319 lines
13 KiB
R
319 lines
13 KiB
R
#
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# This is a Shiny web application. You can run the application by clicking
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# the 'Run App' button above.
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#
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# Find out more about building applications with Shiny here:
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#
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# http://shiny.rstudio.com/
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#
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library(shiny)
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library(shinyjs)
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library(shinydashboard)
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#library("wesanderson") # ayyyy lmao hipster af
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library(dplyr)
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library(DT)
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library(ggplot2)
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library(grid) # for the info box
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library(plotly)
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library(shinycssloaders)
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library(NGLVieweR)
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library(httr)
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library(readr)
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library(RCurl)
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# make shiny non-stupid
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options(shiny.launch.browser = FALSE) # i am a big girl and can tie my own laces
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options(shiny.port = 8000) # don't change the port every time
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options(shiny.host = '0.0.0.0') # This means "listen to all addresses on all interfaces"
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options(DT.options = list(scrollX = TRUE))
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# An "application token" is required here. I generated this one like this:
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# curl -H "Content-Type: application/json" -d '{"name":"r-data"}' -u <username>:<password> https://git.tunstall.in/api/v1/users/sethp/tokens
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# Gitea access token
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access_token = read_lines("~/secret-token")
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gene_url = "https://git.tunstall.in/api/v1/repos/tanu/fellowship_dcdf/raw/tb_data_fc/dashboard/list_unique_missense_genes.csv"
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missense_gene_url = "https://git.tunstall.in/api/v1/repos/tanu/fellowship_dcdf/raw/tb_data_fc/dashboard/missense_genes_params.csv"
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alphafold_url = "https://alphafold.ebi.ac.uk/files/"
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genes=read_csv(paste0(gene_url,"?token=",access_token))
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unique_missense_genes = read_delim(paste0(missense_gene_url,"?token=",access_token))
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#genes = c("Gene 1", "Gene 2", "Gene 3", "Gene 4", "Gene 5")
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# Define UI for application that draws a histogram
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ui=dashboardPage(skin="purple",
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dashboardHeader(title="Tuberculosis Host"),
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dashboardSidebar(
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radioButtons("gene",
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label="Gene",
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choices = genes$Gene,
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selected="alr" # "alr" is a value
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),
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actionButton("clear_ngl",
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"Reset Structures"),
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actionButton("all_mutations",
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"All Mutations")
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),
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dashboardBody(
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useShinyjs(),
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fluidRow(
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box(
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title="Crystallised Structure",
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width=5,
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column(
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NGLVieweROutput("structure"),
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width=12
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)
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),
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box(
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title="AlphaFold Structure",
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width=5,
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column(
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NGLVieweROutput("af_structure"),
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width=12
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)
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),
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box(
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title="Mutation Information",
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width=2,
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column(
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htmlOutput("information"),
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width=12
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)
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)
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),
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fluidRow(
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column(
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DT::dataTableOutput('table'),
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width=12
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)
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),
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verbatimTextOutput("debug")
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)
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)
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# Define server logic required to draw a histogram
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server <- function(input, output) {
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output$distPlot <- renderPlot({
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# generate bins based on input$bins from ui.R
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x <- faithful[, 2]
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bins <- seq(min(x), max(x), length.out = input$bins + 1)
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# draw the histogram with the specified number of bins
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hist(x, breaks = bins, col = 'darkgray', border = 'white',
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xlab = 'Waiting time to next eruption (in mins)',
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main = 'Histogram of waiting times')
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})
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### NGLViewer ####
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# Structure Viewer WebGL/NGLViewR window
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output$structure <- renderNGLVieweR({
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selected_gene=input$gene
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ngl_gene=as.character(genes[genes$Gene==selected_gene,"PDB"])
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NGLVieweR(ngl_gene) %>%
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addRepresentation("cartoon",
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param = list(name = "cartoon",
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color="tan",
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#colorScheme = "bfactor",
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opacity = 1
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)
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) %>%
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stageParameters(backgroundColor = "white") %>%
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setQuality("high") %>%
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setFocus(0) %>%
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setSpin(FALSE)
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})
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output$af_structure <- renderNGLVieweR({
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selected_af_gene=input$gene
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ngl_af_gene=as.character(genes[genes$Gene==selected_af_gene,"AF_PDB"])
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#af_pdb=cat(content(GET(paste0(alphafold_url,"AF-",ngl_af_gene,"-F1-model_v4.pdb")), as="text"), "\n")
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af_pdb=content(GET(paste0(alphafold_url,"AF-",ngl_af_gene,"-F1-model_v4.pdb")), as="text")
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#print(af_pdb)
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#ngl_pdb_file=paste0(load_dir, "Data/", ngl_drug, '/output/depth/', ngl_gene, '_complex.pdb')
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NGLVieweR(af_pdb, format="pdb") %>%
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addRepresentation("cartoon",
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param = list(name = "cartoon",
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#color="tan"
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colorScheme = "bfactor",
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opacity = 1
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)
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) %>%
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stageParameters(backgroundColor = "white") %>%
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setQuality("high") %>%
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setFocus(0) %>%
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setSpin(FALSE)
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})
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# output$table <- DT::renderDataTable({
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# selected_gene=input$gene
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# gene=as.character(genes[genes$Gene==selected_gene,"Gene"])
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# #unique_missense_genes[unique_missense_genes$gene_name==gene,]
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# unique_missense_genes
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# },
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# selection = "single",
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# search = list(search="alr"))
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output$table <- DT::renderDataTable(
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unique_missense_genes,
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selection = "single",
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options=list(
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search = list(search=as.character(genes[genes$Gene==input$gene,"Gene"]))
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)
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)
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observeEvent(input$table_rows_selected,{
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#req(length(input$table_row_selected) > 0)
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mutation = as.character(unique_missense_genes[input$table_rows_selected,"hgvd_p"])
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chain = as.character(unique_missense_genes[input$table_rows_selected,"chain"])
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# what the absolute FUCK is this mess? I JUST WANT THE MATCH ASDLASJASDASHFKLJASDFK
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# coming down from the trees was a mistake, and abandoning Perl doubly so.
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clicked_position = regmatches(mutation, regexpr("[0-9]+", mutation, perl=TRUE))
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# Now update the 3D structure to highlight the clicked thing and then zoom in.
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NGLVieweR_proxy("structure") %>%
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#addSelection('ball+stick'
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addSelection('spacefill'
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, param = list(
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name = "Pos"
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, sele = trimws(paste0(clicked_position,':', chain))
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, color = "red"
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#, colorValue="00ff00"
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#, colorScheme="element"
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)
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)
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NGLVieweR_proxy("af_structure") %>%
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#addSelection('ball+stick'
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addSelection('spacefill'
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, param = list(
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name = "Pos"
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, sele = clicked_position
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, color = "red"
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#, colorValue="00ff00"
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#, colorScheme="element"
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)
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)
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NGLVieweR_proxy("af_structure") %>% updateZoomMove(
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center = clicked_position,
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zoom = clicked_position,
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duration = 1000, # animation time in ms
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z_offSet = -1
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)
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NGLVieweR_proxy("structure") %>% updateZoomMove(
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center = trimws(paste0(clicked_position,':', chain)),
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zoom = trimws(paste0(clicked_position,':', chain)),
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duration = 1000, # animation time in ms
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z_offSet = -1
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)
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output$information <- renderUI({
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selected_gene=input$gene
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HTML(paste0("<strong>Mutation: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mutationinformation"]),"<br/>",
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"<strong>PDB ID: </strong>", as.character(genes[genes$Gene==selected_gene,"PDB"]),"<br/>",
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"<strong>AlphaFold ID: </strong>", as.character(genes[genes$Gene==selected_gene,"AF_PDB"]), "<br/>",
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"<strong>Ligand Distance: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ligand_distance"]), "<br/>",
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"<strong>mCSM Lig: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mcsm_lig"]), "<br/>",
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"<strong>mmCSM Lig: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mmcsm_lig"]), "<br/>",
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"<strong>mCSM DUET: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_duet"]), "<br/>",
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"<strong>FoldX: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_foldx"]), "<br/>",
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"<strong>DeepDDG: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_deepddg"]), "<br/>",
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"<strong>Dynamut2: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"ddg_dynamut2"]), "<br/>",
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"<strong>mCSM PPI2: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mcsm_ppi2_affinity"]), "<br/>",
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"<strong>Interface Distance: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"interface_dist"]), "<br/>",
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"<strong>mCSM NA: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"mcsm_na_affinity"]), "<br/>",
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"<strong>Consurf: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"consurf_score"]), "<br/>",
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#"<strong>SNAP2: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"snap2_score"]), "<br/>",
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"<strong>SNAP2 Outcome: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"snap2_outcome"]), "<br/>",
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#"<strong>PROVEAN: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"provean_score"]), "<br/>",
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"<strong>PROVEAN Outcome: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"provean_outcome"]), "<br/>",
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"<strong>RSA: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"rsa"]), "<br/>",
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"<strong>Residue Depth: </strong>", as.character(unique_missense_genes[input$table_rows_selected,"rd_values"]), "<br/>"
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)
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)
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})
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# output$debug <- renderPrint({
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# print(c(mutation, clicked_position))
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# })
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})
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observeEvent(
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{
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input$clear_ngl
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}, {
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NGLVieweR_proxy("structure") %>%
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removeSelection("Pos") %>%
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removeSelection("all_mutations_surface") %>%
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updateVisibility("cartoon", TRUE) %>%
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removeSelection("all_mutations")
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NGLVieweR_proxy("af_structure") %>%
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removeSelection("Pos") %>%
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removeSelection("all_mutations_surface") %>%
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updateVisibility("cartoon", TRUE) %>%
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removeSelection("all_mutations")
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})
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# add a surface representation and highlight all mutations on it
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observeEvent(
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{
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input$all_mutations
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}, {
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gene = input$gene
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mutations = paste0(":",
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as.matrix(unique_missense_genes[unique_missense_genes$gene_name == gene,c("chain")])[1],
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" and (",
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paste0(
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apply(
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unique_missense_genes[unique_missense_genes$gene_name == gene,c("position","chain")],
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1,
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function(x){
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paste0(trimws(x[1])
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)}
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),
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collapse=", "
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), ")"
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)
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print(mutations)
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NGLVieweR_proxy("structure") %>%
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updateVisibility("cartoon", FALSE) %>%
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addSelection(type="surface",
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param = list(name = "all_mutations_surface",
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sele = "all",
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color="tan",
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opacity = 0.2
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)
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) %>%
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addSelection(type="surface",
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param = list(name = "all_mutations",
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color="red",
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sele = mutations
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#colorScheme = "bfactor",
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#opacity = 1
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)
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)
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NGLVieweR_proxy("af_structure") %>%
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updateVisibility("cartoon", FALSE) %>%
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addSelection(type="surface",
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param = list(name = "all_mutations_surface",
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sele = "all",
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color="tan",
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opacity = 0.2
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)
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) %>%
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addSelection(type="surface",
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param = list(name = "all_mutations",
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color="red",
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sele = mutations
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#colorScheme = "bfactor",
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#opacity = 1
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)
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)
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})
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}
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# Run the application
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shinyApp(ui = ui, server = server)
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