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server.R
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server.R
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## server.R ##
# PopHumanVar is licensed under the GNU General Public License (GPL) v3.0 (https://github.com/ainacolovila/PopHumanVar/blob/master/LICENSE)
server <- function(input, output, session) {
###############################################
# MODAL DIALOGUES #############################
###############################################
# First Pop Up ###############################
observeEvent("", {
showModal(modalDialog(
includeHTML("intro_text.html"),
easyClose = TRUE,
footer = tagList(
actionButton(inputId = "modalButton",
label = "UNDERSTOOD",
icon = icon("thumbs-up"),
color = "success")
)
))
})
observeEvent(input$modalButton,{
removeModal()
})
# Function that Return the UI for a modal dialog with ID selection input.
# If 'failed' is TRUE, then display a message that the previous value was invalid.
dataModal <- function(failed = FALSE) {
modalDialog(
h2("Quick search by ID"),
span("This action will only update the chromosome and the coordinates while keeping the rest of filters as they were"),
br(),
br(),
textInput("rsidSearch",
"Enter an ID:",
placeholder = "Variant ID, Gene symbol or Ensembl ID",
width = "90%"
),
div(HTML("Examples: rs3827760, <em>EDAR</em>, ENSG00000139618...")),
br(),
sliderInput(
'contextSpace',
'Flanking region on each side:',
min = 0,
max = 10,
step = 0.5,
value = 5,
post = " kb",
width = "80%"
),
span("If a variant ID provided, this value must be > 0kb"),
br(),
if (failed)
div(tags$b("(!) INVALID ID SEARCH", style = "color: red;")),
easyClose = FALSE,
size = "m",
fade = TRUE,
footer = tagList(
modalButton("Cancel"),
actionButton("search", "Search")
)
)
}
# Show modal when button is clicked
observeEvent(input$SearchID, {
showModal(dataModal())
})
# Function to retrieve chr and coordinate
# When OK button is pressed, attempt to load the data set. If successful,
# remove the modal. If not show another modal, but this time with a failure
# message.
observeEvent(input$search, {
if (!is.null(input$rsidSearch) && nzchar(input$rsidSearch)) {
# Run function to get coordinates
idcoords <- getGenomicCoordinates(input$rsidSearch)
# Check that ID was found
if(nrow(idcoords)>0) {
# Consider the flanking region
flanking <- ifelse(input$contextSpace==0, 0,(input$contextSpace*1000))
# UPDATE DATA
updateSelectInput(session, "chrInput", selected = idcoords$chr[1])
# UPDATE coords
updateTextInput(session, "minPosInput",
label = 'Start Position',
value = idcoords$start[1]-flanking
)
updateTextInput(session, "maxPosInput",
label = 'End Position',
value = idcoords$end[1]+flanking
)
# Remove modal and click update button
click("UpdateButt")
removeModal()
}else{showModal(dataModal(failed = TRUE))}
}else{
showModal(dataModal(failed = TRUE))
}
})
#Observation to control max lenght of the input regions
observe({
if((as.numeric(input$maxPosInput) - as.numeric(input$minPosInput)) > 2000000){
sendSweetAlert(
session = session,
title = "Error!",
closeOnClickOutside = FALSE,
text = tags$span(
tags$h2("Coordinates must be within a 2Mb range!"),tags$br(),"We have automatically modified your input to match the maximum length allowed."),
html = TRUE,
type = "error"
)
updateTextInput(session, "maxPosInput", label = 'End Position', value = as.numeric(input$minPosInput) + 2*10^6)
}
})
###############################################
# ALL OBSERVATIONS (btns etc) #################
###############################################
# Metapopts-Pops link
observe({
if(length(input$metapopsInput)!=0){
popstoSelect <- c()
for(selMetapop in input$metapopsInput){
popstoSelect <- c(popstoSelect,get(paste0(selMetapop, "pops")))
}
updateCheckboxGroupButtons(session, 'popsInput', selected = popstoSelect)
}
})
# Populations
observe({
if (input$selectAllPopsInput > 0){
if (input$selectAllPopsInput %% 2 == 0){
updateCheckboxGroupButtons(session, 'popsInput', selected = popsData$pops)
updateCheckboxGroupButtons(session, 'metapopsInput', selected = metapopsData$metapops)
updateActionButton(session, inputId="selectAllPopsInput", label = "Deselect All")
}
else {
updateCheckboxGroupButtons(session, 'popsInput', selected = '')
updateCheckboxGroupButtons(session, 'metapopsInput', selected = '')
updateActionButton(session, inputId="selectAllPopsInput", label = "Select All")
}
}
})
# VEP
observe({
if (input$selectAllVEPInput > 0){
if (input$selectAllVEPInput %% 2 == 0){
updateCheckboxGroupInput(session, 'VEPInput', selected = '')
updateActionButton(session, inputId="selectAllVEPInput", label = "Select All")
}
else {
updateCheckboxGroupInput(session, 'VEPInput', selected = VEPinfo$outLabels)
updateActionButton(session, inputId="selectAllVEPInput", label = "Deselect All")
}
}
})
# Clinvar
observe({
if (input$selectAllClinVarInput > 0){
if (input$selectAllClinVarInput %% 2 == 0){
updateCheckboxGroupInput(session, 'ClinVarInput', selected = '')
updateActionButton(session, inputId="selectAllClinVarInput", label = "Select All")
}
else {
updateCheckboxGroupInput(session, 'ClinVarInput', selected = ClinVarLabels$ClinicalSign)
updateActionButton(session, inputId="selectAllClinVarInput", label = "Deselect All")
}
}
})
# Zoom buttons
observeEvent(input$zoomCoordenates,{
# Get absolute min max for the present chromosome
ABSminNmax <- getSliderValues(input$chrInput,NULL) %>% as.data.frame
# Compute increment of coordinates input (100k change)
newStart <- as.numeric(input$minPosInput)+(as.numeric(input$zoomCoordenates)*100000)
newEnd <- as.numeric(input$maxPosInput)-(as.numeric(input$zoomCoordenates)*100000)
# If chromosome end reached, keep last position with data
if(newStart < ABSminNmax$minV){newStart<-ABSminNmax$minV}
if(newEnd > ABSminNmax$maxV){newEnd <-ABSminNmax$maxV}
# Check limits and change number
if(newStart < newEnd){
updateTextInput(session, "minPosInput", label = 'Start Position', value = newStart)
updateTextInput(session, "maxPosInput", label = 'End Position', value = newEnd)
} else {
sendSweetAlert(
session = session,
title = "Warning!",
closeOnClickOutside = FALSE,
text = tags$span(
tags$h2("You can not ZOOM IN anymore."),tags$br()," Since the zoom is of 100kb, next increment would invert the ",tags$b("START"),"and",tags$b("END"), "coordinates.",tags$br(),"If you would like a smaller increment, please, update it manually."),
html = TRUE,
type = "warning"
)
}
# After action, unclick botton
updateRadioGroupButtons(session, "zoomCoordenates", selected=character(0))
})
# When click Update button, update report button as well
observeEvent(input$UpdateButt, {
click("ButtonNum2")
click("ButtonNum3")
})
# Change max and mins (not SELECTION!) from filters according to chr infoData
observeEvent(input$UpdateButt,{
# Get absolute min max for this chromosome
minNmax <- getSliderValues(input$chrInput,input$AgeModelInput) %>% as.data.frame
# Update widgets according to this
updateSliderInput(session, "GWASInput", min = minNmax$minV[3], max = minNmax$maxV[3])
updateSliderInput(session, "NofPmidsInput", min = minNmax$minV[2],max = minNmax$maxV[2])
updateSliderInput(session, "AgeModeInput", min = minNmax$minV[1], max = minNmax$maxV[1])
})
# Same but for the right side of the report plot
observeEvent(input$ButtonNum2,{
minNmax <- getSliderValues(input$chrInput, input$Overview_Clock) %>% as.data.frame
updateSliderInput(session, "Overview_AgeMode",
min = minNmax$minV[1],max = minNmax$maxV[1])
})
observeEvent(input$UpdateButt, {
updatePickerInput(session,"headersDownload",choices = set_download()[[2]])
})
# Select/Desselect button download tab
observe({
if (input$selectAllheaders > 0){
if (input$selectAllheaders %% 2 == 0){
updatePickerInput(session,"headersDownload", selected='')
updateActionButton(session, inputId="selectAllheaders", label = "Select All")
}
else {
updatePickerInput(session,"headersDownload", selected=set_download()[[2]])
updateActionButton(session, inputId="selectAllheaders", label = "Deselect All")
}
}
})
###############################################
# REACTIVE VARIABLES 'N' DATASETS #############
###############################################
# Value with current chr
chrSelect <- reactive({
input$UpdateButt
isolate({
input$chrInput
})
})
# Vector with selected populations
popSelect <- reactive({
input$UpdateButt
isolate({
input$popsInput %>% as.vector()
})
})
# info Selection TAB
set_selection <- reactive({
input$UpdateButt
isolate({
raw_ihs = sqlToDf(local(input$chrInput),local(input$minPosInput),local(input$maxPosInput),'ihs') %>%
dplyr::select(physicalPos, rsid, starts_with(popSelect())) %>%
filter_at(vars(-c("physicalPos","rsid")), any_vars(!is.na(.))) %>%
as.data.table
raw_nsl = sqlToDf(local(input$chrInput),local(input$minPosInput),local(input$maxPosInput),'nsl') %>%
dplyr::select(physicalPos, rsid, starts_with(popSelect())) %>%
filter_at(vars(-c("physicalPos","rsid")), any_vars(!is.na(.))) %>%
as.data.table
list(raw_ihs,raw_nsl)
})
})
set_filter_ihs <- reactive({
input$UpdateButt
isolate({
selection_melt_ihs = meltDFtoPlot(set_selection()[[1]], 'ihs') %>% mutate(AD = ifelse(value > 0, "Derived", "Ancestral"))
selection_melt_filtered_ihs = filterThreshold(df_stat_melted=selection_melt_ihs,threshold_df=ihs_thres,extreme=T)
list(selection_melt_ihs,selection_melt_filtered_ihs)
})
})
set_filter_nsl <- reactive({
input$UpdateButt
isolate({
selection_melt_nsl = meltDFtoPlot(set_selection()[[2]], 'nsl') %>% mutate(AD = ifelse(value > 0, "Derived", "Ancestral"))
selection_melt_filtered_nsl = filterThreshold(df_stat_melted=selection_melt_nsl,threshold_df=nsl_thres,extreme=T)
list(selection_melt_nsl,selection_melt_filtered_nsl)
})
})
set_selection_to_table <- reactive({
input$UpdateButt
isolate({
render_ihs = set_filter_ihs()[[2]]
render_nsl = set_filter_nsl()[[2]]
if(input$extremeValuesInput_ihs){
render_ihs = render_ihs[thres_ad!='Not extreme']
}
if(input$extremeValuesInput_nsl){
render_nsl = render_nsl[thres_ad!='Not extreme']
}
#Rendering table
if(nrow(render_ihs) < 1 & nrow(render_nsl) > 1){
dcast_nsl = dcast(render_nsl,physicalPos + rsid ~ POP)
#Suffix manual
names(dcast_nsl)[3:ncol(dcast_nsl)] = paste0(names(dcast_nsl)[3:ncol(dcast_nsl)] , '_nSL')
render_table = dcast_nsl
}
else if(nrow(render_ihs) > 1 & nrow(render_nsl) < 1 ){
dcast_ihs = dcast(render_ihs,physicalPos + rsid ~ POP)
#Suffix manual
names(dcast_ihs)[3:ncol(dcast_ihs)] = paste0(names(dcast_ihs)[3:ncol(dcast_ihs)] , '_iHS')
render_table = dcast_ihs
}
else if(nrow(render_ihs) < 1 & nrow(render_nsl) < 1){
# render_table = set_selection()[0]
render_table = data.table('physicalPos'=as.numeric(),'rsid'=as.character())
}
else{
dcast_ihs = dcast(render_ihs,physicalPos + rsid ~ POP)
dcast_nsl = dcast(render_nsl,physicalPos + rsid ~ POP)
#Suffix manual
names(dcast_nsl)[3:ncol(dcast_nsl)] = paste0(names(dcast_nsl)[3:ncol(dcast_nsl)] , '_nSL')
names(dcast_ihs)[3:ncol(dcast_ihs)] = paste0(names(dcast_ihs)[3:ncol(dcast_ihs)] , '_iHS')
render_table = merge(dcast_ihs,dcast_nsl,all=T)
}
names(render_table)[1] = 'Position'
render_table
})
})
# info FUNCT TAB
set_functional <- reactive({
input$UpdateButt
isolate({
raw_functional = sqlToDf(local(input$chrInput),local(input$minPosInput),local(input$maxPosInput),'functional')
raw_functional$NumGWASStudies <- NA
raw_functional$NumDGNAsso <- NA
# Add GWAS_count and Disease_count
if(sum(!is.na(raw_functional$accession))>0){
raw_functional$NumGWASStudies[!is.na(raw_functional$accession)] <- do.call(rbind, lapply(strsplit(raw_functional$accession[!is.na(raw_functional$accession)],";"), function(x) length(x)))
}
if(sum(!is.na(raw_functional$diseaseId))>0){
raw_functional$NumDGNAsso[!is.na(raw_functional$diseaseId)] <- do.call(rbind, lapply(strsplit(raw_functional$diseaseId[!is.na(raw_functional$diseaseId)],";"), function(x) length(x)))
}
# data() %>% dplyr::select(physicalPos, rsid, gene_effect, effect, impact, TFbinding,DNasePeak, motif, DNaseFootprint, eQTL, matchedTFmotif, matchedDNaseFootprint, ranking,risk_allele, risk_allele_freq, trait, accession, NumGWASStudies, context, diseaseName, diseaseId, NumDGNAsso, diseaseType, source, DSI, DPI, EI, NofPmids, ClinicalSignificance, ClinSigSimple,PhenotypeList) %>% filter_at(vars(-c("physicalPos","rsid")), any_vars(!is.na(.))) %>% as.data.table
raw_functional %>% filter_at(vars(-c("physicalPos","rsid")), any_vars(!is.na(.))) %>% as.data.table
})
})
set_snpeff <- reactive({
input$UpdateButt
isolate({
filtered = set_functional() %>%
dplyr::select(physicalPos,rsid,gene_effect, effect, impact) %>% na.omit %>% unique
VEPmask1 = VEPinfo$outLabels %in% input$VEPInput
toPlot = SplitDf(filtered,c1=c("gene_effect","impact","effect"),c2=c("effect"),s1=",",s2="&")
toPlot = toPlot[effect %in% VEPinfo$inLabels[VEPmask1]]
toPlot$rank = 28
for(i in unique(na.omit(toPlot$effect))){
toPlot[effect == i]$rank = VEPinfo[inLabels == i]$rank
}
# toPlot = toPlot[impact!='MODIFIER']
toPlot = toPlot[toPlot[,.I[which.min(rank)], by=physicalPos]$V1]
toPlot = toPlot[,1:5]
VEPmask2 = VEPinfo$inLabels %in% as.character(toPlot$effect %>% unique)
# Mask to filter
# #The default order will be alphabetized unless specified as below:
toPlot$effect <- factor(toPlot$effect,levels=VEPinfo$inLabels[VEPmask2])
toPlot$Labs = gsub("_"," ",toPlot$effect)
toPlot$Labs = str_to_title(toPlot$Labs)
list(toPlot,VEPmask2)
})
})
# info GEVA TAB
set_age <- reactive({
input$UpdateButt
isolate({
tmp = sqlToDf(local(input$chrInput),local(input$minPosInput),local(input$maxPosInput),'geva')
toFilter <- tmp %>% dplyr::select(physicalPos, rsid, AlleleRef, AlleleAlt, AlleleAnc, ends_with(input$AgeModelInput))
names(toFilter)<- gsub(paste0('_',input$AgeModelInput),'', names(toFilter))
toPlot <- toFilter %>% filter_at(vars(starts_with('Age')), any_vars(!is.na(.))) %>% as.data.table
toPlot <- toPlot[AgeMode >= input$AgeModeInput[1] & AgeMode <= input$AgeModeInput[2] & QualScore >= input$qualScoreInput[1] & QualScore <= input$qualScoreInput[2]]
toPlot
})
})
# info FAV MUTATION TAB
set_favallel <- reactive({
input$UpdateButt
isolate({
raw_isafe = sqlToDf(local(input$chrInput),local(input$minPosInput),local(input$maxPosInput),'isafe') %>% dplyr::select(physicalPos,rsid,starts_with(popSelect())) %>% dplyr::filter_at(vars(-c("physicalPos","rsid")), any_vars(!is.na(.))) %>%
as.data.table
isafe_melt <- meltDFtoPlot(raw_isafe, 'isafe')
tmp = isafe_melt
tmp$AD = 'Top 0.01%'
isafe_melt_filtered = filterThreshold(df_stat_melted=tmp,threshold_df=isafe_thres,extreme=T)
render_table_tmp = isafe_melt_filtered
if(input$extremeISAFE){
render_table_tmp = isafe_melt_filtered[thres_ad != 'Not extreme']
for(p in unique(isafe_melt_filtered$POP)){
isafe_melt_filtered[POP == p & thres_ad != 'Not extreme']$thres_ad = paste0(isafe_melt_filtered[POP == p & thres_ad != 'Not extreme']$thres_ad,' ',p)
}
# names(render_table)[3:ncol(render_table)] = paste0(names(render_table)[3:ncol(render_table)],'_isafe')
}
if(nrow(render_table_tmp) < 1){
render_table = data.table('physicalPos'=as.numeric(),'rsid'=as.character())
}
else{
render_table = dcast(render_table_tmp, physicalPos + rsid ~ POP)
}
names(render_table)[1] = 'Position'
list(isafe_melt,isafe_melt_filtered,render_table)
})
})
# Subset final Report
set_report <- reactive({
input$ButtonNum2
isolate({
# Generate data
filtered <- mergeAllDF(local(input$chrInput),local(input$minPosInput),local(input$maxPosInput)) %>% dplyr::select(physicalPos, rsid,
starts_with(input$Overview_Pop), effect, impact,'RDBranking'=ranking,
ClinicalSignificance, DSI, EI, NumGWASStudies,
paste0(c('AgeMode_','QualScore_','AgeCI95Lower_', 'AgeCI95Upper_'), input$Overview_Clock))
# Change columns to ease operate with them
names(filtered) = gsub(paste0(input$Overview_Pop,'_'), "", names(filtered))
names(filtered) = gsub(paste0("_", input$Overview_Clock),"", names(filtered))
# Filter according the side gadgets
filtered = filtered[AgeMode > input$Overview_AgeMode[1] & AgeMode < input$Overview_AgeMode[2]]
filtered = filtered[QualScore > input$Overview_qualScore[1] & QualScore < input$Overview_qualScore[2]]
# Modify dataset to obtain Plot
expandDF <- SplitDf(filtered, c1=c("effect","impact"), c2=c("effect"),s1=",",s2="&")
expandDF$rank <- 0
for(i in unique(na.omit(expandDF$effect))){
expandDF[effect == i]$rank = VEPinfo[inLabels == i]$rank
}
expandDF$rank[expandDF$rank==0]<-NA
toPlot <- expandDF[expandDF[,.I[which.min(rank)], by=list(physicalPos, rsid)]$V1]
toPlot$isafe[is.na(toPlot$isafe)]<-0
toPlot$ihs[is.na(toPlot$ihs)]<-0
toPlot$nsl[is.na(toPlot$nsl)]<-0
toPlot
})
})
# Subset prioretized regions
set_infoBoxes<-reactive({
input$ButtonNum2
isolate({
# Sort and change columns names
# Generate data
filtered <- mergeAllDF(local(input$chrInput),local(input$minPosInput),local(input$maxPosInput)) %>%
dplyr::select(physicalPos, rsid, starts_with(input$Overview_Pop), NumGWASStudies, effect, impact,'RDBranking'=ranking, ClinicalSignificance, diseaseId, NofPmids,paste0(c('AgeMode_','QualScore_','AgeCI95Lower_', 'AgeCI95Upper_'), input$Overview_Clock))
# Change columns to ease operate with them
names(filtered) = gsub(paste0(input$Overview_Pop,'_'), "", names(filtered))
names(filtered) = gsub(paste0("_", input$Overview_Clock),"", names(filtered))
# Filter according the side gadgets
filtered = filtered[AgeMode > input$Overview_AgeMode[1] & AgeMode < input$Overview_AgeMode[2]]
filtered = filtered[QualScore > input$Overview_qualScore[1] & QualScore < input$Overview_qualScore[2]]
# Modify dataset to obtain Plot
expandDF <- SplitDf(filtered, c1=c("effect","impact"), c2=c("effect"),s1=",",s2="&")
expandDF$rankSnpEFF <- 0
for(i in unique(na.omit(expandDF$effect))){expandDF[effect == i]$rankSnpEFF = VEPinfo[inLabels == i]$rank}
expandDF$rankSnpEFF[expandDF$rankSnpEFF==0]<-NA
toPlot <- expandDF[expandDF[,.I[which.min(rankSnpEFF)], by=list(physicalPos, rsid)]$V1]
toPlot$isafe[is.na(toPlot$isafe)]<-0
toPlot$ihs[is.na(toPlot$ihs)]<-0
toPlot$nsl[is.na(toPlot$nsl)]<-0
toPlot$ihsABS <- abs(toPlot$ihs)
toPlot$nslABS <- abs(toPlot$nsl)
toPlot$ClinVarRank <- match(toPlot$ClinicalSignificance, ClinVarLabels$ClinicalSign, nomatch=NA)
toShow <- toPlot %>% dplyr::select(physicalPos, rsid, isafe, ihs, ihsABS, nsl, nslABS,
NumGWASStudies, rankSnpEFF, effect, impact, RDBranking, ClinicalSignificance, ClinVarRank, diseaseId, NofPmids)
toShow
})
})
# Subset prioretized regions
set_prior<-reactive({
input$ButtonNum2
isolate({
# Sort and change columns names
toFilter <- set_report() %>%
dplyr::arrange(
desc(isafe),
desc(abs(ihs)),
desc(abs(nsl)),
desc(NumGWASStudies),
rank,
RDBranking) %>%
dplyr::select(
'Position'=physicalPos,
rsid,
'iSAFE'=isafe,
'iHS'=ihs,
'nSL'=nsl,
'GWAS assoc.'=NumGWASStudies,
'Variant effect'=effect,
'Regulome Score'=RDBranking,
'Generations'=AgeMode
)
# Subset top 20
finalSubset <-toFilter[1:20,]
finalSubset$'Variant effect' <- str_to_title(gsub("_"," ",finalSubset$'Variant effect'))
finalSubset
})
})
# Subset download page
# Subset final Report
set_download <- reactive({
input$UpdateButt
# input$ButtonNum3
isolate({
df_stat = set_selection_to_table()
names(df_stat)[1] = 'physicalPos'
df_isafe = set_favallel()[[3]]
names(df_isafe)[1] = 'physicalPos'
# manually forced to merge if non extreme values
if(ncol(df_isafe) > 2){
names(df_isafe)[3:length(names(df_isafe))] <- paste0(names(df_isafe)[3:length(names(df_isafe))], "_isafe")
}
df_snpeff = set_snpeff()[[1]]
names(df_snpeff)[1] = 'physicalPos'
df_geva = set_age()
names(df_geva)[1] = 'physicalPos'
df_func = set_functional() %>% dplyr::select(-c(effect, impact,gene_effect)) %>%
dplyr::filter((ranking >= input$RegulomeInput[1] & ranking <= input$RegulomeInput[2]) | is.na(ranking)) %>%
dplyr::filter((ClinicalSignificance %in% input$ClinVarInput) | is.na(ClinicalSignificance)) %>%
dplyr::filter((NumGWASStudies >= input$GWASInput[1] & ranking <= input$RegulomeInput[2]) | is.na(ranking)) %>%
dplyr::filter((NumGWASStudies > input$GWASInput[1] & NumGWASStudies < input$GWASInput[2]) | is.na(NumGWASStudies)) %>%
dplyr::filter((DSI >= input$DSIInput[1] & DSI <= input$DSIInput[2]) | is.na(DSI)) %>%
dplyr::filter((EI >= input$EInput[1] & EI <= input$EInput[2]) | is.na(EI)) %>%
dplyr::filter((NofPmids >= input$NofPmidsInput[1] & NofPmids <= input$NofPmidsInput[2]) | is.na(NofPmids))
# Merge All
allDF = Reduce(function(x, y) merge(x, y, all=TRUE, by=c("physicalPos", "rsid")),list(df_stat,df_isafe,df_snpeff,df_func,df_geva))
if(input$FilterType=="OR"){
toDownload = allDF
}
if(input$FilterType=="AND"){
toDownload = allDF %>% dplyr::filter_at(vars(-c("physicalPos","rsid")),all_vars(!is.na(.)))
}
toDownload <- toDownload %>% dplyr::select('Position'=physicalPos, rsid, ends_with('_ihs'), ends_with('_nsl'), ends_with('_isafe'), 'Variant effect'=effect,'Impact'= impact, 'Gene Context'=gene_effect,'TF binding'=TFbinding,'DNase Peak'=DNasePeak,'Motif'=motif,'DNase Footprint'=DNaseFootprint,eQTL,'Matched TF Motif'=matchedTFmotif,'Matched DNase Footprint'=matchedDNaseFootprint,'Regulome Score'=ranking,
'Clinical Significance'=ClinicalSignificance,'Phenotype List'=PhenotypeList,
'Risk Allele'=risk_allele,'Allele freq.'= risk_allele_freq,'Trait'= trait,
'Accession'=accession,'context'=context, 'Number of associations'=NumGWASStudies,
'Disease'= diseaseName, 'Disease ID'= diseaseId, 'Disease type'= diseaseType,'Source'= source, DSI, DPI, 'Evidence Index'= EI, 'Pubmed ids'= NofPmids, starts_with('Age'))
set_download_names <- names(toDownload)
list(toDownload, set_download_names)
})
})
#############################################################
###################### PLOTS ###############################
#############################################################
############################ PLOTS SELECTION
# Structure boxes with plots
output$box_general <- renderUI({
div(
style = "position: relative; backgroundColor: #ecf0f5",
tabBox(
id = "box_general",
width = NULL,
height = NULL,
tabPanel(title = "iHS",
withSpinner(
plotlyOutput("plot_general_ihs"),
type = 4,
color = "#d33724",
size = 0.7
)
),
tabPanel(
title = "nSL",
withSpinner(
plotlyOutput("plot_general_nsl"),
type = 4,
color = "#d33724",
size = 0.7
)
)
)
)
})
output$box_bypop <- renderUI({
div(style = "position: relative; backgroundColor: #ecf0f5",
tabBox(
id = "box_bypop",
width = NULL,
height = returnHeigh(popSelect()) + 100,
tabPanel(
title = "iHS",
withSpinner(
plotlyOutput("plot_bypop_ihs", height = returnHeigh(popSelect())),
type = 4,
color = "#d33724",
size = 0.7
)
),
tabPanel(
title = "nSL",
withSpinner(
plotlyOutput("plot_bypop_nsl", height = returnHeigh(popSelect())),
type = 4,
color = "#d33724",
size = 0.7
)),
tabPanel(
title = "Table",
withSpinner(
dataTableOutput('table_selection'),
type = 4,
color = "#d33724",
size = 0.7
)
)
)
)
})
# Filter & plot
plot_general_ihs <- reactive({
input$UpdateButt
isolate({
# Generate data
tmp = set_filter_ihs()
toPlot = tmp[[1]]
toPlot_filter = tmp[[2]]
# Plot the data
# fig_iHS <- plotBoxPlot(toPlot, toPlot_filter, input$chrInput, input$extremeValuesInput_ihs, popSelect())
fig_iHS <- plotBoxPlot(toPlot, toPlot_filter, input$chrInput, input$extremeValuesInput_ihs, popSelect(),'iHS')
fig_iHS %>% layout(xaxis = list(title = ""),
yaxis = list (title = "iHS"))
})
})
plot_general_nsl <- reactive({
input$UpdateButt
isolate({
tmp = set_filter_nsl()
toPlot = tmp[[1]]
toPlot_filter = tmp[[2]]
fig_nSL <- plotBoxPlot(toPlot, toPlot_filter, input$chrInput, input$extremeValuesInput_nsl, popSelect(),'nSL')
fig_nSL %>% layout(xaxis = list(title = ""), yaxis = list (title = "nSL"))
})
})
plot_bypop_ihs <- reactive({
input$UpdateButt
isolate({
toPlot = set_filter_ihs()[[2]]
if(input$extremeValuesInput_ihs){
toShow <- plotScatterPlot(toPlot, 'iHS','thres_ad',unique(toPlot$POP))
}else{
toShow <- plotScatterPlot(toPlot, 'iHS','AD',unique(toPlot$POP))
}
toShow %>% layout(xaxis=list(range = c(input$minPosInput, input$maxPosInput)))
})
})
plot_bypop_nsl <- reactive({
input$UpdateButt
isolate({
toPlot = set_filter_nsl()[[2]]
if(input$extremeValuesInput_nsl){
toShow <- plotScatterPlot(toPlot, 'nSL','thres_ad',unique(toPlot$POP))
}else{
toShow <- plotScatterPlot(toPlot, 'nSL','AD',unique(toPlot$POP))
}
toShow %>% layout(xaxis=list(range = c(input$minPosInput, input$maxPosInput)))
})
})
# Call plot and output
output$plot_general_ihs <- renderPlotly({plot_general_ihs()})
output$plot_general_nsl <- renderPlotly({plot_general_nsl()})
output$plot_bypop_ihs <- renderPlotly({plot_bypop_ihs()})
output$plot_bypop_nsl <- renderPlotly({plot_bypop_nsl()})
#Table selection
output$table_selection <- renderDT(
set_selection_to_table(),
options = list(pageLength = 100,scrollX=TRUE)
)
############################ FAVORED MUTATION
output$box_isafe <- renderUI({
div(
style = "position: relative; backgroundColor: #ecf0f5",
tabBox(
id = "box_isafe",
width = NULL,
height = NULL,
tabPanel(
title = "iSAFE",
withSpinner(
plotlyOutput("plot_isafe_boxplot"),
type = 4,
color = "#d33724",
size = 0.7
)
)
)
)
})
output$scatter_isafe <- renderUI({
div(
style = "position: relative; backgroundColor: #ecf0f5",
tabBox(
id = "scatter_isafe",
width = NULL,
height = returnHeigh(popSelect()) + 100,
tabPanel(
title = "iSAFE",
withSpinner(
plotlyOutput('plot_isafe_scatter',height = returnHeigh(popSelect())),
type = 4,
color = "#d33724",
size = 0.7
)
),
tabPanel(
title = "Table",
withSpinner(
dataTableOutput('table_isafe'),
type = 4,
color = "#d33724",
size = 0.7
)
)
)
)
})
# Filter & plot
plot_isafe_boxplot <- reactive({
input$UpdateButt
isolate({
# Generate data
tmp = set_favallel()
toPlot = tmp[[1]] %>% as.data.table
toPlot_filter = tmp[[2]] %>% as.data.table
# toPlot_filter = toPlot_filter[value > 0.05]
# Plot the data
fig_isafe <- plotBoxPlot(toPlot, toPlot_filter, input$chrInput, input$extremeISAFE, popSelect(),'iSAFE')
fig_isafe %>% layout(xaxis = list(title = ""), yaxis = list (title = "iSAFE"))
})
})
# Plot by pop
plot_scatter_isafe <- reactive({
input$UpdateButt
isolate({
# Generate data
toPlot = set_favallel()[[2]] %>% dplyr::filter(value > 0.05) %>% as.data.table
fltPops = unique(toPlot$POP)
toPlot$POP <- factor(toPlot$POP, levels =popsData$pops[popsData$pops %in% fltPops])
if(input$extremeISAFE){
tmp <- data.table(
"POP" =c("Not extreme", "YRI", "LWK", "GWD", "MSL", "ESN", "ACB", "ASW","CEU", "TSI", "FIN", "GBR", "IBS","CHB", "JPT", "CHS", "CDX", "KHV","GIH", "PJL", "BEB", "STU", "ITU"),
"palName" =c("Not extreme","Top 0.01% YRI", "Top 0.01% LWK", "Top 0.01% GWD", "Top 0.01% MSL", "Top 0.01% ESN", "Top 0.01% ACB", "Top 0.01% ASW","Top 0.01% CEU", "Top 0.01% TSI", "Top 0.01% FIN", "Top 0.01% GBR", "Top 0.01% IBS","Top 0.01% CHB", "Top 0.01% JPT", "Top 0.01% CHS", "Top 0.01% CDX", "Top 0.01% KHV","Top 0.01% GIH", "Top 0.01% PJL", "Top 0.01% BEB", "Top 0.01% STU", "Top 0.01% ITU"),
"colors" =c("grey", "#F5F39F","#F0F56B","#F7F14A","#F5F287","#F5EC05","#FAFAC8" ,"#FAFABE","#2D74B2","#ACD1F2","#6FA6D6","#5691C4","#8AB9E3","#33B033","#6DD66D","#37B337","#008F00","#90DE90","#A965BA","#D5A3E3","#8E3EA3","#B79BBD","#C587D6")
)
column_color = 'thres_ad'
pal = c('grey',as.vector(tmp$colors[tmp$POP %in% fltPops]))
pal <- setNames(pal, c('Not extreme',as.vector(tmp$palName[tmp$POP %in% fltPops])))
}else{
column_color = 'POP'
pal = as.vector(popsData$colors[popsData$pops %in% fltPops])
}
# Plot the data
panel <- . %>% plot_ly(x = ~physicalPos, y= ~value) %>%
add_trace(type = 'scatter',
mode = 'markers',
color = ~get(column_color),
colors = pal,
text = ~paste0('<b>', POP,' → ', rsid,'<br></b>iSAFE: ',
ifelse(thres_ad != 'Not extreme', paste0(round(value,2),"<b> (!) TOP 0.01%</b>"),round(value,2))),
hoverinfo = 'text',
showlegend = F,
marker = list(size = 10,line = list(color = 'black',width = 1))) %>%
add_annotations(text = ~unique(POP),
x = 0.5,
y = 1,
yref = "paper",
xref = "paper",
yanchor = "bottom",
showarrow = FALSE,
font = list(size = 15) ) %>%
layout(showlegend = FALSE,
shapes = list(
list(type = "rect",
x0 = 0,
x1 = 1,
xref = "paper",
y0 = 0,
y1 = 16,
yanchor = 1,
yref = "paper",
ysizemode = "pixel",
fillcolor = toRGB("gray80"),
line = list(color = "transparent")
),
list(
type = "line",
x0 = 0,
x1 = 1,
xref = "paper",
y0 = ~unique(thres),
y1 = ~unique(thres),
line = list(color = "orange"),
width = 4,
dash = 'dot',
opacity = 0.4
)
),
xaxis = list(title = 'Position (bp)',
range = c(input$minPosInput, input$maxPosInput)),
yaxis = list(title = 'iSAFE',range=c(-0.05,max(toPlot$value)+0.1))
)
#Apply panel function to generate the plot
iSAFE_plot <- toPlot %>% group_by(POP) %>% do(p = panel(.)) %>% subplot(nrows = NROW(.), shareX = TRUE, shareY=TRUE, which_layout = 1, margin = 0.005,heights = returnHeighPop(fltPops))
iSAFE_plot
})
})
# Call plot and output
output$plot_isafe_boxplot <- renderPlotly({plot_isafe_boxplot()})
output$plot_isafe_histogram <- renderPlotly({plot_isafe_histogram()})
output$plot_isafe_scatter <- renderPlotly({plot_scatter_isafe()})
# Table isafe
output$table_isafe <- renderDT(
set_favallel()[[3]],
selection = 'none',
options = list(pageLength = 25,scrollX=TRUE)
)
############################ PLOTS FUNCT CHARACT
################### SNPEFF
# Structure boxes with plots
output$box_SNPEFF <- renderUI({
div(
style = "position: relative; backgroundColor: #ecf0f5",
tabBox(
id = "box_SNPEFF",
width = NULL,
height = 550,
tabPanel(
title = "SnpEff",
withSpinner(
plotlyOutput("plot_SNPEFF", height = 480),
type = 4,
color = "#d33724",
size = 0.7
)
),
tabPanel(
title = "Table",
withSpinner(
# table_isafe
dataTableOutput('table_snpeff'),
type = 4,
color = "#d33724",
size = 0.7
)
)
)
)
})
# Filter and plot
plot_SNPEFF <- reactive({
input$UpdateButt
isolate({
toPlot = set_snpeff()[[1]]
VEPmask_plot = set_snpeff()[[2]]
if(nrow(toPlot) < 1){
fig <- toPlot %>% plot_ly(x = ~physicalPos,