GC-MS based metabolomics data

knitr::opts_chunk$set(echo = F)
library(shiny)
library(DT)
library(mzrtsim)
data("hmdbcms")
data("monams1")

Input data

inputPanel(
        sliderInput(
        "ncomp",
        "Numbers of the compounds",
        min = 10,
        max = 2000,
        value = 100
        ),
        sliderInput(
        "ncpeaks",
        "Percentage of the compounds influnced by condition",
        min = 0,
        max = 1,
        value = 0.1
        ),
        sliderInput(
        "nbpeaks",
        "Percentage of the peaks influnced by batch",
        min = 0,
        max = 1,
        value = 0.3
        ),
        radioButtons('batchtype', 'batch type', c('m','b','mb'),
'mb'),
        radioButtons(inputId = 'db', label='database', choices = c('HMDB','MoNA'), selected = 'HMDB')
        )
sim <- reactive({
        if(input$db == 'HMDB'){
                data("hmdbcms")
                return(mzrtsim(ncomp = input$ncomp, ncpeaks = input$ncpeaks, nbpeaks = input$nbpeaks,db=hmdbcms,batchtype=input$batchtype))
        } else if(input$db == 'MoNA'){
                data("monams1")
                return(mzrtsim(ncomp = input$ncomp, ncpeaks = input$ncpeaks, nbpeaks = input$nbpeaks,db=monams1,batchtype=input$batchtype))
        }
})

Simulated data

DT::renderDataTable({
                sim <- sim()
                rbind.data.frame(Label = sim$group$sample_group, sim$data)
        })
downloadHandler('simdata.csv', content = function(file) {
                sim <- sim()
                data <- rbind.data.frame(Label = sim$group$sample_group, sim$data)
                write.csv(data, file)
        })

Baseline matrix

DT::renderDataTable({
                sim <- sim()
                rbind.data.frame(sim$group$sample_group,sim$matrix)
        })
downloadHandler('matrix.csv', content = function(file) {
                sim <- sim()
                data <- rbind.data.frame(sim$group$sample_group,sim$matrix)
                write.csv(data, file)
})

Batch effect matrix

DT::renderDataTable({
                sim <- sim()
                rbind.data.frame(sim$group$sample_group,sim$batch,sim$bmatrix)
        })
downloadHandler('batch.csv', content = function(file) {
                sim <- sim()
                data <- rbind.data.frame(sim$group$sample_group,sim$bmatrix)
                write.csv(data, file)
        })

Biological effect matrix

DT::renderDataTable({
                sim <- sim()
                rbind.data.frame(sim$group$sample_group, sim$batch, sim$cmatrix)
        })
downloadHandler('con.csv', content = function(file) {
                sim <- sim()
                data <- rbind.data.frame(sim$group$sample_group, sim$batch, sim$cmatrix)
                write.csv(data, file)
        })

Biological effect matrix with baseline

DT::renderDataTable({
                sim <- sim()
                rbind.data.frame(sim$group$sample_group, sim$batch, sim$compmatrix)
        })
downloadHandler('cb.csv', content = function(file) {
                sim <- sim()
                data <- rbind.data.frame(sim$group$sample_group, sim$batch, sim$compmatrix)
                write.csv(data, file)
        })

Biological folds change

DT::renderDataTable({
                sim <- sim()
                sim$changec
        })
downloadHandler('conc.csv', content = function(file) {
                sim <- sim()
                data <- sim$changec
                write.csv(data, file)
        })

Batch effect folds change

DT::renderDataTable({
                sim <- sim()
                sim$changeb
        })
downloadHandler('batc.csv', content = function(file) {
                sim <- sim()
                data <- sim$changeb
                write.csv(data, file)
        })

monotonic change

DT::renderDataTable({
                sim <- sim()
                sim$changem
        })
downloadHandler('batmc.csv', content = function(file) {
                sim <- sim()
                data <- sim$changem
                write.csv(data, file)
        })