DEBrowser Modules¶
Debrowser is created with moduler structure which allows user to run each module separately. In this guide, you can find explanation about structure of modules and how to run each module.
Demo Data¶
To start with, you need to create following variables: demodata and metadatatable by using your data and save as demodata.Rda. The structure of these variables showed at below:
> head (demodata)
exper_rep1 exper_rep2 exper_rep3 control_rep1 control_rep2 control_rep3
AK212155 0.00 0.00 0 0.0 0 0
Sp2 52.00 47.00 36 99.0 53 66
AK051368 4.39 1.11 0 1.1 0 0
Ubiad1 121.00 125.00 65 134.0 95 111
Src 21.00 35.00 20 43.0 22 32
Racgap1 9.00 20.00 11 14.0 10 7
> head (metadatatable)
samples treatment batch
1 exper_rep1 cond1 1
2 exper_rep2 cond1 2
3 exper_rep3 cond1 1
4 control_rep1 cond2 2
5 control_rep2 cond2 1
6 control_rep3 cond2 2
One way to import tsv files is showed at below:
demodata <- read.table("~/Downloads/shKRAS.tsv", header=T, row.names=1, sep="\t")
Now we can run each modules separately through R studio by clicking Run App button.
Example Modules¶
You can reach demo data and latest versions of modules through our github page.
Barmain plot:
library(debrowser)
options(warn =-1)
header <- dashboardHeader( title = "DEBrowser Bar Plots" )
sidebar <- dashboardSidebar(
sidebarMenu(
id="DEAnlysis",
menuItem("BarMain", tabName = "BarMain"),
textInput("genename", "Gene/Region Name", value = "Foxa3"),
plotSizeMarginsUI("barmain", h=400)
)
)
body <- dashboardBody(
tabItems(
tabItem(
tabName="BarMain",
fluidRow(column(12,getBarMainPlotUI("barmain")))
)
)
)
ui <- dashboardPage(header, sidebar, body, skin = "blue")
server <- function(input, output, session) {
load(system.file("extdata", "demo", "demodata.Rda", package = "debrowser"))
observe({
if (!is.null(input$genename))
callModule(debrowserbarmainplot, "barmain", demodata, metadatatable$sample, metadatatable$treatment, input$genename)
})
}
shinyApp(ui, server)
The example module is created with UI and Server with shinyApp(ui, server) command. Similarly UI structure is defined with the ui <- dashboardPage(header, sidebar, body, skin = "blue") command. You can simply follow the structure of UI which is created by four variables: header, sidebar, body and skin. In the server function, demodata, metadatatable variables are loaded from demodata.Rda, and debrowserbarmainplot module is called with callModule function.
Main Plots:
library(plotly)
library(debrowser)
header <- dashboardHeader(title = "DEBrowser Main Plots")
sidebar <- dashboardSidebar( sidebarMenu(id="DEAnalysis",
menuItem("Main", tabName = "Main"),
mainPlotControlsUI("main"),
plotSizeMarginsUI("main")))
body <- dashboardBody(
tabItems(
tabItem(tabName="Main", getMainPlotUI("main"),
column(4,
verbatimTextOutput("main_hover"),
verbatimTextOutput("main_selected")
)
)
)
)
ui <- dashboardPage(header, sidebar, body, skin = "blue")
server <- function(input, output, session) {
#Example usage with demodata
load(system.file("extdata", "demo", "demodata.Rda",
package = "debrowser"))
dat <-c()
dat$columns <- c("exper_rep1", "exper_rep2", "exper_rep3",
"control_rep1", "control_rep2", "control_rep3")
dat$conds <- factor( c("Control", "Control", "Control",
"Treat", "Treat", "Treat") )
dat$data <- data.frame(demodata[, dat$columns])
# You can also use your dataset by reading your data from a file like below;
# The data in this commented out example is not supplied but these lines
# can give you an idea about how to read the data from a file;
#
# data <- read.table("~/Downloads/shKRAS.tsv", header=T, row.names=1, sep="\t")
# dat$columns <- c("CNT.2", "CNT.3", "CNT.4",
# "shKRAS_T1", "shKRAS_T2", "shKRAS_T3")
# dat$conds <- factor( c("Control", "Control", "Control",
# "shKRAS", "shKRAS", "shKRAS") )
# dat$data <- data.frame(data[, dat$columns])
#
xdata <- generateTestData(dat)
selected <- callModule(debrowsermainplot, "main", xdata)
output$main_hover <- renderPrint({
selected$shgClicked()
})
output$main_selected <- renderPrint({
selected$selGenes()
})
}
shinyApp(ui, server)
PCA plot:
library(debrowser)
header <- dashboardHeader(title = "DEBrowser PCA Plots")
sidebar <- dashboardSidebar(
sidebarMenu(id="DataAssessment",
menuItem("PCA", tabName = "PCA"),
menuItem("PCA Options",
pcaPlotControlsUI("pca")),
plotSizeMarginsUI("pca", w=600, h=400, t=50, b=50, l=60, r=0)
))
body <- dashboardBody(
tabItems(
tabItem(tabName="PCA", getPCAPlotUI("pca"),
column(4,
verbatimTextOutput("pca_hover"),
verbatimTextOutput("pca_selected")
)
)
)
)
ui <- shinydashboard::dashboardPage(header, sidebar, body, skin = "blue")
server <- function(input, output, session) {
load(system.file("extdata", "demo", "demodata.Rda", package = "debrowser"))
selected <- callModule(debrowserpcaplot, "pca", demodata)
}
shinyApp(ui, server)
All2All Plot:
library(debrowser)
options(warn =-1)
header <- dashboardHeader( title = "DEBrowser All2All Plots")
sidebar <- dashboardSidebar( sidebarMenu(id="DEAnlysis",
menuItem("All2All", tabName = "All2All"),
plotSizeMarginsUI("all2all", h=800, w=800),
all2allControlsUI("all2all")
)
)
body <- dashboardBody(
tabItems(
tabItem(tabName="All2All",
fluidRow(column(12,getAll2AllPlotUI("all2all")))
)
)
)
ui <- dashboardPage(header, sidebar, body, skin = "blue")
server <- function(input, output, session) {
load(system.file("extdata", "demo", "demodata.Rda",package = "debrowser"))
observe({
callModule(debrowserall2all, "all2all", demodata, input$cex)
})
}
shinyApp(ui, server)
Batch Effect Module:
library(debrowser)
options(warn =-1)
header <- dashboardHeader(title = "DEBrowser Batch Effect")
sidebar <- dashboardSidebar(sidebarMenu(id="DataPrep",
menuItem("BatchEffect", tabName = "BatchEffect")
)
)
body <- dashboardBody(
tabItems(
tabItem(tabName="BatchEffect", batchEffectUI("batcheffect"),
column(4,
verbatimTextOutput("batcheffecttable")
)
)
)
)
ui <- dashboardPage(header, sidebar, body, skin = "blue")
server <- function(input, output, session) {
load(system.file("extdata", "demo", "demodata.Rda", package = "debrowser"))
ldata <- reactiveValues(count=NULL, meta=NULL)
ldata$count <- demodata
ldata$meta <- metadatatable
data <- callModule(debrowserbatcheffect, "batcheffect", ldata)
observe({
output$batcheffecttable <- renderPrint({
head( data$BatchEffect()$count )
})
})
}
shinyApp(ui, server)
Main Box plot:
library(debrowser)
library(plotly)
options(warn =-1)
header <- dashboardHeader(title = "DEBrowser Box Plots")
sidebar <- dashboardSidebar( sidebarMenu(id="DEAnlysis",
menuItem("BoxMain", tabName = "BoxMain"),
textInput("genename", "Gene/Region Name", value = "Foxa3" ),
plotSizeMarginsUI("boxmain", h=400, t = 30)
)
)
body <- dashboardBody(
tabItems(
tabItem(tabName="BoxMain",
fluidRow(column(12,getBoxMainPlotUI("boxmain")))
)
)
)
ui <- dashboardPage(header, sidebar, body, skin = "blue")
server <- function(input, output, session) {
load(system.file("extdata", "demo", "demodata.Rda", package = "debrowser"))
observe({
if (!is.null(input$genename))
callModule(debrowserboxmainplot, "boxmain", demodata, metadatatable$sample, metadatatable$treatment, input$genename)
})
}
shinyApp(ui, server)
Density Plot:
library(debrowser)
options(warn =-1)
header <- dashboardHeader(title = "DEBrowser Density Plots" )
sidebar <- dashboardSidebar( sidebarMenu(id="DataAssessment",
menuItem("Density", tabName = "Density"),
textInput("maxCutoff", "Max Cutoff", value = "10" ),
plotSizeMarginsUI("density", h=400),
plotSizeMarginsUI("afterFiltering", h=400)
)
)
body <- dashboardBody(
tabItems(
tabItem(tabName="Density",
fluidRow(column(12,getDensityPlotUI("density"))),
fluidRow(column(12,getDensityPlotUI("afterFiltering")))
)
)
)
ui <- dashboardPage(header, sidebar, body, skin = "blue")
server <- function(input, output, session) {
load(system.file("extdata", "demo", "demodata.Rda", package = "debrowser"))
filtd <- reactive({
# Filter out the rows that has maximum 100 reads in a sample
subset(demodata, apply(demodata, 1, max, na.rm = TRUE) >= as.numeric(input$maxCutoff))
})
observe({
if(!is.null(filtd())){
callModule(debrowserdensityplot, "density", demodata)
callModule(debrowserdensityplot, "afterFiltering", filtd())
}
})
}
shinyApp(ui, server)
Heatmap Module:
library(debrowser)
library(DESeq2)
library(heatmaply)
library(RColorBrewer)
library(gplots)
options(warn=-1)
header <- dashboardHeader(title = "DEBrowser Heatmap")
sidebar <- dashboardSidebar( getJSLine(), sidebarMenu(id="DataAssessment",
menuItem("Heatmap", tabName = "Heatmap"),
plotSizeMarginsUI("heatmap"),
heatmapControlsUI("heatmap"))
)
body <- dashboardBody(
tabItems(
tabItem(tabName="Heatmap", getHeatmapUI("heatmap"),
column(4,
verbatimTextOutput("heatmap_hover"),
verbatimTextOutput("heatmap_selected")
)
)
)
)
ui <- dashboardPage(header, sidebar, body, skin = "blue")
server <- function(input, output, session) {
load(system.file("extdata", "demo", "demodata.Rda", package = "debrowser"))
insulinSignalingGenes <- reactive({
genes <- c("Prkar2a", "Tsc1", "Mapk8", "Sos1", "Pik3r1", "Srebf1",
"Insr", "Fasn", "Ppp1r3b", "Pik3r3", "Ptprf", "Pklr",
"Irs2", "Socs4", "Eif4ebp1", "Ppp1r3c", "Pygl", "Socs2",
"Cbl","Acaca", "Crkl")
normDat <- getNormalizedMatrix(demodata, method = "MRN")
normDat[genes, ]
})
selected <- reactiveVal()
observe({
withProgress(message = 'Creating plot', style = "notification", value = 0.1,
{ selected(callModule(debrowserheatmap, "heatmap", insulinSignalingGenes())) }
)
})
output$heatmap_hover <- renderPrint({
if (!is.null(selected()) && !is.null(selected()$shgClicked()) &&
selected()$shgClicked() != "")
return(paste0("Clicked: ",selected()$shgClicked()))
else
return(paste0("Hovered:", selected()$shg()))
})
output$heatmap_selected <- renderPrint({
if (!is.null(selected()))
selected()$selGenes()
})
}
shinyApp(ui, server)