R/DMR.GUI.R
In JoshuaTian/MyChAMP: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC
if(getRversion() >= "3.1.0") utils::globalVariables(c("myDMR","myLoad","myNorm","probe.features.epic","probe.features","Value","ID","Sample","aggregate","x","y","fitted","loess","gene","Number","Variance"))
DMR.GUI <- function(DMR=myDMR,
beta=myNorm,
pheno=myLoad$pd$Sample_Group,
runDMP=TRUE,
compare.group=NULL,
arraytype="450K")
{
if(arraytype == "EPIC") data(probe.features.epic) else data(probe.features)
probe.features <- probe.features[rownames(beta),]
if(runDMP)
{
message("Calculating DMP")
DMP <- champ.DMP(beta=beta,
pheno=pheno,
adjPVal=1,
adjust.method="BH",
compare.group=compare.group,
arraytype=arraytype)
}
message("<< Generating Annotation File >>")
DMR[[1]]$seqnames <- as.factor(substr(DMR[[1]]$seqnames,4,100))
index <- apply(DMR[[1]],1,function(x) which(probe.features$CHR==x[1] & probe.features$MAPINFO >= as.numeric(x[2]) & probe.features$MAPINFO <= as.numeric(x[3])))
Anno <- data.frame(DMRindex=unname(unlist(sapply(names(index),function(x) rep(x,length(index[[x]]))))),probe.features[do.call(c,index),1:8])
message("<< Generating Annotation File Success >>")
if(identical(names(DMR),"DMRcateDMR"))
{
sig <- data.frame(DMR.pvalue=rep(0,nrow(DMR$DMRcateDMR)),
DMR.probes=unlist(lapply(index,length)))
}else if(identical(names(DMR),"BumphunterDMR"))
{
sig <- data.frame(DMR.pvalue=DMR$BumphunterDMR$p.valueArea,
DMR.probes=unlist(lapply(index,length)))
}else if(identical(names(DMR),"ProbeLassoDMR"))
{
sig <- data.frame(DMR.pvalue=DMR$ProbeLassoDMR$dmrP,
DMR.probes=unlist(lapply(index,length)))
}
innerdmrplot <- function(select,Group,dmr.idx)
{
message("<< Generating dmrplot >>")
#select <- select[order(select$MAPINFO),]
G <- lapply(Group,function(x) t(x))
G <- lapply(G,function(h) data.frame(Sample=rep(colnames(h),each=nrow(h)),ID=rep(rownames(h),ncol(h)),pos=rep(as.numeric(as.factor(select$MAPINFO)),ncol(h)),Value=as.vector(h)))
for(i in 1:length(G)) G[[i]] <- data.frame(G[[i]],pheno=names(G)[i])
X <- do.call(rbind,G)
X <- cbind(X,showtext=paste("cpgID:",X$ID," Sample:",X$Sample,sep=""))
p <- plot_ly()
p <- add_trace(p,data=X, x=~pos, y=~Value,type="scatter", mode = "markers", text =~showtext,color=~pheno,marker=list(opacity = 0.6,size = 4))
message("<< Dots Plotted >>")
Fit <- lapply(G,function(h) data.frame(pos=unique(h$pos),ID=unique(h$ID),Mean=aggregate(h$Value,by=list(h$pos),mean)[,2]))
for(i in 1:length(Fit)) Fit[[i]] <- data.frame(Fit[[i]],pheno=paste(names(Fit)[i],"Mean"))
df <- data.frame(x = unlist(lapply(Fit, "[[", "pos")),
y = unlist(lapply(Fit, "[[", "Mean")),
ID = unlist(lapply(Fit, "[[", "ID")),
cut = unlist(lapply(Fit, "[[", "pheno")))
p <- add_trace(p,data=df, x =~x, y =~y, text =~ID, color =~cut,line = list(shape = "spline",width = 3,opacity = 0.3),marker=list(size = 1))
message("<< Mean line Plotted >>")
Fit2 <- lapply(G,function(h) data.frame(pos=unique(h$pos),ID=unique(h$ID),Fitted=unique(fitted(loess(h$Value ~ h$pos)))))
for(i in 1:length(Fit2)) Fit2[[i]] <- data.frame(Fit2[[i]],pheno=paste(names(Fit2)[i],"Loess"))
df2 <- data.frame(x = unlist(lapply(Fit2, "[[", "pos")),
y = unlist(lapply(Fit2, "[[", "Fitted")),
ID = unlist(lapply(Fit2, "[[", "ID")),
cut = unlist(lapply(Fit2, "[[", "pheno")))
p <- add_trace(p,data=df2, x =~x, y =~y, text =~ID, color =~cut,line =list(shape ="spline",width=3,opacity=0.3,dash = "dash"),marker=list(size = 1))
message("<< Loess line Plotted >>")
regioncol <- c("1stExon"="#00eeee","3'UTR"="#8b008b","5'UTR"="#00ee00","Body"="#ffd700","IGR"="#9e9e9e","TSS1500"="#ff3030","TSS200"="#00688b")
featureregion <- list()
for(i in names(regioncol))
{
index <- which(select$feature==i)
if(length(index)==0) next
featureregion[[i]]<- data.frame(x1 = index-0.5,
x2 = index+0.5,
y = -0.05,
IDfeature = i,
color = regioncol[i])
}
cgicol <- c("island"="#ff83fa","opensea"="#ffdab9","shelf"="#bbffff","shore"="#98fb98")
cgiregion <- list()
for(i in names(cgicol))
{
index <- which(select$cgi==i)
if(length(index)==0) next
cgiregion[[i]]<- data.frame(x1 = index-0.5,
x2 = index+0.5,
y = -0.1,
IDfeature = i,
color = cgicol[i])
}
#############################################
df <- rbind(do.call(rbind,featureregion),do.call(rbind,cgiregion))
colorrecord <- c()
for(i in 1:nrow(df))
{
legend = F
if(!df$color[i] %in% colorrecord)
{
colorrecord <- c(colorrecord,as.character(df$color[i]))
legend = T
}
p <- add_trace(p,
x = c(df$x1[i], df$x2[i]), # x0, x
y = c(df$y[i],df$y[i]), # y0, y1
name = df$IDfeature[i],
mode = "lines",
line = list(color = df$color[i], width = 10 , opacity=0.5),
showlegend = legend,
hoverinfo = "text",
# Create custom hover text
text = df$IDfeature[i],
evaluate = T)
}
#############################################
message("<< Cgi Bar Plotted >>")
p <- layout(p, title = paste("DMR",dmr.idx,sep="_"))
}
innergeneenrichplot <- function(select)
{
print(dim(select))
message("<< Calculating Gene Enrich Plot >>")
select <- select[which(select$gene != ""),]
if(c("adj.P.Val") %in% colnames(select))
{
select$t <- select$t>0
select$adj.P.Val[which(select$adj.P.Val <= 0.05)] <- 1
select$adj.P.Val[which(select$adj.P.Val != 1)] <- 0
h <- table(as.character(select$gene),paste(select$t,select$adj.P.Val))
h <- h[order(rowSums(h),decreasing=T),]
h <- cbind(h[,c(4,2)],rowSums(h[,c(1,3)]))
colnames(h) <- c("Hyper","Hypo","Not Significance")
h <- data.frame(Variance=rep(colnames(h),each=nrow(h)),gene=rep(rownames(h),ncol(h)),Number=as.vector(h))
p <- plot_ly(data=h,x =~gene, y =~Number, type = "bar", color =~Variance)
}else
{
h <- data.frame(table(as.character(select$gene)))
colnames(h) <- c("gene","Number")
h <- h[order(h$Number,decreasing=T),]
p <- plot_ly(data=~h,x=~gene,y=~Number,type="bar")
}
m = list(l = 70,r = 30,b = 150,t = 50,pad = 10)
p <- layout(p, title = paste("All ",length(unique(h$gene))," Gene Enriched by DMR-related CpGs",sep=""),margin=m,barmode = "stack")
}
innerheatmap <- function(data)
{
m = list(l = 170,r = 70,b = 60,t = 50,pad = 10)
p <- plot_ly(z = data,x = colnames(data), y = rownames(data),type = "heatmap")
p <- layout(p, title = paste("Heatmap for ",nrow(data)," CpGs in all DMRs",sep=""),margin=m)
}
innertestplot <- function()
{
plot_ly(x=1:10,y=1:10)
}
app <- shinyApp(
ui = fluidPage(
tags$head(tags$style("#dmrplot{height:40vh !important;}")),
tags$head(tags$style("#dmrcateplot{height:80vh !important;}")),
tags$head(tags$style("#geneenrichplot{height:40vh !important;}")),
tags$head(tags$style("#heatmap{height:40vh !important;}")),
theme = shinytheme("readable"),
titlePanel(paste(names(DMR)[1],"Overview")),
sidebarLayout(
sidebarPanel(
br(),
width=3,
sliderInput("pvaluebin","P value Cutoff:",min = 0,max = 1,step = 0.025,value = 0.05),
sliderInput("minprobebin","Min Number Probes:",min = 1,max = 100,step = 1,value = 7),
actionButton("go", "Submit"),
br(),
br(),
br(),
sliderInput("dmrbin","DMR Index:",min = 1,max = nrow(sig),step = 1,value = 1),
actionButton("dmrsearch", "Submit")
),#sidebarPanel
mainPanel(
tabsetPanel(
tabPanel("DMRtable",
align = "left",
div(dataTableOutput("table"), style = "font-size:75%")
),
tabPanel("CpGtable",
align = "left",
div(dataTableOutput("cpgtable"), style = "font-size:75%")
),
tabPanel("DMRPlot",
align = "center",
fluidRow(
align = "center",
br(),
column(width = 12,
align = "left",
div(dataTableOutput("probetable"), style = "font-size:75%")
),#column
column(width = 12,
align = "center",
plotlyOutput("dmrplot")
)#column
)#fluidRow
),
tabPanel("Summary",
align = "center",
fluidRow(
align = "center",
br(),
column(width = 12,
align = "center",
plotlyOutput("geneenrichplot")
),#column
column(width = 12,
align = "center",
plotlyOutput("heatmap")
)#column
)#fluidRow
)
)#tabsetPanel
)#mainPanel
)#sidebarLayout
),#ui
server = function(input, output){
DMR_repalce <- eventReactive(input$dmrsearch,
{
gc()
pvalueCutoff <- as.numeric(input$pvaluebin)
minprobeCutoff <- as.numeric(input$minprobebin)
dmr.idx <- as.numeric(input$dmrbin)
### Generate Data for dmrplot
mydmrselect <- Anno[Anno$DMRindex==paste("DMR",dmr.idx,sep="_"),]
mydmrselect <- mydmrselect[order(mydmrselect$MAPINFO),]
mygroup <- split(as.data.frame(t(beta[rownames(mydmrselect),])),pheno)
list(mydmrselect=mydmrselect,
mygroup=mygroup,
dmr.idx=dmr.idx)
})
Cutoff_repalce <- eventReactive(input$go,
{
gc()
pvalueCutoff <- as.numeric(input$pvaluebin)
minprobeCutoff <- as.numeric(input$minprobebin)
mydmr <- DMR[[1]][which(sig$DMR.pvalue <= pvalueCutoff & sig$DMR.probes >= minprobeCutoff),]
## Generate data for geneenrichplot
mygeneselect <- Anno[which(Anno$DMRindex %in% rownames(mydmr)),]
if(runDMP) mygeneselect <- data.frame(mygeneselect,DMP[rownames(mygeneselect),c("t","adj.P.Val")])
## Generate data for heatmap
mydata <- beta[rownames(mygeneselect),]
rownames(mydata) <- paste(mygeneselect$DMRindex,rownames(mydata),sep="-")
list(pvalueCutoff=pvalueCutoff,
minprobeCutoff=minprobeCutoff,
mydmr=mydmr,
mygeneselect=mygeneselect,
mydata=mydata)
})
output$probetable <- renderDataTable({
datatable <- DMR_repalce()$mydmrselect
datatable <- data.frame(ID=rownames(datatable),datatable)
},options = list(pageLength=8))
output$table <- renderDataTable({
datatable <- Cutoff_repalce()$mydmr
datatable <- data.frame(ID=rownames(datatable),datatable)
},options = list(pageLength=20))
output$cpgtable <- renderDataTable({
datatable <- Cutoff_repalce()$mygeneselect
datatable <- data.frame(ID=rownames(datatable),datatable)
},options = list(pageLength=20))
output$dmrplot <- renderPlotly({
innerdmrplot(DMR_repalce()$mydmrselect,DMR_repalce()$mygroup,DMR_repalce()$dmr.idx)
})
output$geneenrichplot <- renderPlotly({
innergeneenrichplot(Cutoff_repalce()$mygeneselect)
})
output$heatmap <- renderPlotly({
innerheatmap(Cutoff_repalce()$mydata)
})
}
)
runApp(app)
}
JoshuaTian/MyChAMP documentation built on May 7, 2019, 12:04 p.m.
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if(getRversion() >= "3.1.0") utils::globalVariables(c("myDMR","myLoad","myNorm","probe.features.epic","probe.features","Value","ID","Sample","aggregate","x","y","fitted","loess","gene","Number","Variance"))
DMR.GUI <- function(DMR=myDMR,
beta=myNorm,
pheno=myLoad$pd$Sample_Group,
runDMP=TRUE,
compare.group=NULL,
arraytype="450K")
{
if(arraytype == "EPIC") data(probe.features.epic) else data(probe.features)
probe.features <- probe.features[rownames(beta),]
if(runDMP)
{
message("Calculating DMP")
DMP <- champ.DMP(beta=beta,
pheno=pheno,
adjPVal=1,
adjust.method="BH",
compare.group=compare.group,
arraytype=arraytype)
}
message("<< Generating Annotation File >>")
DMR[[1]]$seqnames <- as.factor(substr(DMR[[1]]$seqnames,4,100))
index <- apply(DMR[[1]],1,function(x) which(probe.features$CHR==x[1] & probe.features$MAPINFO >= as.numeric(x[2]) & probe.features$MAPINFO <= as.numeric(x[3])))
Anno <- data.frame(DMRindex=unname(unlist(sapply(names(index),function(x) rep(x,length(index[[x]]))))),probe.features[do.call(c,index),1:8])
message("<< Generating Annotation File Success >>")
if(identical(names(DMR),"DMRcateDMR"))
{
sig <- data.frame(DMR.pvalue=rep(0,nrow(DMR$DMRcateDMR)),
DMR.probes=unlist(lapply(index,length)))
}else if(identical(names(DMR),"BumphunterDMR"))
{
sig <- data.frame(DMR.pvalue=DMR$BumphunterDMR$p.valueArea,
DMR.probes=unlist(lapply(index,length)))
}else if(identical(names(DMR),"ProbeLassoDMR"))
{
sig <- data.frame(DMR.pvalue=DMR$ProbeLassoDMR$dmrP,
DMR.probes=unlist(lapply(index,length)))
}
innerdmrplot <- function(select,Group,dmr.idx)
{
message("<< Generating dmrplot >>")
#select <- select[order(select$MAPINFO),]
G <- lapply(Group,function(x) t(x))
G <- lapply(G,function(h) data.frame(Sample=rep(colnames(h),each=nrow(h)),ID=rep(rownames(h),ncol(h)),pos=rep(as.numeric(as.factor(select$MAPINFO)),ncol(h)),Value=as.vector(h)))
for(i in 1:length(G)) G[[i]] <- data.frame(G[[i]],pheno=names(G)[i])
X <- do.call(rbind,G)
X <- cbind(X,showtext=paste("cpgID:",X$ID," Sample:",X$Sample,sep=""))
p <- plot_ly()
p <- add_trace(p,data=X, x=~pos, y=~Value,type="scatter", mode = "markers", text =~showtext,color=~pheno,marker=list(opacity = 0.6,size = 4))
message("<< Dots Plotted >>")
Fit <- lapply(G,function(h) data.frame(pos=unique(h$pos),ID=unique(h$ID),Mean=aggregate(h$Value,by=list(h$pos),mean)[,2]))
for(i in 1:length(Fit)) Fit[[i]] <- data.frame(Fit[[i]],pheno=paste(names(Fit)[i],"Mean"))
df <- data.frame(x = unlist(lapply(Fit, "[[", "pos")),
y = unlist(lapply(Fit, "[[", "Mean")),
ID = unlist(lapply(Fit, "[[", "ID")),
cut = unlist(lapply(Fit, "[[", "pheno")))
p <- add_trace(p,data=df, x =~x, y =~y, text =~ID, color =~cut,line = list(shape = "spline",width = 3,opacity = 0.3),marker=list(size = 1))
message("<< Mean line Plotted >>")
Fit2 <- lapply(G,function(h) data.frame(pos=unique(h$pos),ID=unique(h$ID),Fitted=unique(fitted(loess(h$Value ~ h$pos)))))
for(i in 1:length(Fit2)) Fit2[[i]] <- data.frame(Fit2[[i]],pheno=paste(names(Fit2)[i],"Loess"))
df2 <- data.frame(x = unlist(lapply(Fit2, "[[", "pos")),
y = unlist(lapply(Fit2, "[[", "Fitted")),
ID = unlist(lapply(Fit2, "[[", "ID")),
cut = unlist(lapply(Fit2, "[[", "pheno")))
p <- add_trace(p,data=df2, x =~x, y =~y, text =~ID, color =~cut,line =list(shape ="spline",width=3,opacity=0.3,dash = "dash"),marker=list(size = 1))
message("<< Loess line Plotted >>")
regioncol <- c("1stExon"="#00eeee","3'UTR"="#8b008b","5'UTR"="#00ee00","Body"="#ffd700","IGR"="#9e9e9e","TSS1500"="#ff3030","TSS200"="#00688b")
featureregion <- list()
for(i in names(regioncol))
{
index <- which(select$feature==i)
if(length(index)==0) next
featureregion[[i]]<- data.frame(x1 = index-0.5,
x2 = index+0.5,
y = -0.05,
IDfeature = i,
color = regioncol[i])
}
cgicol <- c("island"="#ff83fa","opensea"="#ffdab9","shelf"="#bbffff","shore"="#98fb98")
cgiregion <- list()
for(i in names(cgicol))
{
index <- which(select$cgi==i)
if(length(index)==0) next
cgiregion[[i]]<- data.frame(x1 = index-0.5,
x2 = index+0.5,
y = -0.1,
IDfeature = i,
color = cgicol[i])
}
#############################################
df <- rbind(do.call(rbind,featureregion),do.call(rbind,cgiregion))
colorrecord <- c()
for(i in 1:nrow(df))
{
legend = F
if(!df$color[i] %in% colorrecord)
{
colorrecord <- c(colorrecord,as.character(df$color[i]))
legend = T
}
p <- add_trace(p,
x = c(df$x1[i], df$x2[i]), # x0, x
y = c(df$y[i],df$y[i]), # y0, y1
name = df$IDfeature[i],
mode = "lines",
line = list(color = df$color[i], width = 10 , opacity=0.5),
showlegend = legend,
hoverinfo = "text",
# Create custom hover text
text = df$IDfeature[i],
evaluate = T)
}
#############################################
message("<< Cgi Bar Plotted >>")
p <- layout(p, title = paste("DMR",dmr.idx,sep="_"))
}
innergeneenrichplot <- function(select)
{
print(dim(select))
message("<< Calculating Gene Enrich Plot >>")
select <- select[which(select$gene != ""),]
if(c("adj.P.Val") %in% colnames(select))
{
select$t <- select$t>0
select$adj.P.Val[which(select$adj.P.Val <= 0.05)] <- 1
select$adj.P.Val[which(select$adj.P.Val != 1)] <- 0
h <- table(as.character(select$gene),paste(select$t,select$adj.P.Val))
h <- h[order(rowSums(h),decreasing=T),]
h <- cbind(h[,c(4,2)],rowSums(h[,c(1,3)]))
colnames(h) <- c("Hyper","Hypo","Not Significance")
h <- data.frame(Variance=rep(colnames(h),each=nrow(h)),gene=rep(rownames(h),ncol(h)),Number=as.vector(h))
p <- plot_ly(data=h,x =~gene, y =~Number, type = "bar", color =~Variance)
}else
{
h <- data.frame(table(as.character(select$gene)))
colnames(h) <- c("gene","Number")
h <- h[order(h$Number,decreasing=T),]
p <- plot_ly(data=~h,x=~gene,y=~Number,type="bar")
}
m = list(l = 70,r = 30,b = 150,t = 50,pad = 10)
p <- layout(p, title = paste("All ",length(unique(h$gene))," Gene Enriched by DMR-related CpGs",sep=""),margin=m,barmode = "stack")
}
innerheatmap <- function(data)
{
m = list(l = 170,r = 70,b = 60,t = 50,pad = 10)
p <- plot_ly(z = data,x = colnames(data), y = rownames(data),type = "heatmap")
p <- layout(p, title = paste("Heatmap for ",nrow(data)," CpGs in all DMRs",sep=""),margin=m)
}
innertestplot <- function()
{
plot_ly(x=1:10,y=1:10)
}
app <- shinyApp(
ui = fluidPage(
tags$head(tags$style("#dmrplot{height:40vh !important;}")),
tags$head(tags$style("#dmrcateplot{height:80vh !important;}")),
tags$head(tags$style("#geneenrichplot{height:40vh !important;}")),
tags$head(tags$style("#heatmap{height:40vh !important;}")),
theme = shinytheme("readable"),
titlePanel(paste(names(DMR)[1],"Overview")),
sidebarLayout(
sidebarPanel(
br(),
width=3,
sliderInput("pvaluebin","P value Cutoff:",min = 0,max = 1,step = 0.025,value = 0.05),
sliderInput("minprobebin","Min Number Probes:",min = 1,max = 100,step = 1,value = 7),
actionButton("go", "Submit"),
br(),
br(),
br(),
sliderInput("dmrbin","DMR Index:",min = 1,max = nrow(sig),step = 1,value = 1),
actionButton("dmrsearch", "Submit")
),#sidebarPanel
mainPanel(
tabsetPanel(
tabPanel("DMRtable",
align = "left",
div(dataTableOutput("table"), style = "font-size:75%")
),
tabPanel("CpGtable",
align = "left",
div(dataTableOutput("cpgtable"), style = "font-size:75%")
),
tabPanel("DMRPlot",
align = "center",
fluidRow(
align = "center",
br(),
column(width = 12,
align = "left",
div(dataTableOutput("probetable"), style = "font-size:75%")
),#column
column(width = 12,
align = "center",
plotlyOutput("dmrplot")
)#column
)#fluidRow
),
tabPanel("Summary",
align = "center",
fluidRow(
align = "center",
br(),
column(width = 12,
align = "center",
plotlyOutput("geneenrichplot")
),#column
column(width = 12,
align = "center",
plotlyOutput("heatmap")
)#column
)#fluidRow
)
)#tabsetPanel
)#mainPanel
)#sidebarLayout
),#ui
server = function(input, output){
DMR_repalce <- eventReactive(input$dmrsearch,
{
gc()
pvalueCutoff <- as.numeric(input$pvaluebin)
minprobeCutoff <- as.numeric(input$minprobebin)
dmr.idx <- as.numeric(input$dmrbin)
### Generate Data for dmrplot
mydmrselect <- Anno[Anno$DMRindex==paste("DMR",dmr.idx,sep="_"),]
mydmrselect <- mydmrselect[order(mydmrselect$MAPINFO),]
mygroup <- split(as.data.frame(t(beta[rownames(mydmrselect),])),pheno)
list(mydmrselect=mydmrselect,
mygroup=mygroup,
dmr.idx=dmr.idx)
})
Cutoff_repalce <- eventReactive(input$go,
{
gc()
pvalueCutoff <- as.numeric(input$pvaluebin)
minprobeCutoff <- as.numeric(input$minprobebin)
mydmr <- DMR[[1]][which(sig$DMR.pvalue <= pvalueCutoff & sig$DMR.probes >= minprobeCutoff),]
## Generate data for geneenrichplot
mygeneselect <- Anno[which(Anno$DMRindex %in% rownames(mydmr)),]
if(runDMP) mygeneselect <- data.frame(mygeneselect,DMP[rownames(mygeneselect),c("t","adj.P.Val")])
## Generate data for heatmap
mydata <- beta[rownames(mygeneselect),]
rownames(mydata) <- paste(mygeneselect$DMRindex,rownames(mydata),sep="-")
list(pvalueCutoff=pvalueCutoff,
minprobeCutoff=minprobeCutoff,
mydmr=mydmr,
mygeneselect=mygeneselect,
mydata=mydata)
})
output$probetable <- renderDataTable({
datatable <- DMR_repalce()$mydmrselect
datatable <- data.frame(ID=rownames(datatable),datatable)
},options = list(pageLength=8))
output$table <- renderDataTable({
datatable <- Cutoff_repalce()$mydmr
datatable <- data.frame(ID=rownames(datatable),datatable)
},options = list(pageLength=20))
output$cpgtable <- renderDataTable({
datatable <- Cutoff_repalce()$mygeneselect
datatable <- data.frame(ID=rownames(datatable),datatable)
},options = list(pageLength=20))
output$dmrplot <- renderPlotly({
innerdmrplot(DMR_repalce()$mydmrselect,DMR_repalce()$mygroup,DMR_repalce()$dmr.idx)
})
output$geneenrichplot <- renderPlotly({
innergeneenrichplot(Cutoff_repalce()$mygeneselect)
})
output$heatmap <- renderPlotly({
innerheatmap(Cutoff_repalce()$mydata)
})
}
)
runApp(app)
}
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