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feature complete dashboard

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This commit is contained in:
Tanushree Tunstall 2022-12-10 16:34:47 +00:00
parent 6f2cb5bb70
commit e7725235bb
1 changed files with 118 additions and 25 deletions
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143
tb_host/app.R
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@ -18,13 +18,29 @@ library(grid) # for the info box
library(plotly)
library(shinycssloaders)
library(NGLVieweR)
library(httr)
library(readr)
library(RCurl)
# make shiny non-stupid
options(shiny.launch.browser = FALSE) # i am a big girl and can tie my own laces
options(shiny.port = 8000) # don't change the port every time
options(shiny.host = '0.0.0.0') # This means "listen to all addresses on all interfaces"
options(DT.options = list(scrollX = TRUE))
genes = c("Gene 1", "Gene 2", "Gene 3", "Gene 4", "Gene 5")
# An "application token" is required here. I generated this one like this:
# curl -H "Content-Type: application/json" -d '{"name":"r-data"}' -u <username>:<password> https://git.tunstall.in/api/v1/users/sethp/tokens
# Gitea access token
access_token = read_lines("~/secret-token")
gene_url = "https://git.tunstall.in/api/v1/repos/tanu/fellowship_dcdf/raw/tb_data_fc/dashboard/list_unique_missense_genes.csv"
missense_gene_url = "https://git.tunstall.in/api/v1/repos/tanu/fellowship_dcdf/raw/tb_data_fc/dashboard/missense_genes_params.csv"
alphafold_url = "https://alphafold.ebi.ac.uk/files/"
genes=read_csv(paste0(gene_url,"?token=",access_token))
unique_missense_genes = read_delim(paste0(missense_gene_url,"?token=",access_token))
#genes = c("Gene 1", "Gene 2", "Gene 3", "Gene 4", "Gene 5")
# Define UI for application that draws a histogram
ui=dashboardPage(skin="purple",
@ -32,9 +48,12 @@ ui=dashboardPage(skin="purple",
dashboardSidebar(
radioButtons("gene",
label="Gene",
choices = genes,
selected="Gene 1" # "none" is a value
)
choices = genes$Gene,
selected="alr" # "alr" is a value
),
actionButton("clear_ngl",
"Clear Structures")
),
dashboardBody(
useShinyjs(),
@ -80,47 +99,121 @@ server <- function(input, output) {
### NGLViewer ####
# Structure Viewer WebGL/NGLViewR window
output$structure <- renderNGLVieweR({
#ngl_gene=isolate(input$switch_target)
#ngl_gene=input$switch_target
#ngl_drug=target_map[[ngl_gene]]
#ngl_pdb_file=paste0(load_dir, "Data/", ngl_drug, '/output/depth/', ngl_gene, '_complex.pdb')
#print(ngl_pdb_file)
#NGLVieweR(ngl_pdb_file) %>%
NGLVieweR("3pl1") %>%
selected_gene=input$gene
ngl_gene=as.character(genes[genes$Gene==selected_gene,"PDB"])
NGLVieweR(ngl_gene) %>%
addRepresentation("cartoon",
param = list(name = "cartoon",
color="tan"
#, colorScheme = "chainid"
#color="tan"
colorScheme = "bfactor",
opacity = 1
)
) %>%
stageParameters(backgroundColor = "lightgrey") %>%
stageParameters(backgroundColor = "white") %>%
setQuality("high") %>%
setFocus(0) %>%
setSpin(FALSE)
})
output$af_structure <- renderNGLVieweR({
#ngl_gene=isolate(input$switch_target)
#ngl_gene=input$switch_target
#ngl_drug=target_map[[ngl_gene]]
selected_af_gene=input$gene
ngl_af_gene=as.character(genes[genes$Gene==selected_af_gene,"AF_PDB"])
#af_pdb=cat(content(GET(paste0(alphafold_url,"AF-",ngl_af_gene,"-F1-model_v4.pdb")), as="text"), "\n")
af_pdb=content(GET(paste0(alphafold_url,"AF-",ngl_af_gene,"-F1-model_v4.pdb")), as="text")
#print(af_pdb)
#ngl_pdb_file=paste0(load_dir, "Data/", ngl_drug, '/output/depth/', ngl_gene, '_complex.pdb')
#print(ngl_pdb_file)
#NGLVieweR(ngl_pdb_file) %>%
NGLVieweR("3pl1") %>%
NGLVieweR(af_pdb, format="pdb") %>%
addRepresentation("cartoon",
param = list(name = "cartoon",
color="tan"
#, colorScheme = "chainid"
#color="tan"
colorScheme = "bfactor",
opacity = 1
)
) %>%
stageParameters(backgroundColor = "lightgrey") %>%
stageParameters(backgroundColor = "white") %>%
setQuality("high") %>%
setFocus(0) %>%
setSpin(FALSE)
})
output$table = DT::renderDataTable(mtcars)
# output$table <- DT::renderDataTable({
# selected_gene=input$gene
# gene=as.character(genes[genes$Gene==selected_gene,"Gene"])
# #unique_missense_genes[unique_missense_genes$gene_name==gene,]
# unique_missense_genes
# },
# selection = "single",
# search = list(search="alr"))
output$table <- DT::renderDataTable(
unique_missense_genes,
selection = "single",
options=list(
search = list(search=as.character(genes[genes$Gene==input$gene,"Gene"]))
)
)
observeEvent(input$table_rows_selected,{
#req(length(input$table_row_selected) > 0)
mutation = as.character(unique_missense_genes[input$table_rows_selected,"hgvd_p"])
# what the absolute FUCK is this mess? I JUST WANT THE MATCH ASDLASJASDASHFKLJASDFK
# coming down from the trees was a mistake, and abandoning Perl doubly so.
clicked_position = regmatches(mutation, regexpr("[0-9]+", mutation, perl=TRUE))
# Now update the 3D structure to highlight the clicked thing and then zoom in.
NGLVieweR_proxy("structure") %>%
#addSelection('ball+stick'
addSelection('ball+stick'
, param = list(
name = "Pos"
, sele = clicked_position
, color = "green"
#, colorValue="00ff00"
#, colorScheme="element"
)
)
NGLVieweR_proxy("af_structure") %>%
#addSelection('ball+stick'
addSelection('ball+stick'
, param = list(
name = "Pos"
, sele = clicked_position
, color = "green"
#, colorValue="00ff00"
#, colorScheme="element"
)
)
NGLVieweR_proxy("af_structure") %>% updateZoomMove(
center = clicked_position,
zoom = clicked_position,
duration = 1000, # animation time in ms
z_offSet = -1
)
NGLVieweR_proxy("structure") %>% updateZoomMove(
center = clicked_position,
zoom = clicked_position,
duration = 1000, # animation time in ms
z_offSet = -1
)
# output$debug <- renderPrint({
# print(c(mutation, clicked_position))
# })
})
observeEvent(
{
input$clear_ngl
}, {
NGLVieweR_proxy("structure") %>%
removeSelection("Pos")
NGLVieweR_proxy("af_structure") %>%
removeSelection("Pos")
})
}
# Run the application
shinyApp(ui = ui, server = server)