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server.R
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################################## Server #####################################
.libPaths(c(.libPaths(), '/home/ubuntu/R/x86_64-pc-linux-gnu-library/3.6/'))
library(shiny)
library(shinydashboard)
library(shinydashboardPlus)
library(shinyjs)
#library(shinyWidgets)
library(shinycssloaders)
#library(readxl)
library(ggplot2)
library(Matrix)
library(stringr)
library(Biobase)
library(survival)
library(survminer)
library(limma)
library(edgeR)
library(DT)
library(dplyr)
library(pROC)
library(ROCR)
library(digest)
library(pheatmap)
library(plotly)
library(glmnet)
library(heatmaply)
library(htmlwidgets)
library(slickR)
library(dashboardthemes)
library(shinythemes)
library(shinyalert)
source('script/shiny_functions.R')
google.red <- '#ea4235'
google.yellow <- '#fabd03'
google.green <- '#34a853'
google.blue <- '#4286f5'
################################## Data #####################################
# ### TCGA Datasets
# tcga.datasets <- readRDS('data/TCGA_Projects.RDS')
#
# projects.tcga <- c("TCGA-ACC","TCGA-BLCA","TCGA-BRCA","TCGA-CESC",
# "TCGA-CHOL","TCGA-COAD","TCGA-DLBC","TCGA-ESCA",#"TCGA-GBM",
# "TCGA-HNSC","TCGA-KICH","TCGA-KIRC","TCGA-KIRP",
# "TCGA-LAML","TCGA-LGG","TCGA-LIHC","TCGA-LUAD",
# "TCGA-LUSC","TCGA-MESO","TCGA-OV","TCGA-PAAD",
# "TCGA-PCPG","TCGA-PRAD","TCGA-READ","TCGA-SARC",
# "TCGA-SKCM","TCGA-STAD","TCGA-TGCT","TCGA-THCA",
# "TCGA-THYM","TCGA-UCEC","TCGA-UCS","TCGA-UVM")
#
# projects.tcga.sub <- c("TCGA-BLCA","TCGA-BRCA","TCGA-CESC","TCGA-CHOL",
# "TCGA-COAD","TCGA-ESCA","TCGA-HNSC","TCGA-KICH",
# "TCGA-KIRC","TCGA-KIRP","TCGA-LIHC","TCGA-LUAD",
# "TCGA-LUSC","TCGA-PAAD","TCGA-PCPG","TCGA-PRAD",
# "TCGA-READ","TCGA-SKCM","TCGA-STAD","TCGA-THCA",
# "TCGA-THYM","TCGA-UCEC")
#
# ### CCMA Datasets
# ccma.datasets <- readRDS('data/miRNomes_Datasets.RDS')
# ccma.datasets$Name <- paste0(ccma.datasets$Dataset, ': ', ccma.datasets$Title)
# ccma.primary <- readRDS('data/miRNomes_Datasets_Primary.RDS')
# ccma.primary <- ccma.primary[,c(1:3,5)]
#
# ### TCGA Data
# meta.tcga <- readRDS('data/Metadata_TCGA.RDS')
# mir.tcga <- readRDS('data/miRNA_Expression_TCGA.RDS')
# #rna.tcga <- readRDS('data/RNAseq_Expression_TCGA.miRTarBase.RDS')
#
#
# ### TCGA Data Analysis
# expr.high.tcga <- readRDS(file='data/Highly.Expressed.miRNAs.TCGA.RDS')
#
# km.tcga <- readRDS('data/Survival.KM.TCGA.RDS')
# coxph.tcga <- readRDS('data/Survival.CoxPH.TCGA.RDS')
#
# lasso.tcga <- readRDS('data/Survival.Lasso.Feature.TCGA.RDS')
# lasso.plot.tcga <- readRDS('data/Survival.Lasso.Plot.TCGA.RDS')
#
# #risk.km.plot.tcga <- readRDS(file='data/Survival.KM.Risk.Plot.TCGA.RDS')
# risk.km.data.tcga <- readRDS(file='data/Survival.KM.Risk.Data.TCGA.RDS')
#
# tcga.feature.table <- readRDS('data/Lasso.Feature.Table.RDS')
# tcga.feature.plot <- readRDS('data/Lasso.Feature.Plot.RDS')
#
# roc.tcga <- readRDS('data/ROC.Analysis.TCGA.RDS')
# surv.roc.plot.tcga <- readRDS(file='data/Survival.ROC.Risk.Plot.TCGA.RDS')
#
# pca.tcga <- readRDS(file='data/PCA.Analysis.TCGA.RDS')
#
# ### Correlation/Functional Analysis
# #cor.table <- readRDS('data/Correlation.miRTarBase.RDS')
# enrichment.table <- readRDS('data/Enrichment.miRTarBase.RDS')
#
#
#
# ### CCMA Data Analysis
# expr.high.ccma <- readRDS(file='data/Highly.Expressed.miRNAs.CCMA.RDS')
#
# #expr.ccma <- readRDS('data/miRNomes_Expression.RDS')
# meta.ccma <- readRDS('data/miRNomes_Metadata.RDS')
#
# pca.ccma <- readRDS(file='data/PCA.Analysis.CCMA.RDS')
################################## Input #####################################
###### miRNA
mir.default <- 'MIMAT0000062' # hsa-let-7a-5p
mir.annotation <- readRDS('data/miRBase_10.0_22.RDS')
mir.id <- selectizeInput(inputId = "mir.id", label=h4(strong('Search a miRNA')), #list(h4('Search a miRNA:'), icon('search', 'fa-1.5x')),# h4(strong('miRNA'))
choices = NULL, selected = mir.default, #mir.default,
multiple = FALSE, width = 400,
options = list(placeholder = 'e.g. hsa-miR-7a-5p', # or MIMAT0000062
server = TRUE, selectOnTab=TRUE,
searchField = c('Name', 'ID', 'Previous_ID'),
labelField = "Name",
valueField = "ID",
#maxOptions = 5,
render = I("{option: function(item, escape)
{var gene = '<div>' + '<strong>' + escape(item.Name) + '</strong>' + '<ul>';
gene = gene + '<li>' + 'Previous IDs: ' + item.Previous_ID + '</li>';
gene = gene + '<li>' + 'Accession: ' + item.ID + '</li>' + '</ul>' + '</div>';
return gene
}
}")
))
circulating.expression.default <- 'GSE106817' # hsa-let-7a-5p
#mir.annotation <- readRDS('data/miRBase_10.0_22.RDS')
circulating.expression.id <- selectizeInput(inputId = "circulating.expression.id",
label=h4(strong('Select a dataset')), #list(h4('Search a miRNA:'), icon('search', 'fa-1.5x')),# h4(strong('miRNA'))
choices = NULL, selected = circulating.expression.default, #mir.default,
multiple = FALSE, width = 600,
options = list(placeholder = 'e.g. GSE106817',
server = TRUE, selectOnTab=TRUE,
searchField = c('Name', 'Disease'),
labelField = "Name",
valueField = "Dataset",
#maxOptions = 5,
render = I("{option: function(item, escape)
{var gene = '<div>' + '<strong>' + escape(item.Name) + '</strong>' + '<ul>';
gene = gene + '<li>' + 'Sample types: ' + item.Disease + '</li>' + '</ul>' + '</div>';
return gene
}
}")
))
###### TCGA Project
project.default <- 'TCGA-BLCA'
project.id <- selectizeInput(inputId = "project.id", label=h5(strong('TCGA Project:')),# h4(strong('miRNA'))
choices = NULL, selected = project.default,
multiple = FALSE, width = 150,
options = list(placeholder = 'Select a project',
server = TRUE, selectOnTab=TRUE
))
project.id.cor <- selectizeInput(inputId = "project.id.cor", label=h5(strong('TCGA Project:')),# h4(strong('miRNA'))
choices = NULL, selected = project.default,
multiple = FALSE, width = 150,
options = list(placeholder = 'Select a project',
server = TRUE, selectOnTab=TRUE
))
###### Gene Sets
gene.sets <- c('Kyoto Encyclopedia of Genes and Genomes (KEGG)' = 'KEGG',
'REACTOME' = 'REACTOME',
'Disease Ontology (DO)' = 'DO',
'Network of Cancer Gene (NCG)' = 'NCG',
'DisGeNET' = 'DGN',
'Gene Ontology - Biological Process (GO-BP)' = 'GOBP',
'Gene Ontology - Cellular Component (GO-CC)' = 'GOCC',
'Gene Ontology - Molecular Function (GO-MF)' = 'GOMF',
'MSigDB - H:HALLMARK' = 'MSigDBHALLMARK',
'MSigDB - C4:Cancer Gene Neighborhoods' = 'MSigDBC4CGN',
'MSigDB - C4:Cancer Modules' = 'MSigDBC4CM',
'MSigDB - C6:Oncogenic Signatures' = 'MSigDBC6'#,
#'MSigDB - C7:Immunologic Signatures' = 'MSigDBC7'
)
geneset.id.default <- gene.sets[1]
geneset.id <- selectizeInput(inputId = "geneset.id", label=h5(strong('Gene Sets:')),# h4(strong('miRNA'))
choices = NULL, selected = geneset.id.default,
multiple = FALSE, width = 410,
options = list(placeholder = 'Select a gene set',
server = TRUE, selectOnTab=TRUE
))
## Survival
survival.analysis <- c('Univariate Survival Analysis' = 'Univariate',
'Pre-built Prognostic Model' = 'Pre-built',
'User-provided Prognostic Signature' = 'User-provided'
)
survival.analysis.default <- survival.analysis[1]
survival_analysis_input <- selectizeInput(inputId = "survival_analysis_input", label=h5(strong('Survival Analysis Modules:')),# h4(strong('miRNA'))
choices = NULL, selected = survival.analysis.default,
multiple = FALSE, width = 310,
options = list(placeholder = NULL,
server = TRUE, selectOnTab=TRUE
))
#$("button.buttons-copy").css("border","grey");
# table.download.button <- JS('$("button.buttons-copy").css("background","white");
# $("button.buttons-copy").css("width",40);
# $("button.buttons-copy").css("height",20);
# $("button.buttons-copy").css("font-size",10);
# $("button.buttons-csv").css("background","lightskyblue");
# $("button.buttons-csv").css("width",40);
# $("button.buttons-csv").css("height",24);
# $("button.buttons-csv").css("font-size",10);
# $("button.buttons-csv").css("border","white");
# $("button.buttons-excel").css("background","lightskyblue");
# $("button.buttons-excel").css("border","white");
# $("button.buttons-excel").css("width",40);
# $("button.buttons-excel").css("height",24);
# $("button.buttons-excel").css("font-size",10);
# return table;')
table.download.button <- JS('$("button.buttons-copy").css("font-size",12);
$("button.buttons-csv").css("font-size",12);
$("button.buttons-excel").css("font-size",12);
return table;')
################################## Server #####################################
server <- function(input, output, session) {
output$slick_output <- renderSlickR({
imgs <- c('img/figure1.jpg','img/figure2.jpg','img/figure3.jpg')
slickR(imgs, height = 500, width='100%') + settings(dots = TRUE, autoplay = TRUE, autoplaySpeed = 3000)
})
updateSelectizeInput(session, 'mir.id', choices = mir.annotation, selected = mir.default, server = TRUE)
updateSelectizeInput(session, 'circulating.expression.id', choices = ccma.datasets, selected = circulating.expression.default, server = TRUE)
updateSelectizeInput(session, 'project.id', choices = projects.tcga, selected = project.default, server = TRUE)
updateSelectizeInput(session, 'project.id.cor', choices = projects.tcga, selected = project.default, server = TRUE)
updateSelectizeInput(session, 'geneset.id', choices = gene.sets, selected = geneset.id.default, server = TRUE)
updateSelectizeInput(session, 'survival_analysis_input', choices = survival.analysis, selected = survival.analysis.default, server = TRUE)
seed <- reactiveVal()
# observe({
# if (input$navbar == "home") {
# js$refresh();
# }
# })
################################################################
######################## Information ###########################
observeEvent(input$mir.id, {
#req(input$mir.id)
output$mir.name <- renderUI({
mir.id <- input$mir.id
mir.name <- mir.annotation[mir.id, 'Name']
mir.url <- paste0('http://www.mirbase.org/cgi-bin/mature.pl?mature_acc=', mir.id)
mir.url <- a(mir.name, href = mir.url, target="_blank", style = "font-size:150%; color:#21ADA8; font-family:Georgia") # #fabd03
if (is.na(mir.name)) {
return('')
} else {
tagList(mir.url)
}
})
output$mir.preid <- renderText({
mir.id <- input$mir.id
mir.preid <- mir.annotation[mir.id, 'Previous_ID']
mir.preid <- paste0('Previous IDs: ', ifelse(is.na(mir.preid), '', mir.preid))
mir.preid
})
output$mir.info <- renderText({
mir.id <- input$mir.id
mir.name <- mir.annotation[mir.id, 'Name']
mir.info <- paste0('Accession: ', ifelse(is.na(mir.id), '', mir.id))
mir.info
})
output$mir.seq <- renderText({
mir.id <- input$mir.id
mir.seq <- mir.annotation[mir.id, 'Sequence']
mir.seq <- paste0('Sequence: ', ifelse(is.na(mir.seq), '', mir.seq))
mir.seq
})
output$mir.targets <- renderUI({
mir.id <- input$mir.id
mir.name <- mir.annotation[mir.id, 'Name']
mir.encori <- paste0('http://starbase.sysu.edu.cn/agoClipRNA.php?source=mRNA&flag=miRNA&clade=mammal&genome=human&assembly=hg19&miRNA=',
mir.name, '&clipNum=&deNum=&panNum=&proNum=&program=&target=')
mir.encori <- a('ENCORI', href = mir.encori, target="_blank", style = "font-size:100%; color:#3b8dbc")
mir.mirdb <- paste0('http://mirdb.org/cgi-bin/search.cgi?searchType=miRNA&full=mirbase&searchBox=',mir.id)
mir.mirdb <- a('miRDB', href = mir.mirdb, target="_blank", style = "font-size:100%; color:#3b8dbc;")
mir.mirtarbase <- paste0('https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2019/php/search.php?org=hsa&opt=mirna_id&kw=',mir.name)
mir.mirtarbase <- a('miRTarBase', href = mir.mirtarbase, target="_blank", style = "font-size:100%; color:#3b8dbc;")
mir.targetscan <- paste0('http://www.targetscan.org/cgi-bin/targetscan/vert_72/targetscan.cgi?mirg=',mir.name)
mir.targetscan <- a('TargetScan', href = mir.targetscan, target="_blank", style = "font-size:100%; color:#3b8dbc;")
mir.dianatarbase <- paste0('http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index&mirnas=',mir.name)
mir.dianatarbase <- a('Diana-TarBase', href = mir.dianatarbase, target="_blank", style = "font-size:100%; color:#3b8dbc;")
if (is.na(mir.name)) {
tagList("Targets:")
} else {
tagList("Targets:", mir.encori, mir.mirdb, mir.mirtarbase, mir.targetscan, mir.dianatarbase)
}
})
})
#########################################################
######################## TCGA ###########################
observeEvent(input$mir.id, {
#req(input$mir.id)
mir.id <- input$mir.id
mir.name <- mir.annotation[mir.id, 'Name']
tcga.overview <- reactiveValues()
output$tcga_boxplot <- renderPlot({
if (mir.id=='') {
return()
}
sample <- unlist(lapply(meta.tcga, function(x) x[,'sample']))
group <- unlist(lapply(meta.tcga, function(x) x[,'sample_type']))
expr <- unlist(lapply(mir.tcga, function(x) x[mir.id,]))
project <- unlist(lapply(meta.tcga, function(x) x[,'project_id']))
dataForBoxPlot <- data.frame(mir=mir.name, project, sample, group, expr, stringsAsFactors = F)
tcga.overview$box.data <- dataForBoxPlot
p <- tcgaboxplotFun(dataForBoxPlot)
tcga.overview$box.plot <- p
p
}, height = 400)
output$tcga.box.summ.downbttn.csv <- downloadHandler(
filename = function(){paste(mir.name,'.TCGA_PanCancer_Expression_Data.csv', sep = '')},
content = function(file){
write.csv(tcga.overview$box.data, file, row.names = FALSE, quote = F)
})
output$tcga.box.summ.downbttn.png <- downloadHandler(
filename = function(){paste(mir.name,'.TCGA_PanCancer_Expression_BoxPlot.png', sep = '')},
content = function(file){
png(file, width = 1000, height = 500)
print(tcga.overview$box.plot)
dev.off()
})
output$tcga.box.summ.downbttn.pdf <- downloadHandler(
filename = function(){paste(mir.name,'.TCGA_PanCancer_Expression_BoxPlot.pdf', sep = '')},
content = function(file){
pdf(file, width = 10, height = 5)
print(tcga.overview$box.plot)
dev.off()
})
output$tcga_rocplot_forest <- renderPlot({
if (mir.id=='') {
return()
}
#sample <- unlist(lapply(meta.tcga, function(x) x[,'sample']))
group <- unlist(lapply(meta.tcga, function(x) x[,'sample_type']))
expr <- unlist(lapply(mir.tcga, function(x) x[mir.id,]))
project <- unlist(lapply(meta.tcga, function(x) x[,'project_id']))
dataForForestPlot <- c()
for (prj in projects.tcga.sub) {
idx <- which(project==prj)
n.tumor <- sum(group[idx]=='Tumor')
n.normal <- sum(group[idx]=='Normal')
roc.test <- roc(group[idx], expr[idx], plot=FALSE, ci=TRUE, auc=TRUE)
ci.auc <- roc.test$ci
auc <- ci.auc[2]
auc.ci.lower95 <- ci.auc[1]
auc.ci.upper95 <- ci.auc[3]
auc <- format(auc, digits = 2, nsmall=2)
auc.ci.lower95 <- format(auc.ci.lower95, digits = 2, nsmall=2)
auc.ci.upper95 <- format(auc.ci.upper95, digits = 2, nsmall=2)
# pred <- prediction(expr[idx], group[idx])
# perf <- performance(pred, measure = "tpr", x.measure = "fpr")
#
# FPR <- [email protected][[1]]
# TPR <- [email protected][[1]]
#
# #df <- data.frame(FPR,TPR)
#
# auc.test <- wilcox.test(FPR, TPR, alternative = 'two.sided')
# pvalue <- formatC(auc.test$p.value, format = 'e', digits = 2)
dataForForestPlot <- rbind(dataForForestPlot,
c(#mir=mir.name, project=prj,
n.tumor, n.normal, auc, auc.ci.lower95, auc.ci.upper95)) #, pvalue
}
dataForForestPlot <- apply(dataForForestPlot, 2, as.numeric)
dataForForestPlot <- data.frame(dataForForestPlot,
row.names = projects.tcga.sub,
stringsAsFactors = F)
colnames(dataForForestPlot) <- c('N.Tumor','N.Normal','AUC','Lower95','Upper95') #,'P.Value'
dataForForestPlot$mir <- mir.name
dataForForestPlot$Project <- projects.tcga.sub
o <- order(dataForForestPlot$AUC, decreasing = F)
dataForForestPlot <- dataForForestPlot[o,]
# dataForForestPlot$Project <- factor(dataForForestPlot$Project,
# levels = dataForForestPlot$Project)
tcga.overview$roc.forest.data <- dataForForestPlot
p <- tcgaROCForestplotFunT(dataForForestPlot)
tcga.overview$roc.forest.plot <- p
p
}, height = 750)
output$tcga.roc.forest.downbttn.csv <- downloadHandler(
filename = function(){paste(mir.name,'.TCGA_PanCancer_ROC_Data.csv', sep = '')},
content = function(file){
write.csv(tcga.overview$roc.forest.data, file, row.names = FALSE, quote = F)
})
output$tcga.roc.forest.downbttn.png <- downloadHandler(
filename = function(){paste(mir.name,'.TCGA_PanCancer_ROC_ForestPlot.png', sep = '')},
content = function(file){
png(file, width = 1000, height = 500)
print(tcga.overview$roc.forest.plot)
dev.off()
})
output$tcga.roc.forest.downbttn.pdf <- downloadHandler(
filename = function(){paste(mir.name,'.TCGA_PanCancer_ROC_ForestPlot.pdf', sep = '')},
content = function(file){
pdf(file, width = 13, height = 11)
print(tcga.overview$roc.forest.plot)
dev.off()
})
output$tcga_km_forest <- renderPlot({
mir.id <- input$mir.id
if (mir.id=='') {
return()
}
mir.name <- mir.annotation[mir.id, 'Name']
group <- unlist(lapply(meta.tcga, function(x) x[,'sample_type']))
expr <- unlist(lapply(mir.tcga, function(x) x[mir.id,]))
project <- unlist(lapply(meta.tcga, function(x) x[,'project_id']))
os.time <- unlist(lapply(meta.tcga, function(x) as.numeric(x[,'OS.time'])/30))
os.status <- unlist(lapply(meta.tcga, function(x) as.numeric(x[,'OS'])))
dataForForestPlot <- c()
for (prj in projects.tcga) {
idx <- which(project==prj & group=='Tumor')
surv.data <- data.frame(expr=expr[idx],
os.time=os.time[idx],
os.status=os.status[idx],
stringsAsFactors = F)
km <- kmTest(exprDa=surv.data$expr, daysToDeath=surv.data$os.time, vitalStatus=surv.data$os.status)
dataForForestPlot <- rbind(dataForForestPlot, c(length(idx), as.numeric(format(km, digits=3))))
}
dataForForestPlot <- apply(dataForForestPlot, 2, as.numeric)
dataForForestPlot <- data.frame(dataForForestPlot,
row.names = projects.tcga,
stringsAsFactors = F)
colnames(dataForForestPlot) <- c('Patients','HR','Lower95','Upper95','P.Value')
dataForForestPlot$mir <- mir.name
dataForForestPlot$Project <- projects.tcga
o <- order(dataForForestPlot$HR, decreasing = F)
dataForForestPlot <- dataForForestPlot[o,]
# dataForForestPlot$Project <- factor(dataForForestPlot$Project,
# levels = dataForForestPlot$Project)
tcga.overview$km.forest.data <- dataForForestPlot
dataForForestPlot$HR <- ifelse(dataForForestPlot$HR>100, 100, dataForForestPlot$HR)
dataForForestPlot$Upper95 <- ifelse(dataForForestPlot$Upper95>100, Inf, dataForForestPlot$Upper95)
filter <- which(dataForForestPlot$Upper95==Inf | dataForForestPlot$Lower95==-Inf | is.na(dataForForestPlot$HR))
if (length(filter)>0) {
dataForForestPlot <- dataForForestPlot[-filter,]
}
if (nrow(dataForForestPlot)==0) {
p <- ggplot()
}
p <- tcgaKMForestplotFunT(dataForForestPlot)
tcga.overview$km.forest.plot <- p
p
}, height = 950)
output$tcga.km.forest.downbttn.csv <- downloadHandler(
filename = function(){paste(mir.name,'.TCGA_PanCancer_KM_Survival_Data.csv', sep = '')},
content = function(file){
write.csv(tcga.overview$km.forest.data, file, row.names = FALSE, quote = F)
})
output$tcga.km.forest.downbttn.png <- downloadHandler(
filename = function(){paste(mir.name,'.TCGA_PanCancer_KM_Survival_ForestPlot.png', sep = '')},
content = function(file){
png(file, width = 1350, height = 1350)
print(tcga.overview$km.forest.plot)
dev.off()
})
output$tcga.km.forest.downbttn.pdf <- downloadHandler(
filename = function(){paste(mir.name,'.TCGA_PanCancer_KM_Survival_ForestPlot.pdf', sep = '')},
content = function(file){
pdf(file, width = 13.5, height = 13.5)
print(tcga.overview$km.forest.plot)
dev.off()
})
observeEvent(input$project.id, {
project <- input$project.id
tcga.mir.project <- reactiveValues()
output$tcga_violinplot <- renderPlot({
#mir.id <- input$mir.id
if (mir.id=='') {
return()
}
group <- meta.tcga[[project]][,'sample_type']
expr <- mir.tcga[[project]][mir.id,]
sample <- meta.tcga[[project]][,'sample']
dataForBoxPlot <- data.frame(mir=mir.name, project, sample,
group, expr, stringsAsFactors = F)
tcga.mir.project$box.data <- dataForBoxPlot
p <- BoxPlotFun(dataForBoxPlot)
tcga.mir.project$box.plot <- p
p
})
output$tcga.box.downbttn.csv <- downloadHandler(
filename = function(){paste(mir.name,'.', project, '.Expression_Data.csv', sep = '')},
content = function(file){
write.csv(tcga.mir.project$box.data, file, row.names = FALSE, quote = F)
})
output$tcga.box.downbttn.png <- downloadHandler(
filename = function(){paste(mir.name,'.', project, '.Expression_BoxPlot.png', sep = '')},
content = function(file){
png(file, width = 500, height = 500)
print(tcga.mir.project$box.plot)
dev.off()
})
output$tcga.box.downbttn.pdf <- downloadHandler(
filename = function(){paste(mir.name,'.', project, '.Expression_BoxPlot.pdf', sep = '')},
content = function(file){
pdf(file, width = 5, height = 5)
print(tcga.mir.project$box.plot)
dev.off()
})
output$tcga_rocplot <- renderPlot({
if (mir.id=='') {
return()
}
group <- meta.tcga[[project]][,'sample_type']
expr <- mir.tcga[[project]][mir.id,]
sample <- meta.tcga[[project]][,'sample']
dataForROCPlot <- data.frame(mir=mir.name, project, sample, group, expr,
stringsAsFactors = F)
tcga.mir.project$roc.data <- dataForROCPlot
dataForROCPlot$group <- ifelse(dataForROCPlot$group=='Normal',0,1)
p <- rocplotFun(dataForROCPlot)
tcga.mir.project$roc.plot <- p
p
})
output$tcga.roc.downbttn.csv <- downloadHandler(
filename = function(){paste(mir.name,'.', project, '.ROC_Data.csv', sep = '')},
content = function(file){
write.csv(tcga.mir.project$roc.data, file, row.names = FALSE, quote = F)
})
output$tcga.roc.downbttn.png <- downloadHandler(
filename = function(){paste(mir.name,'.', project, '.ROC_Curve.png', sep = '')},
content = function(file){
png(file, width = 500, height = 500)
print(tcga.mir.project$roc.plot)
dev.off()
})
output$tcga.roc.downbttn.pdf <- downloadHandler(
filename = function(){paste(mir.name,'.', project, '.ROC_Curve.pdf', sep = '')},
content = function(file){
pdf(file, width = 5, height = 5)
print(tcga.mir.project$roc.plot)
dev.off()
})
output$tcga_km_plot <- renderPlot({
if (mir.id=='') {
return()
}
group <- meta.tcga[[project]][,'sample_type']
expr <- mir.tcga[[project]][mir.id,]
sample <- meta.tcga[[project]][,'sample']
idx <- which(group=='Tumor')
os.time <- as.numeric(meta.tcga[[project]][,'OS.time'])/30
os.status <- as.numeric(meta.tcga[[project]][,'OS'])
dataForKMPlot <- data.frame(mir=mir.name,project,
sample=sample[idx],
expr=expr[idx],
os.time=os.time[idx],
os.status=os.status[idx],
stringsAsFactors = F)
p <- KMPlotFun(dataForKMPlot)
tcga.mir.project$km.data <- dataForKMPlot
tcga.mir.project$km.plot <- p
p
})
output$tcga.km.downbttn.csv <- downloadHandler(
filename = function(){paste(mir.name,'.', project, '.KM_Survival_Data.csv', sep = '')},
content = function(file){
write.csv(tcga.mir.project$km.data, file, row.names = FALSE, quote = F)
})
output$tcga.km.downbttn.png <- downloadHandler(
filename = function(){paste(mir.name,'.', project, '.KM_Survival_Curve.png', sep = '')},
content = function(file){
png(file, width = 500, height = 500)
print(tcga.mir.project$km.plot)
dev.off()
})
output$tcga.km.downbttn.pdf <- downloadHandler(
filename = function(){paste(mir.name,'.', project, '.KM_Survival_Curve.pdf', sep = '')},
content = function(file){
pdf(file, width = 5, height = 5)
print(tcga.mir.project$km.plot)
dev.off()
})
})
observeEvent(input$project.id.cor, {
cor.list <- reactiveValues()
output$correlation <- DT::renderDataTable({
# if (!exists('cor.table')) {
# cor.table <- readRDS('data/Correlation.miRTarBase.RDS')
# }
mir <- input$mir.id
project <- input$project.id.cor
cor.table <- getCorTable(project = project, mir = mir)
cor.list$cor.table <- cor.table
cor.table
},
callback=table.download.button,
options = list(pageLength = 5, dom = 'lfrtBp', buttons = c('copy', 'csv', 'excel')), #i
extensions = "Buttons",
selection = list(mode='single', selected=1),
server = FALSE
)
observe({ #Event(input$correlation_rows_selected,
req(input$mir.id, input$correlation_rows_selected)
mir <- input$mir.id
project <- input$project.id.cor
cor.table <- cor.list$cor.table #getCorTable(project = project, mir = mir)
idx <- input$correlation_rows_selected
mir.id <- cor.table[idx, 'miRNA.Accession']
mir.name <- cor.table[idx, 'miRNA.ID']
target.id <- cor.table[idx, 'Target.Ensembl']
target.name <- cor.table[idx, 'Target.Symbol']
tcga.cor <- reactiveValues()
output$cor_plot <- renderPlot({
if (mir.id=='') {
return()
}
rna.tcga <- getRNATable(project)
colnames(rna.tcga) <- gsub('.', '-', colnames(rna.tcga), fixed = T)
samples <- intersect(colnames(mir.tcga[[project]]), colnames(rna.tcga))
mir.expr <- as.numeric(mir.tcga[[project]][mir.id,samples])
rna.expr <- as.numeric(rna.tcga[target.id,samples])
group <- meta.tcga[[project]][samples,'sample_type']
coef <- cor.table[idx, 'Correlation']
p.val <- cor.table[idx, 'P.Value']
dataForCorrPlot <- data.frame(mir.expr, rna.expr, group, project,
mir.id, target.id, mir.name, target.name,
coef, p.val, stringsAsFactors = F)
p <- ExprCorrPlotFun(dataForCorrPlot)
tcga.cor$cor.data <- dataForCorrPlot
tcga.cor$cor.plot <- p
p
})
output$tcga.cor.downbttn.csv <- downloadHandler(
filename = function(){paste(mir.name, '.', target.name, '.', project,
'.Correlation_Data.csv', sep = '')},
content = function(file){
write.csv(tcga.cor$cor.data, file, row.names = FALSE, quote = F)
})
output$tcga.cor.downbttn.png <- downloadHandler(
filename = function(){paste(mir.name, '.', target.name, '.', project,
'.Correlation_Plot.png', sep = '')},
content = function(file){
png(file, width = 500, height = 500)
print(tcga.cor$cor.plot)
dev.off()
})
output$tcga.cor.downbttn.pdf <- downloadHandler(
filename = function(){paste(mir.name, '.', target.name, '.', project,
'.Correlation_Plot.pdf', sep = '')},
content = function(file){
pdf(file, width = 6, height = 5)
print(tcga.cor$cor.plot)
dev.off()
})
output$cor_heatmap <- renderPlotly({
if (mir.id=='') {
return()
}
req(tcga.cor$cor.plot)
cor.val <- c()
p.val <- c()
for (prj in projects.tcga) {
cor.mir.target <- getCorData(project = prj,mir=mir,target = target.id)
cor <- as.numeric(cor.mir.target$Correlation)
p <- as.numeric(cor.mir.target$P.Value)
cor.val <- c(cor.val, cor)
p.val <- c(p.val, p)
}
o <- order(cor.val, decreasing = F)
cor.val <- cor.val[o]
p.val <- p.val[o]
projects <- projects.tcga[o]
anno <- as.character(symnum(p.val, #corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 1),
symbols = c("***",'**','*','')))
htmp.data <- data.frame(t(cor.val))
rownames(htmp.data) <- paste0(mir.name, ':', target.name)
colnames(htmp.data) <- projects
anno.data <- data.frame(t(anno))
rownames(anno.data) <- paste0(mir.name, ':', target.name)
colnames(anno.data) <- projects
hover.text <- data.frame(t(paste0('Project: ', projects,'\nCorrelation: ',cor.val,'\nP Value: ', p.val)))
rownames(hover.text) <- paste0(mir.name, ':', target.name)
colnames(hover.text) <- projects
max.cor <- max(abs(htmp.data))
colors <- c(google.blue, 'white', google.red)
p <- heatmaply(htmp.data,
plot_method = 'plotly',
# scale_fill_gradient_fun = ggplot2::scale_fill_gradient2(
# low = "blue",
# high = "red",
# midpoint = 0,
# limits = c(-1, 1)
# ),
Rowv = FALSE,
Colv = FALSE,
fontsize_row = 14,
fontsize_col = 12,
colors <- c(google.blue, 'white', google.red),
column_text_angle = 315,
hide_colorbar = TRUE,
custom_hovertext = hover.text,
limits = c(-max.cor,max.cor),
cellnote = anno.data,
draw_cellnote = TRUE,
cellnote_textposition = 'middle center',
cellnote_size = 8,
show_dendrogram = c(FALSE, FALSE))
p
})
})
})
observe({
req(input$mir.id, input$geneset.id)
mir <- input$mir.id
mir.name <- mir.annotation[mir, 'Name']
geneset <- input$geneset.id
# signature.enrich <- reactive({
# enrich.table <- enrichment.table[[geneset]][[mir]]
# enrich.table
# })
#
dataForBarPlot <- enrichment.table[[geneset]][[mir]]
plotHeight.Enrich.Bubble <- reactive({
#dataForBarPlot <- signature.enrich()
if (nrow(dataForBarPlot)>=30) {
500
} else if (nrow(dataForBarPlot)>=20 & nrow(dataForBarPlot)<30) {
nrow(dataForBarPlot)/30*500
} else {
20/30*500
}
})