geneID_filtered1 = geneID[RNAmean > quantile(RNAmean, percentile)[[1]], ]
}
else {
RNA_filtered1 = RNA
geneID_filtered1 = as.matrix(geneID)
}
print("after remove lowest k% mean exp value:")
print(dim(RNA_filtered1))
incProgress(1/5, detail = "Remove lowest k% mean exp value")
# Remove data with lowest 10% variance across samples
percentile <- ifelse(is.na(input$variance_expval),0,input$variance_expval)/100.
percentile.var <- percentile
print(sprintf("percentile 2: %f",percentile))
if (percentile > 0){
if (dim(RNA_filtered1)[2] > 3){
index <- varFilter2(eset = RNA_filtered1, var.cutoff = percentile)
RNA_filtered2 = RNA_filtered1[index, ]
geneID_filtered2 = geneID_filtered1[index]
}
else{
smartModal(error=F, title = "Preprocessing input data", content = "Preprocessing ... \n Cannot calculate order statistic on object with less than 3 columns, will not remove data based on variance.")
RNA_filtered2 = RNA_filtered1
geneID_filtered2 = geneID_filtered1
}
}
else {
RNA_filtered2 = RNA_filtered1
geneID_filtered2 = geneID_filtered1
}
print("after remove lowest l% var exp value:")
print(dim(RNA_filtered2))
incProgress(1/5, detail = "Remove lowest l% var exp value")
# expData <- RNA_filtered2
# res <- highExpressionProbes(geneID_filtered2, geneID_filtered2, expData)
# ind1 <- res$first
# uniGene <- as.character(res$second)
# tmpExp <- expData[ind1,]
uniGene <- geneID_filtered2
tmpExp <- RNA_filtered2
if (input$checkbox_logarithm){
# tmpExp[tmpExp <= 0] <- 0.000001
tmpExp <- log2(tmpExp+1)
}
if (input$checkbox_empty){
print(sprintf("data dimension before remove gene with empty symbol: %d x %d",dim(tmpExp)[1],dim(tmpExp)[2]))
uniGene_temp <- subset(uniGene, nchar(as.character(uniGene)) > 0)
tmpExp <- subset(tmpExp, nchar(as.character(uniGene)) > 0)
uniGene <- uniGene_temp
print(sprintf("data dimension after remove gene with empty symbol: %d x %d",dim(tmpExp)[1],dim(tmpExp)[2]))
}
if (input$checkbox_duplicated){
row2remove <- numeric()
finalSymCharTable <- table(uniGene)
for (i in 1:length(finalSymCharTable)){
if (as.numeric(finalSymCharTable[i]) > 1){ # if exist duplicated Gene
genename <- names(finalSymCharTable[i])
idx_with_max_mean <- which.max(rowMeans(tmpExp[which(uniGene == genename),]))
# print(idx_with_max_mean)
row2remove <- c( row2remove, (which(uniGene == genename)[-idx_with_max_mean]) )
}
}
if (length(row2remove) > 0){ # Otherwise numerical(0) will remove all data in tmpExp
tmpExp <- tmpExp[-row2remove,]
uniGene <- uniGene[-row2remove]
}
print(sprintf("data dimension after remove duplicated gene symbol: %d x %d",dim(tmpExp)[1],dim(tmpExp)[2]))
}
incProgress(1/5, detail = "Sort genes based on mean.")
nSample <- ncol(tmpExp)
res <- sort.int(rowMeans(tmpExp), decreasing = TRUE, index.return=TRUE)
sortMean <- res$x
sortInd <- res$ix
# topN <- min(ifelse(is.na(input$max_gene_retain),Inf,input$max_gene_retain), nrow(tmpExp))
#Remove gene symbol after vertical line: from ABC|123 to ABC:
uniGene <- gsub("\\|.*$","", uniGene)
finalExp(tmpExp[sortInd, ])
finalSym(uniGene[sortInd])
finalSymChar(as.character(finalSym()))
# save(finalExp, file = "~/Desktop/finalExp.Rdata")
# save(finalSym, file = "~/Desktop/finalSym.Rdata")
# save(finalSymChar, file = "~/Desktop/finalSymChar.Rdata")
incProgress(1/5, detail = "\nPost-processing...")
data_final(data.frame(cbind(finalSym(), finalExp())))
data_final.temp <- data_final()
colnames(data_final.temp)[1] = "Gene_Symbol"
data_final(data_final.temp)
# data_final.temp <- data.frame(finalExp())
# rownames(data_final.temp) <- finalSym()
# data_final(data_final.temp)
#finally no matter if just basic or advanced:
sampleID(colnames(finalExp()))
output$mytable_finaldata <- DT::renderDataTable({
DT::datatable(data_final(),
extensions = 'Responsive', escape=F, selection = 'none')
})
removeModal()
}) # progress bar finished.
# showModal(modalDialog(
# title = "Data After Preprocessing", footer = modalButton("OK"), easyClose = TRUE,
# div(class = "busy",
# p(sprintf("%d genes ---- Original.", dim(RNA)[1])),
# p(sprintf("%d genes remained after remove lowest %.2f%% means.", dim(RNA_filtered1)[1], percentile.mean*100)),
# p(sprintf("%d genes remained after remove lowest %.2f%% variances.", dim(RNA_filtered2)[1], percentile.var*100)),
# style = "margin: auto; text-align: left"
# )
# ))
sendSweetAlert(session, title = "Data After Preprocessing",
text = sprintf("%d genes ---- Original.\n%d genes remained after remove lowest %.2f%% means.\n%d genes remained after remove lowest %.2f%% variances.",
dim(RNA)[1], dim(RNA_filtered1)[1], percentile.mean*100, dim(RNA_filtered2)[1], percentile.var*100),
type = "success",
btn_labels = "OK", html = FALSE, closeOnClickOutside = TRUE)
print('tab3')
session$sendCustomMessage("download_finaldata_ready","-")
session$sendCustomMessage("myCallbackHandler", "tab3")
})
# +------------------------------------------------------------+
# |
# |
# | l m Q C M
# |
# |
# +--------------------------------
observeEvent(input$action4_lmQCM,{
if(is.null(finalExp())){
smartModal(error=T, title = "Operation Failed", content = "You have not selected any data. Please go to previous section.")
return()
}
#lmQCM
smartModal(error=F, title = "lmQCM [1/2]", content = "Calculating massive correlation coefficient matrix...")
data_in = finalExp()
gamma = input$gamma
t = input$t
lambda = input$lambda
beta = input$beta
minClusterSize = input$minClusterSize
CCmethod = tolower(input$massiveCC)
normalization = input$lmQCM_weight_normalization
#----------------------------------------------------------------------------
# lmQCM start
#----------------------------------------------------------------------------
withProgress(message = 'lmQCM [1/2]: ', value = 0, {
incProgress(1/16, detail = "Calculating massive correlation coefficient matrix...")
cMatrix <- cor(t(data_in), method = CCmethod)
diag(cMatrix) <- 0
incProgress(1/2, detail = "Find the local maximal edges")
if(normalization){
# Normalization
cMatrix <- abs(cMatrix)
D <- rowSums(cMatrix)
D.half <- 1/sqrt(D)
cMatrix <- apply(cMatrix, 2, function(x) x*D.half )
cMatrix <- t(apply(cMatrix, 1, function(x) x*D.half ))
}
C <- list()
nRow <- nrow(cMatrix)
maxV <- apply(cMatrix, 2, max)
maxInd <- apply(cMatrix, 2, which.max) # several diferrences comparing with Matlab results
# Step 1 - find the local maximal edges
lm.ind <- which(maxV == sapply(maxInd, function(x) max(cMatrix[x,])))
maxEdges <- cbind(maxInd[lm.ind], lm.ind)
maxW <- maxV[lm.ind]
res <- sort.int(maxW, decreasing = TRUE, index.return=TRUE)
sortMaxV <- res$x
sortMaxInd <- res$ix
sortMaxEdges <- maxEdges[sortMaxInd,]
message(sprintf("Number of Maximum Edges: %d", length(sortMaxInd)))
incProgress(7/16, detail = sprintf("Number of Maximum Edges: %d", length(sortMaxInd)))
currentInit <- 1
noNewInit <- 0
nodesInCluster <- matrix(0, nrow = 0, ncol = 1)
})
removeModal()
pb <- progress_bar$new(format = " Calculating [:bar] :percent eta: :eta",
total = length(sortMaxInd), clear = F, width=60)
iter = 0
progressSweetAlert(
session = session, id = "myprogress",
title = "lmQCM [2/2] Merging ...",
display_pct = TRUE, value = 0
)
while ((currentInit <= length(sortMaxInd)) & (noNewInit == 0)) {
pb$tick()
iter = iter + 1
updateProgressBar(
session = session,
id = "myprogress",
value = iter/length(sortMaxInd)*100
)
if (sortMaxV[currentInit] < (gamma * sortMaxV[1]) ) {
noNewInit <- 1
}
else {
if ( (is.element(sortMaxEdges[currentInit, 1], nodesInCluster) == FALSE) & is.element(sortMaxEdges[currentInit, 2], nodesInCluster) == FALSE) {
newCluster <- sortMaxEdges[currentInit, ]
addingMode <- 1
currentDensity <- sortMaxV[currentInit]
nCp <- 2
totalInd <- 1:nRow
remainInd <- setdiff(totalInd, newCluster)
# C = setdiff(A,B) for vectors A and B, returns the values in A that
# are not in B with no repetitions. C will be sorted.
while (addingMode == 1) {
neighborWeights <- colSums(cMatrix[newCluster, remainInd])
maxNeighborWeight <- max(neighborWeights)
maxNeighborInd <- which.max(neighborWeights)
c_v = maxNeighborWeight/nCp;
alphaN = 1 - 1/(2*lambda*(nCp+t));
if (c_v >= alphaN * currentDensity) {
newCluster <- c(newCluster, remainInd[maxNeighborInd])
nCp <- nCp + 1
currentDensity <- (currentDensity*((nCp-1)*(nCp-2)/2)+maxNeighborWeight)/(nCp*(nCp-1)/2)
remainInd <- setdiff(remainInd, remainInd[maxNeighborInd]);
}
else {
addingMode <- 0
}
}
nodesInCluster <- c(nodesInCluster, newCluster)
C <- c(C, list(newCluster))
}
}
currentInit <- currentInit + 1
}
if(length(C) == 0) {
removeModal()
sendSweetAlert(
session = session,
title ="Clusters size = 0 before merging. Please try other set of parameters. Program stopped.",
type = "error"
)
return()
}
closeSweetAlert(session = session)
sendSweetAlert(
session = session,
title ="lmQCM Calculation completed !",
type = "success"
)
t <- try(clusters <- merging_lmQCM(C, beta, minClusterSize))
if("try-error" %in% class(t)) {
removeModal()
sendSweetAlert(
session = session,
title ="Too few genes to perform lmQCM clustering and merging.",
type = "error"
)
clusters <- C
}
# map rownames to clusters
clusters.names = list()
for (i in 1:length(clusters)){
mc = clusters[[i]]
clusters.names[[i]] = rownames(data_in)[mc]
}
# calculate eigengene
eigengene.matrix <- matrix(0, nrow = length(clusters), ncol = dim(data_in)[2]) # Clusters * Samples
for (i in 1:(length(clusters.names))) {
geneID <- as.matrix(clusters.names[[i]])
X <- data_in[geneID,]
mu <- rowMeans(X)
stddev <- rowSds(as.matrix(X), na.rm=TRUE) # standard deviation with 1/(n-1)
XNorm <- sweep(X,1,mu) # normalize X
XNorm <- apply(XNorm, 2, function(x) x/stddev)
SVD <- svd(XNorm, LINPACK = FALSE)
eigengene.matrix[i,] <- t(SVD$v[,1])
}
eigengene.matrix = data.frame(eigengene.matrix)
colnames(eigengene.matrix) = colnames(data_in)
mergedCluster <- methods::new("QCMObject", clusters.id = clusters, clusters.names = clusters.names,
eigengene.matrix = eigengene.matrix)
message("Done.")
#----------------------------------------------------------------------------
# lmQCM finished
#----------------------------------------------------------------------------
# if("try-error" %in% class(t)) {
# removeModal()
# smartModal(error=T, title = "Error in lmQCM",
# content = sprintf("Too few genes to do lmQCM merging."))
# return()
# }
mergedCluster <- mergedCluster@clusters.id
geneCharVector <- matrix(0, nrow = 0, ncol = length(mergedCluster))
temp_eigengene <- matrix(0, nrow = length(mergedCluster), ncol = dim(finalExp())[2]) # Clusters * Samples
temptext <- ""
for (i in 1:(length(mergedCluster))) {
vector <- as.matrix(mergedCluster[[i]])
geneID <- vector
print(i)
print(vector)
# ===== Calculate Eigengene Start
X <- finalExp()[geneID,]
mu <- rowMeans(X)
stddev <- rowSds(as.matrix(X), na.rm=TRUE) # standard deviation with 1/(n-1)
#normalize X:
XNorm <- sweep(X,1,mu)
XNorm <- apply(XNorm, 2, function(x) x/stddev)
SVD <- svd(XNorm, LINPACK = FALSE)
temp_eigengene[i,] <- t(SVD$v[,1])
# ===== Calculate Eigengene Finished
geneChar <- c(toString(i), finalSymChar()[vector])
geneCharVector[i] <- list(geneChar)
temptext <- paste(temptext, capture.output(cat(geneChar, sep=',')), sep="\n")
}
temptext <- substring(temptext, 2) # remove firstfinal_genes_str \n separater
geneCharVector_global(geneCharVector)
text.final(temptext)
colnames(temp_eigengene) <- sampleID()
eigengene_matrix(temp_eigengene)
output$eigengene_matrix_select_row_ui <- renderUI({
selectInput(inputId="eigengene_matrix_select_row", label="Please select row:",
choices = 1:dim(eigengene_matrix())[1], selected = 1, multiple = FALSE,
selectize = TRUE, width = NULL, size = NULL)
})
## Compute maximum length
max.length <- max(sapply(geneCharVector, length))
## Add NA values to list elements
geneCharVector2 <- lapply(geneCharVector, function(v) { c(v, rep(NA, max.length-length(v)))})
## Rbind
geneCharVector2 <- data.frame(do.call(rbind, geneCharVector2))
output$clusterResult <- DT::renderDataTable({
geneCharVector2[["Actions"]] <- paste0('<div style="text-align: center"><button type="button" class="btn-analysis" id=go_analysis_',1:nrow(geneCharVector2),'>GO</button></div>')
geneCharVector2[["Plots"]] <- paste0('<div style="text-align: center"><button type="button" class="btn-plot" id=circos_',1:nrow(geneCharVector2),'>Circos</button></div>')
geneCharVector2 <- geneCharVector2 %>%
select(c("Actions","Plots"), everything())
colnames(geneCharVector2)[3:4] <- c("Cluster ID", "Genes")
# print(head(geneCharVector2))
DT::datatable(geneCharVector2, selection="none", escape=FALSE,
options = list(paging = F, searching = F, dom='t',ordering=T),
rownames = F#, colnames = NULL
)
})
output$mytable7 <- renderTable({
return(eigengene_matrix())
},rownames = TRUE, colnames = TRUE, na = "", bordered = TRUE, digits = 4)
removeModal()
print('tab4')
session$sendCustomMessage("download_cluster_ready","-")
session$sendCustomMessage("myCallbackHandler", "tab4")
})
#=================================================
#=================== ===================
#================== W G C N A ==================
#=================== ===================
#=================================================
observeEvent(input$checkPower, {
if(is.null(finalExp())){
smartModal(error=T, title = "Operation Failed", content = "You have not selected any data. Please go to previous section.")
return()
}
if (length(finalSym()) > 0){
#WGCNA
smartModal(error=F, title = "Preview the power", content = "Running ...")
finalExp.temp <- finalExp()
row.names(finalExp.temp) <- finalSym()
finalExp(finalExp.temp)
datExpr <- t(finalExp()) # gene should be colnames, sample should be rownames
# datExpr <- log(datExpr + 1) # uncomment if don't need logarithm
print(dim(datExpr))
# The following setting is important, do not omit.
options(stringsAsFactors = FALSE);
allowWGCNAThreads() #enableWGCNAThreads()
#=====================================================================================