这篇文章主要介绍“R语言shiny如何实现简单的GO富集分析”，在日常操作中，相信很多人在R语言shiny如何实现简单的GO富集分析问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”R语言shiny如何实现简单的GO富集分析”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
模仿的是 https://github.com/sk-sahu/sig-bio-shiny
基本功能是用户输入
然后分别把
以表格输出，
代码中 cc和mf结果表格输出的逻辑没有写，和bp是完全一样的
library(shiny)
ui<-navbarPage("Pomegranate",
tabPanel("Gene Ontology",
sidebarLayout(sidebarPanel(width=2,
textAreaInput("text_area_list",
label = "Please input protein id, one per line",
height = "200px",
width="180px",
value="Pg00001\nPg00002"),
selectInput("id_type",label = "Input gene-id Type",
selected = "ensembl",
choices = c('ensembl','refseq','entrezid')),
helpText("Please"),
numericInput('pval_cutoff',label="pvalue-Cutoff",
value = 1,min=0.001,max=1,step=0.001),
numericInput("qval_cutoff",label="qvalue-Cutoff",
value=1,min=0.001,max=1,step=0.001),
actionButton('submit',label = 'Submit',
icon=icon('angle-double-right')),
tags$hr()),
mainPanel(helpText("ABC"),
downloadButton('download_plot',label = "Download results plot"),
downloadButton('download_table',label="Download result table"),
textOutput("gene_number_info"),
tags$br(),
tags$br(),
tabsetPanel(
tabPanel("Biological Process",DT::dataTableOutput(outputId="table_go_bp")),
tabPanel("Cellular Component",DT::dataTableOutput(outputId = "table_go_cc")),
tabPanel("Molecular Functions",DT::dataTableOutput(outputId = 'table_go_mf')),
tabPanel("dotplot",plotOutput('dot_plot_go'))
)))))
server<-function(input,output){
observeEvent(input$submit,{
withProgress(message = 'Steps:',value=0,{
incProgress(1/7,detail = "A")
text_area_input<-input$text_area_list
print(text_area_input)
df<-as.data.frame(matrix(unlist(stringr::str_split(text_area_input,"\n")),ncol=1))
colnames(df)<-"protein_id"
print(dim(df))
input_gene_number<-dim(df)[1]
output$gene_number_info<-renderText({
paste("Done!","Total Number of Input genes:",input_gene_number,sep="\n")
})
incProgress(2/7,detail = "B")
library(clusterProfiler)
enrichGO_res<-enrichGO(gene=df$protein_id,
OrgDb = 'org.Hs.eg.db',
ont="all",
pvalueCutoff = input$pval_cutoff,
qvalueCutoff = input$qval_cutoff)
go_enricher_res<-enrichGO_res@result
go_bp<-go_enricher_res[go_enricher_res$ONTOLOGY == "BP",]
output$table_go_bp<-DT::renderDataTable({
go_bp
})
incProgress(3/7,detail="plot")
output$dot_plot_go<-renderPlot({
p1<-dotplot(enrichGO_res)
print(p1)
})
incProgress(4/7,detail = "OK")
go_plot_download<-reactive({
dotplot(enrichGO_res)
}
)
output$download_plot<-downloadHandler(
filename = function(){
paste("go_dot_plot.png",sep='')
},
content = function(file){
ggplot2::ggsave(file,plot=go_plot_download(),device = 'png',width=12,height = 10)
}
)
output$download_table<-downloadHandler(
filename = function(){
paste0("ABC.zip")
},
content = function(file){
fs<-c('go_results.tsv')
write.table(go_enricher_res,file="go_results.tsv",sep="\t",row.names = F)
zip(zipfile = file,files=fs)
},
contentType = "application/zip"
)
})
})
}
shinyApp(ui,server)
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