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Healthy_reg.R
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Healthy_reg.R
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library(shiny)
library(shinydashboard)
library(ggplot2)
library(reshape2)
source("custom_scripts/Fun_Rank_disease.R")
source("custom_scripts/global.R")
source("custom_scripts/enrichr.R")
Healthy_UI <- function(id,label="Rank"){
fluidPage(
## First Row: Filtering sex-biased genes from differential expression topTable.
fluidRow(
h3("Step1: Filtering sex-biased genes of each dataset before rank aggregation by p-value & logFC cutoff"),
h4("Please wait for a few minutes for calculation"),
##### INPUT BOX #######
box(title = "Sex differential genes of each dataset",
selectInput(NS(id,"region"),"brain region",
choices = list("AMY" = 1, "CBC" = 2, "CC" = 3,
"FC" = 4, "HIP" = 5, "MED" = 6,
"OC" = 7, "PL" = 8, "STR" = 9,
"TC" = 10, "THA" = 11
), selected = 1
),
sliderInput(NS(id,"DE_p_value"), "p-value cutoff:", min=0, max = 1, value = 0.05),
numericInput(NS(id,"FC"),"FC cutoff", value = 1.2)
),
######## OUTPUT BOX ######
box(title='The number of sex-biased genes',
plotOutput(NS(id,"plt_gene_num")),
downloadButton(NS(id,'downloadNum'), 'Download plot' )
)
),
## Second Row: Filtering genes from RRA
fluidRow(
h3("Step2: Combind all dataset ranks by RRA and filtered sex-biased genes by RRA p-value"),
box(title = 'Robust Rank aggregation cutoff',
sliderInput(NS(id,"RRA_p_value"), "RRA p-value cutoff:", min=0, max = 1, value = 0.05),
),
#box(title = 'Rank aggregation table of male-biased genes',
# dataTableOutput(NS(id,"RRAM")))
tabBox(title = "RRA output",
id= "tabRRA",
tabPanel("Output","The number of RRA genes",
plotOutput(NS(id,"plt_RRAnum")),
downloadButton(NS(id,'downloadRRANum'), 'Download plot' )
#dataTableOutput(NS(id, "RRAF"))
),
tabPanel("Female RRA table","Rank aggregation table of female-biased genes",
dataTableOutput(NS(id, "RRAFtable"))
),
tabPanel("Male RRA table","Rank aggregation table of male-biased genes",
dataTableOutput(NS(id, "RRAMtable"))
#dataTableOutput(NS(id, "RRA"))
)
)
),
## Third Row: GO Enrichment of Filtered sex-biased genes of RRA
fluidRow(
h3("Step3: Enrichment analysis of sex-biased genes of RRA"),
tabBox(title = "GO Enrichment",
id = "GO",
tabPanel("GO Biological Process",
h4("Female"),
plotOutput(NS(id,"GO_BP_F")),
downloadButton(NS(id,'downloadGO_BP_F'), 'Download plot' ),
h4("Male"),
plotOutput(NS(id,"GO_BP_M")),
downloadButton(NS(id,'downloadGO_BP_M'), 'Download plot' ),
),
tabPanel("GO Molecular Function",
h4("Female"),
plotOutput(NS(id,"GO_MO_F")),
downloadButton(NS(id,'downloadGO_MO_F'), 'Download plot' ),
h4("Male"),
plotOutput(NS(id,"GO_MO_M")),
downloadButton(NS(id,'downloadGO_MO_M'), 'Download plot' ),
),
tabPanel("GO Cellular Component",
h4("Female"),
plotOutput(NS(id,"GO_CC_F")),
downloadButton(NS(id,'downloadGO_CC_F'), 'Download plot' ),
h4("Male"),
plotOutput(NS(id,"GO_CC_M")),
downloadButton(NS(id,'downloadGO_CC_M'), 'Download plot' ),
)
),
box(title = "DisGeNET Enrichement",
h4("Female"),
plotOutput(NS(id,"Dis_F")),
downloadButton(NS(id,'downloadDis_F'), 'Download plot' ),
h4("Male"),
plotOutput(NS(id,"Dis_M")),
downloadButton(NS(id,'downloadDis_M'), 'Download plot' ),
),
box(title = "KEGG enrichment",
h4("Female"),
plotOutput(NS(id,"KEGG_F")),
downloadButton(NS(id,'downloadKEGG_F'), 'Download plot' ),
h4("Male"),
plotOutput(NS(id,"KEGG_M")),
downloadButton(NS(id,'downloadKEGG_M'), 'Download plot' ),
)
)
)
}
Healthy_Server <- function(id) {
moduleServer(id, function(input, output, session) {
########### MAIN RUN FOR EACH REGION #############
# 1. filtered genes for each dataset
sig_reg_F <- reactive( Filter_gene_reg_dis(input$region,input$DE_p_value,input$FC,'F','Healthy') )
sig_reg_M <- reactive( Filter_gene_reg_dis(input$region,input$DE_p_value,input$FC,'M','Healthy') )
# 2. Plot number of filtered genes for each dataset
output$plt_gene_num <- renderPlot({
num_fil_df <- numfil_gene(sig_reg_F() ,sig_reg_M() )
ggplot(num_fil_df, aes(x=dataset, y=value, fill=gender)) +
geom_bar(stat="identity",position='dodge') +
scale_fill_manual(values=c("deeppink", "dodgerblue"))+
theme(axis.text.x = element_text(angle = 45))
})
# save plot
num_df <- reactive( numfil_gene(sig_reg_F(), sig_reg_M() ) )
num_plot <- reactive( ggplot(num_df(), aes(x=dataset, y=value, fill=gender)) +
geom_bar(stat="identity",position='dodge') +
scale_fill_manual(values=c("deeppink", "dodgerblue"))+
theme(axis.text.x = element_text(angle = 45))
)
output$downloadNum <- downloadHandler(
filename = "Sex_biased_genes.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(num_plot())
dev.off()
}
)
# 3. Rank aggregation of filtered genes with RRA pvalue cutoff
RRA_F <- reactive({
Fsig <- lapply(sig_reg_F() , rownames)
RRA_F <- RA.analysis(Fsig)
sig_RRA_F <- RRA_F[ RRA_F['P.value'] < input$RRA_p_value,]
})
RRA_M <- reactive({
Msig <- lapply(sig_reg_M() , rownames)
RRA_M <- RA.analysis(Msig)
sig_RRA_M <- RRA_M[ RRA_M['P.value'] < input$RRA_p_value,]
})
output$RRAFtable <- renderDataTable(RRA_F(),server = FALSE,extensions = 'Buttons',
options = list(scrollX=TRUE, lengthMenu = c(5,10,15),
paging = TRUE, searching = TRUE,
fixedColumns = TRUE, autoWidth = TRUE,
ordering = TRUE, dom = 'tBp',
#ordering = TRUE, dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel','pdf'))
)
output$RRAMtable <- renderDataTable(RRA_M(),server = FALSE,extensions = 'Buttons',
options = list(scrollX=TRUE, lengthMenu = c(5,10,15),
paging = TRUE, searching = TRUE,
fixedColumns = TRUE, autoWidth = TRUE,
ordering = TRUE, dom = 'tBp',
#ordering = TRUE, dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel','pdf')))
# 4. Plot number of filtered genes for each dataset and RRA
output$plt_RRAnum <- renderPlot({
num_RRA <- data.frame(Female=c(nrow(RRA_F() )),Male=c(nrow(RRA_M() )) )
barplot(t(as.matrix(num_RRA)),beside=TRUE,col = c('deeppink','dodgerblue'))
})
plt_RRA_num <- reactive({
num_RRA <- data.frame(Number_of_genes=c(nrow(RRA_F() ), nrow(RRA_M() )),
Sex = c('Female', 'Male')
)
ggplot(num_RRA, aes(x= Sex, y = Number_of_genes, fill = Sex))+
geom_bar(stat = 'identity')+
scale_fill_manual(values=c("deeppink", "dodgerblue"))
})
# 5. User download RRA plot
output$downloadRRANum <- downloadHandler(
filename = "Number_of_sex_biased_RRA_filtered.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(plt_RRA_num())
dev.off()
}
)
# 6. Enrichment of GO, DisGeNET
############
## GO BP####
############
output$GO_BP_F <- renderPlot(plt_enriched_reg(RRA_F(), "GO_Biological_Process_2021"))
output$GO_BP_M <- renderPlot(plt_enriched_reg(RRA_M(), "GO_Biological_Process_2021"))
pltGO_BP_F <- reactive( plt_enriched_reg(RRA_F(), "GO_Biological_Process_2021" ))
pltGO_BP_M <- reactive( plt_enriched_reg(RRA_M(), "GO_Biological_Process_2021" ))
# User download GO plot
output$downloadGO_BP_F <- downloadHandler(
filename = "GO_BP_F.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(pltGO_BP_F())
dev.off()}
)
output$downloadGO_BP_M <- downloadHandler(
filename = "GO_BP_M.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(pltGO_BP_M())
dev.off()}
)
###########
## GO MO ##
###########
output$GO_MO_F <- renderPlot(plt_enriched_reg(RRA_F(), "GO_Molecular_Function_2021"))
output$GO_MO_M <- renderPlot(plt_enriched_reg(RRA_M(), "GO_Molecular_Function_2021"))
pltGO_MO_F <- reactive( plt_enriched_reg(RRA_F(), "GO_Molecular_Function_2021"))
pltGO_MO_M <- reactive( plt_enriched_reg(RRA_M(), "GO_Molecular_Function_2021"))
# User download GO plot
output$downloadGO_MO_F <- downloadHandler(
filename = "GO_MO_F.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(pltGO_MO_F())
dev.off()}
)
output$downloadGO_MO_M <- downloadHandler(
filename = "GO_MO_M.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(pltGO_MO_M())
dev.off()}
)
output$GO_CC_F <- renderPlot(plt_enriched_reg(RRA_F(), "GO_Cellular_Component_2021"))
output$GO_CC_M <- renderPlot(plt_enriched_reg(RRA_M(), "GO_Cellular_Component_2021"))
pltGO_CC_F <- reactive( plt_enriched_reg(RRA_F(), "GO_Cellular_Component_2021"))
pltGO_CC_M <- reactive( plt_enriched_reg(RRA_M(), "GO_Cellular_Component_2021"))
output$downloadGO_CC_F <- downloadHandler(
filename = "GO_CC_F.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(pltGO_CC_F())
dev.off()}
)
output$downloadGO_CC_M <- downloadHandler(
filename = "GO_CC_M.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(pltGO_CC_M())
dev.off()})
###############
## DisGeNET ###
##############
output$Dis_F <- renderPlot({
plt_enriched_reg(RRA_F(), "DisGeNET")
})
output$Dis_M <- renderPlot({
plt_enriched_reg(RRA_M(), "DisGeNET")
})
pltDis_F <- reactive( plt_enriched_reg(RRA_F(), "DisGeNET"))
pltDis_M <- reactive( plt_enriched_reg(RRA_M(), "DisGeNET"))
output$downloadDis_F <- downloadHandler(
filename = "DisGeNET_F.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(pltDis_F())
dev.off()}
)
output$downloadDis_M <- downloadHandler(
filename = "DisGeNET_M.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(pltDis_M())
dev.off()})
########
# KEGG #
########
output$KEGG_F <- renderPlot({
plt_enriched_reg(RRA_F(), "KEGG_2021_Human")
})
output$KEGG_M <- renderPlot({
plt_enriched_reg(RRA_M(), "KEGG_2021_Human")
})
pltKEGG_F <- reactive( plt_enriched_reg(RRA_F(), "KEGG_2021_Human"))
pltKEGG_M <- reactive( plt_enriched_reg(RRA_M(), "KEGG_2021_Human"))
output$downloadKEGG_F <- downloadHandler(
filename = "KEGG_F.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(pltKEGG_F())
dev.off()}
)
output$downloadKEGG_M <- downloadHandler(
filename = "KEGG_M.tiff",
content = function(file) {
tiff(file, res = 300, units = 'in', height =5, width = 5)
plot(pltKEGG_M())
dev.off()}
)
})
}