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server.R
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server.R
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# Author: Tokhir Dadaev
# License: MIT + file LICENSE.txt
# About -------------------------------------------------------------------
# Server file for shiny
# Define Server -----------------------------------------------------------
shinyServer(function(input, output, session) {
# Data level 1 - Raw Input ------------------------------------------------
datAssoc <- reactive({
req(input$dataType)
switch(input$dataType,
OncoArrayFineMapping = {
req(input$RegionID)
fread(paste0("Data/ProstateData/OncoArrayFineMapping/plotData/", input$RegionID, "_assoc.txt"),
header = TRUE, data.table = FALSE)},
# OncoArrayMeta = {
# fread(paste0("Data/ProstateData/OncoArrayMeta/plotData/", input$RegionID, "_assoc.txt"),
# header = TRUE, data.table = FALSE)},
Custom = {
#input file check
validate(need(input$FileStats != "", "Please upload Association file"))
inFile <- input$FileStats
req(inFile)
fread(inFile$datapath, header = TRUE, data.table = FALSE)
},
Example = {
fread("Data/CustomDataExample/Association.txt",
header = TRUE, data.table = FALSE)
})
}) # END datAssoc
datLD <- reactive({
req(input$dataType)
switch(input$dataType,
OncoArrayFineMapping = {
req(input$RegionID)
fread(paste0("Data/ProstateData/OncoArrayFineMapping/plotData/",input$RegionID,"_LD.txt"),
header = TRUE, data.table = FALSE)},
# OncoArrayMeta = {
# fread(paste0("Data/ProstateData/OncoArrayMeta/plotData/",input$RegionID,"_LD.txt"),
# header = TRUE, data.table = FALSE)},
Custom = {
#input file check
#validate(need(input$FileLD != "", "Please upload LD file"))
# If the LD file is missing then create a dummy LD input,
# with top SNP LD at 0.01
inFile <- input$FileLD
if(is.null(inFile)){
datAssoc() %>%
transmute(CHR_A = RegionChrN(),
BP_A = datAssoc() %>% arrange(P) %>% head(1) %>% .$BP,
SNP_A = datAssoc() %>% arrange(P) %>% head(1) %>% .$SNP,
CHR_B = CHR_A,
BP_B = BP,
SNP_B = SNP,
R2 = 0.01)
}else{fread(inFile$datapath, header = TRUE, data.table = FALSE) }
},
Example = {
fread("Data/CustomDataExample/LD.txt",
header = TRUE, data.table = FALSE)}
)# END switch
})# END datLD
datStats <- reactive({
req(input$dataType)
req(input$RegionID)
if(input$dataType == "OncoArrayFineMapping"){
d <- fread(paste0("Data/ProstateData/OncoArrayFineMapping/plotData/",
input$RegionID,"_stats.txt"))
# SNP,rsid,BP,PP_best_tag,PP_tag_r2,PostProb,BF,JAM99
# rs587636640,1:150283370:C:AA,150283370,1:150283370:C:AA,1,0.0129,2.601395291,0
d$Method <- "JAM"
d$Stats <- as.numeric(d$BF)
#return
d
} else {
datAssoc() %>% dplyr::filter(BP %in% unique(datLD()$BP_A)) %>%
transmute(SNP,
Method = "Forward regression",
BP,
Stats = P)
}
})
datAnnot <- reactive({
annotOncoFinemap
# req(input$dataType)
# switch(input$dataType,
# OncoArrayFineMapping = { annotOncoFinemap },
# # OncoArrayMeta = { NULL },
# Custom = { NULL },
# Example = { NULL })
}) # END datAnnot
datAnnotEQTL <- reactive({
annotOncoFinemapEQTL
# req(input$dataType)
# switch(input$dataType,
# OncoArrayFineMapping = { annotOncoFinemapEQTL },
# # OncoArrayMeta = { NULL },
# Custom = { NULL },
# Example = { NULL })
}) # END datAnnotEQTL
datLDlink <- reactive({
#input file check
validate(need(input$FileLDlink != "", "Please upload LDlink file"))
inFile <- input$FileLDlink
req(inFile)
#if(is.null(inFile)){return(NULL)}
fread(inFile$datapath, header = TRUE, data.table = FALSE)
})
datLDlinkProcess <- reactive({
datLDlink() %>%
## select only relevant columns
dplyr::select(SNP_B = RS_Number, Coord, R2) %>%
## add index SNP for LD comparison
mutate(
SNP_B = ifelse(SNP_B == ".", Coord, SNP_B),
SNP_A = SNP_B[c(1)]) %>%
## separate 'Coord' into CHR_B and BP_B
separate(Coord, into = c("CHR_B", "BP_B"), sep = ":") %>%
mutate(CHR_B = gsub("chr", "", x = CHR_B),
## add BP for index SNP
CHR_A = CHR_B[c(1)],
BP_A = BP_B[c(1)]) %>%
## Reorder columns
dplyr::select(c(6, 7, 5, 2, 3, 1, 4)) %>%
arrange(BP_B)})
# Define ROI --------------------------------------------------------------
RegionFlank <- reactive({ req(input$Flank)
max(c(1, input$Flank * 1000)) })
RegionChr <- reactive({
req(datAssoc())
datAssoc()$CHR[1]
})
RegionChrN <- reactive({
req(RegionChr())
x <- gsub("chr", "", RegionChr(), fixed = TRUE)
if(x == "X") x <- "23"
as.numeric(x)
})
RegionStart <- reactive({
req(datAssoc())
req(RegionChr())
x <- datAssoc() %>%
dplyr::filter(CHR == RegionChr() & !is.na(BP)) %>%
.$BP %>% min
#round down to 10K bp
as.integer(max(0, round((x - RegionFlank())/RegionFlank()) * RegionFlank()))
})
RegionEnd <- reactive({
req(datAssoc())
req(RegionChr())
x <- datAssoc() %>%
dplyr::filter(CHR == RegionChr() & !is.na(BP)) %>%
.$BP %>% max
#round up to 10K bp
as.integer(round((x + RegionFlank())/RegionFlank()) * RegionFlank())
})
RegionHits <- reactive({
req(datLD())
d <- datLD() %>% .$SNP_A %>% unique %>% sort
d[1:min(length(d), 150)]
})
RegionHitsSelected <- reactive({
x <- unique(c(input$HitSNPs, input$otherHits))
if(is.null(x)) x <- ""
x
})
#Genomic ranges to subset bigwig data - wgEncodeBroadHistone
RegionGR <-
reactive({
GRanges(seqnames = RegionChr(),
IRanges(start = RegionStart(),
end = RegionEnd()))
})
# # Data level 2 - ROI ------------------------------------------------------
ROIdatAssoc <- reactive({
req(datAssoc())
datAssoc() %>%
dplyr::filter(BP >= RegionStart() &
BP <= RegionEnd()) %>%
mutate(PLog = -log10(P)) })
ROIdatLD <- reactive({
req(datLD())
datLD() %>%
dplyr::filter(CHR_B == RegionChrN() &
BP_B >= RegionStart() & BP_B <= RegionEnd()) })
# ROIdatBedGraph <- reactive({
# datBedGraph() %>%
# dplyr::filter(CHR == RegionChr()) %>%
# #scale to -1 and 1,
# mutate(SCORE = SCORE/max(abs(SCORE), na.rm = TRUE))
# })
ROIdatAnnot <- reactive({
req(datAnnot())
datAnnot() %>%
dplyr::filter(CHR == RegionChrN() &
BP >= RegionStart() & BP <= RegionEnd())
})
ROIdatAnnotEQTL <- reactive({
req(datAnnotEQTL())
datAnnotEQTL() %>%
dplyr::filter(CHR == RegionChrN() &
(
(SNPhit_BP >= RegionStart() & SNPhit_BP <= RegionEnd()) |
(SNP_BP >= RegionStart() & SNP_BP <= RegionEnd())
)
)
})
ROIPLogMax <- reactive({
#ylim Max for plotting Manhattan
maxY <- max(ROIdatAssoc()$PLog, na.rm = TRUE)
max(10, ceiling((maxY+1)/5)*5)
})
ROIdatGeneticMap <- reactive({
req(GeneticMap1KG)
GeneticMap1KG[ CHR == RegionChrN() & BP >= RegionStart() & BP <= RegionEnd()]
})
# Define Zoom Start End ---------------------------------------------------
zoomStart <- reactive({ input$BPrange[1] })
zoomEnd <- reactive({ input$BPrange[2] })
# Data level 3 - Plot data ------------------------------------------------
plotDatAssoc <- reactive({
d <- ROIdatAssoc()
d <- d[ d$PLog >= input$FilterMinPlog, ]
d[ order(d$BP), ]
})
plotDatLD <- reactive({
#subset LD based on ui input
LD <- ROIdatLD() %>%
dplyr::filter(
R2 >= input$FilterMinLD &
SNP_A %in% RegionHitsSelected())})
#subset of recombination rates for zoomed region
plotDatGeneticMap <- reactive({ ROIdatGeneticMap() %>%
dplyr::filter(between(BP, zoomStart(), zoomEnd())) })
#get granges collapsed genes for ggplot+ggbio
plotDatGene <- reactive({
GeneSymbol(chrom = RegionChr(),
chromStart = zoomStart(),
chromEnd = zoomEnd()) })
#number of genes in zoomed region
# if not genes, set to 1, to plot a message: no genes.
plotDatGeneN <- reactive({
res <- try({
length(unique(plotDatGene()@elementMetadata$gene_id))}, silent = TRUE)
if(class(res) == "try-error"){res <- 1}
#return
res })
# OncoArray finemapping Annotaion and TCGA EQTL
plotDatAnnot <- reactive({ annotOncoFinemap[ CHR == RegionChrN(), ] })
plotDatAnnotEQTL <- reactive({
# c(CHR, BP, TYPE1, TYPE2, COLOUR_HEX, TYPE2N)
annotOncoFinemapEQTL %>%
filter(CHR == RegionChrN() &
between(SNP_BP, zoomStart(), zoomEnd())) %>%
#filter(SNPhit == SNP) %>%
#filter(SNP_BP %in% datLD()[datLD()$SNP_A %in% RegionHitsSelected(), "BP_A"]) %>%
transmute(CHR,
BP = SNP_BP,
TYPE1 = "EQTL",
TYPE2 = GENE,
COLOUR_HEX = "#F00034", #ICRcolours("BrighRed")
TYPE2N = 4) %>%
unique()
})
# Output ------------------------------------------------------------------
# Output Summary ----------------------------------------------------------
output$SummaryStats <- DT::renderDataTable({
datAssoc() %>% arrange(P) %>%
#if SNP name has rs number then convert to a link to NCBI
mutate(SNP = hyperlink(SNP))},
# FALSE to parse as a link
escape = FALSE)
output$SummaryHitSNPStats <- DT::renderDataTable({
datStats() %>%
dplyr::select(SNP, Method, BP, Stats) %>%
arrange(Method, BP) %>%
mutate(
# to output Inf as "Inf" convert to character, relevant GitHub issue:
# https://github.com/jeroen/jsonlite/issues/94
# https://stackoverflow.com/q/38771807/680068
Stats = as.character(Stats),
# output SNP names as links to NCBI
SNP = hyperlink(SNP))
},
# FALSE to parse as a link
escape = FALSE)
output$SummaryLD <- DT::renderDataTable({
req(input$dataType)
datLD() %>%
arrange(BP_B) %>%
mutate(SNP_A = hyperlink(SNP_A),
SNP_B = hyperlink(SNP_B))
}, escape = FALSE)
output$SummaryROIdatAnnot <- DT::renderDataTable({
merge(datAssoc()[, c("SNP", "BP")], ROIdatAnnot(), by = "BP") %>%
transmute(SNP = hyperlink(SNP),
BP,
TYPE1, TYPE2) %>%
arrange(BP)
}, escape = FALSE)
output$SummaryROIdatAnnotEQTL <- DT::renderDataTable({
ROIdatAnnotEQTL() %>%
mutate(SNPhit = hyperlink(SNPhit),
SNP = hyperlink(SNP),
R2,
GENE = hyperlink(GENE, type = "geneSymbol"))
}, escape = FALSE)
# output$SummaryBedGraph <-
# DT::renderDataTable({datBedGraph() %>% arrange(START)},
# options = list(searching = FALSE, searchable = FALSE))
output$ui_SummaryRegion <-
renderUI(a(paste0(RegionChr(),':',RegionStart(),'-',RegionEnd()),
href=paste0('http://genome-euro.ucsc.edu/cgi-bin/hgTracks?db=hg19&position=',
RegionChr(),'%3A',RegionStart(),'-',RegionEnd()),target="_blank"))
# Output: LD Link -----------------------------------------------------------
output$SummaryLDlink <- DT::renderDataTable( datLDlink() )
output$SummaryLDlinkProcess <- DT::renderDataTable( datLDlinkProcess() )
output$downloadLDFile <- downloadHandler(
filename = function() { paste0(
tools::file_path_sans_ext(input$FileLDlink),
"_LD.txt") },
content = function(file) {
write.table(datLDlinkProcess(), file,
sep = " ", row.names = FALSE, quote = FALSE)
})
# ~~~ TAB: Manhattan --------------------------------------------------------
# Plot: Title ---------------------------------------------------------------
output$ui_plotTitle <-
renderUI(a(paste0(RegionChr(),':',zoomStart(),'-',zoomEnd()),
href=paste0('http://genome-euro.ucsc.edu/cgi-bin/hgTracks?db=hg19&position=',
RegionChr(),'%3A',zoomStart(),'-',zoomEnd()), target="_blank"))
# Plot: Chr ideogram --------------------------------------------------------
plotObjChromosome <- reactive({
Ideogram(genome = "hg19",
subchr = RegionChr(),
#highlight ROI - region
color = "#F00034", fill = "#F00034",
zoom.region = c(RegionStart(), RegionEnd())) +
ylab(label = element_blank())
})
output$PlotChromosome <- renderPlot({print(plotObjChromosome())})
# Plot: Manhattan Pvalues ----------------------------------------------------
plotObjManhattanPvalues <- reactive({
# Create a Progress object
progress <- shiny::Progress$new()
progress$set(message = "Plotting please wait...", value = 0)
# Close the progress when this reactive exits (even if there's an error)
on.exit(progress$close())
plotHits <- plotDatAssoc()[ plotDatAssoc()$SNP %in% RegionHitsSelected(), "SNP" ]
plotManhattan(assoc = plotDatAssoc(),
LD = plotDatLD(),
geneticMap = plotDatGeneticMap(),
suggestiveLine = input$suggestiveLine,
genomewideLine = input$genomewideLine,
hits = plotHits,
xStart = zoomStart(),
xEnd = zoomEnd(),
opts = intersect(
c("Hits", "LD", "SuggestiveLine", "GenomewideLine",
input$ShowHideManhattanPvalues),
c("Recombination", "LDSmooth",
"Hits", "LD", "SuggestiveLine", "GenomewideLine", "Effect")),
pad = TRUE,
postprob = FALSE) +
theme_LE()
})
output$PlotManhattanPvalues <- renderPlot({
# Create a Progress object
progress <- shiny::Progress$new()
progress$set(message = "Almost there...", value = 80)
# Close the progress when this reactive exits (even if there's an error)
on.exit(progress$close())
print(plotObjManhattanPvalues())})
# Plot: Manhattan PostProbs --------------------------------------------------
plotObjManhattanPostProbs <- reactive({
# Create a Progress object
progress <- shiny::Progress$new()
progress$set(message = "Plotting please wait...", value = 0)
# Close the progress when this reactive exits (even if there's an error)
on.exit(progress$close())
# c("SNP","BP","P","TYPED")
datStats <- datStats()
datStats$TYPED <- ifelse(datStats$SNP %in% datAssoc()[datAssoc()$TYPED == 2, "SNP"], 2, 1)
plotDat <- data.frame(SNP = datStats$SNP,
BP = datStats$BP,
P = datStats$PostProb,
TYPED = datStats$TYPED,
BF = datStats$BF)
plotDat <- plotDat[ order(plotDat$BP), ]
# add BF to snp names, e.g: rs1234 (12.34)
plotHits <- plotDat[ plotDat$SNP %in% RegionHitsSelected(), "SNP" ]
plotHitsName <- paste0(plotHits, " (",
formatC(plotDat[ plotDat$SNP %in% RegionHitsSelected(), "BF"],
digits = 2, format = "f"),
")")
# x <- c(0.1111, -Inf, Inf, 23439924.22222, -0.00000001, 0.000002)
# format(round(x, 2), nsmall = 2)
# formatC(x, digits = 2, format = "f")
plotManhattan(assoc = plotDat,
LD = plotDatLD(),
geneticMap = plotDatGeneticMap(),
suggestiveLine = 0,
genomewideLine = 0,
hits = plotHits,
hitsName = plotHitsName,
xStart = zoomStart(),
xEnd = zoomEnd(),
opts = intersect(
c("Hits", "LD", "SuggestiveLine", "GenomewideLine",
input$ShowHideManhattanPostProbs),
c("Recombination", "LDSmooth",
"Hits", "LD", "SuggestiveLine", "GenomewideLine")),
pad = TRUE,
postprob = TRUE) +
theme_LE()
})
output$PlotManhattanPostProbs <- renderPlot({
# Create a Progress object
progress <- shiny::Progress$new()
progress$set(message = "Almost there...", value = 80)
# Close the progress when this reactive exits (even if there's an error)
on.exit(progress$close())
print(plotObjManhattanPostProbs())})
# Plot: SNPType -------------------------------------------------------------
# SNP is imputed or typed
plotObjSNPType <- reactive({
plotSNPtype(assoc = plotDatAssoc(),
xStart = zoomStart(),
xEnd = zoomEnd(),
pad = TRUE) +
theme_LE()
})
output$PlotSNPType <- renderPlot({print(plotObjSNPType())})
# Plot: SNP LD --------------------------------------------------------------
plotObjSNPLD <- reactive({
plotLD(data = plotDatLD(),
hits = RegionHitsSelected(),
xStart = zoomStart(),
xEnd = zoomEnd(),
pad = TRUE) +
theme_LE()
})
output$PlotSNPLD <- renderPlot({print(plotObjSNPLD())})
# Plot: wgEncodeBroadHistone 7 bigwig ---------------------------------------
plotObjwgEncodeBroadHistone <- reactive({
gg <- try({
plotHistone(folder = "Data/wgEncodeBroadHistone/",
chr = RegionChr(),
xStart = zoomStart(),
xEnd = zoomEnd(),
pad = TRUE
) + theme_LE()}, silent = TRUE)
if(class(gg) == "try-error"){
gg <- plotBlank(zoomStart(), zoomEnd(),
yLabel = expression(ENCODE[]),
textAnnot = "Error: Histone bigwig files are missing.") +
theme_LE() #+
#ylab(expression(ENCODE[]))
}
#return
gg
})
output$PlotwgEncodeBroadHistone <- renderPlot({print(plotObjwgEncodeBroadHistone())})
# Plot: BedGraph ------------------------------------------------------------
# plotObjBedGraph <- reactive({
# plotBlank(xStart = 1, xEnd = 10, yLabel = "test") + theme_LE()
#
# })
# output$PlotBedGraph <- renderPlot({print(plotObjBedGraph())})
# Plot: Annot & eQTL OncoFinemap -------------------------------------------
plotObjAnnotOncoFinemap <- reactive({
plotDat <- rbind(plotDatAnnot(), plotDatAnnotEQTL())
gg <- plotAnnot(plotDat,
chrom = RegionChrN(),
xStart = zoomStart(), xEnd = zoomEnd(),
collapse = TRUE) +
theme_LE()
# add gene names if there eqtl
# if(nrow(plotDatAnnotEQTL()) > 0) {
# gg <- gg +
# geom_text_repel(data = plotDatAnnotEQTL(),
# aes(x = BP, y = 4, label = TYPE2), col = "black")}
#return ggplot
gg
})
output$PlotAnnotOncoFinemap <- renderPlot({print(plotObjAnnotOncoFinemap())})
# Plot: Gene ---------------------------------------------------------------
# returns ggplot and count of genes
plotObjGene <- reactive({
plotGene(chrom = ifelse(RegionChr() == "chr23", "chrX", RegionChr()),
chromStart = zoomStart(), chromEnd = zoomEnd(),
hits = unique(plotDatAnnotEQTL()$TYPE2),
vline = unique(plotDatLD()$BP_A),
pad = TRUE)})
# ggplot object to plot gene track
plotObjGenePlot <- reactive({
plotObjGene()$genePlot + theme_LE()})
# numeric count of genes, passed for merging tracks, more genes vertical space.
# used for cowplot::plot_grid(), rel_heights
# plotObjGene()$geneCnt
output$PlotGene <- renderPlot({print(plotObjGenePlot())})
# ~~~ TAB: LD-Heatmap ------------------------------------------------------
plotObjSNP_LDheatmap <- reactive({
plotLDmatrix(data = plotDatLD(), hits = RegionHitsSelected())
})
output$plotSNP_LDheatmap <- renderPlot({print(plotObjSNP_LDheatmap())})
# ~~~ TAB: LD-Network -----------------------------------------------------
# 1. Nodes ------------------------------------------------------------------
nodes <- reactive({
nodeHits <- data.frame(id = RegionHitsSelected(),
label = RegionHitsSelected(),
group = "Hits")
nodeTags <-
rbind.data.frame(
plotDatLD() %>%
transmute(
id = SNP_A,
label = SNP_A,
group = "Tags"),
plotDatLD() %>%
transmute(
id = SNP_B,
label = SNP_B,
group = "Tags")) %>%
filter(!(id %in% nodeHits$id)) %>%
unique
# %>%
# filter(
# # filter on BF
# id %in% metaRegionClean[ MaxBF > input$filterMinBF, SNP]
# )
#merge hit nodes and other tag nodes
if(nrow(nodeTags) > 0) {
res <- rbind.data.frame(nodeHits, nodeTags)} else {
res <- nodeHits}
#keep only hits?
if(input$hitsOnly){
res <- res %>%
filter(id %in% RegionHitsSelected())
}
# remove nodes that have no links
res <- res %>%
filter(id %in% RegionHitsSelected() |
id %in% c(links()$from, links()$to))
#add meta info: pvalue, infoscore, allele, maf
# res <- merge(res,
# metaRegionClean[, list(SNPid, MaxBF, title)],
# by = "SNPid", all.x = TRUE)
# # set the size of nodes
# if(input$nodeSizeBF){
# res <- res %>%
# mutate(size = as.numeric(
# as.character(cut(MaxBF,
# breaks = c(-1, 0, 1, 3, 5, 10, 100, Inf),
# labels = c(1, 4, 10, 12, 15, 20, 25)))))
# #return
# res %>%
# arrange(group) %>%
# select(id, label, group, size, title) %>% unique
# } else {
# #return
# res %>%
# arrange(group) %>%
# select(id, label, group, title) %>% unique
#
# }
#return
res %>%
arrange(group) %>%
dplyr::select(id, label, group) %>%
unique
})
# 2. Links ---------------------------------------------------------------------
links <- reactive({
if(input$hitsOnly){
res <-
plotDatLD() %>%
transmute(from = SNP_A,
to = SNP_B,
value = R2)%>%
filter(from != to) %>%
filter(from %in% RegionHitsSelected() &
to %in% RegionHitsSelected())
} else {
res <-
plotDatLD() %>%
transmute(from = SNP_A,
to = SNP_B,
value = R2) %>%
filter(from != to &
from %in% RegionHitsSelected())
}
#res$color <- if_else(res$value < 0.1, "gray", "lightblue")
res$color <- "lightblue"
#res$value <- round(res$value, 2) * 100
res$title <- paste0("R2:", round(res$value, 2))
res %>%
filter(value >= input$FilterMinLD)
})
# 3. networkHits ----------------------------------------------------------
output$plotSNP_LDnetwork <- renderVisNetwork({
visNetwork(nodes(), links(),
main = "LD network",
submain = paste0("Filter R2>", input$FilterMinLD)
) %>%
visOptions(highlightNearest = TRUE) %>%
visLegend(useGroups = FALSE,
addNodes = data.frame(label = c("Hit SNPs", "Tag SNPs"),
shape = c("circle", "circle"),
color = c("#97C2FC", "yellow")),
addEdges = data.frame(label = "LD",
color = "lightblue",
arrows = "none", width = 2),
zoom = FALSE) %>%
visInteraction(navigationButtons = TRUE) %>%
visIgraphLayout(layout = input$networkLayout, randomSeed = 12) %>%
visExport(name = paste(RegionChr(), zoomStart(), zoomEnd(), "network", sep = "_"),
label = "Save as PNG",
style = "background-color:#85E7FF")
})
# Dynamic UI --------------------------------------------------------------
output$ui_refProstatePaper <- renderUI(
if(input$dataType %in% c("OncoArrayFineMapping",
"OncoArrayMeta","iCOGS")){
includeMarkdown(
paste0("Data/ProstateData/", input$dataType, "/README.md"))
} else if(input$dataType == "Custom") {
includeMarkdown("Data/CustomDataExample/README.md")
}
)
#Zoom to region X axis BP
output$ui_BPrange <-
renderUI({
req(RegionStart())
req(RegionEnd())
sliderInput("BPrange", h5("Use sliders to zoom in to required region."),
min = RegionStart(),
max = RegionEnd(),
value = c(RegionStart(),RegionEnd()),
step = 20000)})
#Select hit SNPs
output$ui_HitSNPs <-
renderUI({
list(tags$div(align = 'left',
class = 'multicol',
checkboxGroupInput(inputId = 'HitSNPs',
label = "Hit SNPs:",
choices = RegionHits(),
selected =
if(input$dataType == "OncoArrayFineMapping") {
datStats() %>% dplyr::filter(JAM99 == 1) %>% .$SNP
# SNP,rsid,BP,PP_best_tag,PP_tag_r2,PostProb,BF,JAM99
# rs6724057,chr2_242113751_A_G,242113751,chr2_242113751_A_G,1,0.0507,25.58232942,0
# rs77559646,chr2_242135265_A_G,242135265,chr2_242135265_A_G,1,0.9998,Inf,1
} else {
RegionHits()[1:min(c(5, length(RegionHits())))]},
inline = FALSE)))
# checkboxGroupInput("HitSNPs", h4("Hit SNPs:"),
# RegionHits(),
# #select max of 5 SNPs
# selected = RegionHits()[1:min(c(5, length(RegionHits())))],
# inline = TRUE
#) #END checkboxGroupInput
})
output$ui_otherHits <- renderUI({
selectizeInput("otherHits", "Other SNPs:",
choices = plotDatAssoc()$SNP,
multiple = TRUE)
})
output$ui_Chr <- renderUI({
selectInput(inputId = "Chr", label = h5("Chr"),
choices = regions %>%
dplyr::filter(DATA == input$dataType) %>%
.$CHR %>% unique,
selected = "chr2"
)
})
output$ui_RegionID <- renderUI({
req(input$Chr)
req(input$dataType)
selectInput(inputId = "RegionID", label= h5("Region ID"),
choices = regions %>%
dplyr::filter(CHR == input$Chr &
DATA == input$dataType) %>%
.$REGIONBED,
#pre selected a region to demonstrate as an example - HNF1B gene
selected = "chr2_241657087_242920971"
)})
#download file name - default: chr_start_end
output$ui_downloadPlotFileName <- renderUI({
textInput(
inputId = "downloadPlotFileName",
label = "Download file name",
value = paste(RegionChr(), zoomStart(), zoomEnd(), sep = "_"))})
#download plot title - default: chr:start-end
output$ui_downloadPlotTitle <- renderUI({
textInput(
inputId = "downloadPlotTitle",
label = "Plot title",
value = paste0(RegionChr(), ':', zoomStart(), '-', zoomEnd(), sep=""))})
output$ui_PlotThemeColour2 <- renderUI({
req(input$PlotThemeColour1)
x <- input$PlotThemeColour1
colourInput("PlotThemeColour2",
"Plot theme shade 2",
#"#E5E5E5"
shadeTintColour(input$PlotThemeColour1, 10)
)})
# Merged Final plot -------------------------------------------------------
#merged plot with dynamic plot height
plotList <- reactive({
plots <- list(
if("Chromosome" %in% input$ShowHideTracks) plotObjChromosome() else NA,
#scale_y_continuous(expand = c(0.05, 0))
if("Manhattan" %in% input$ShowHideManhattanPvalues) plotObjManhattanPvalues() else NA,
if("Manhattan" %in% input$ShowHideManhattanPostProbs) plotObjManhattanPostProbs() else NA,
if("SNPType" %in% input$ShowHideTracks) plotObjSNPType() +
scale_y_continuous(breaks = c(0.5, 1.5),
labels = strPadLeft(c("Imputed", "Typed")),
limits = c(-0.90, 2),
name = expression(SNP[])) else NA,
if("LD" %in% input$ShowHideTracks) plotObjSNPLD() else NA,
if("wgEncodeBroadHistone" %in% input$ShowHideTracks) plotObjwgEncodeBroadHistone() else NA,
if("annotOncoFinemap" %in% input$ShowHideTracks) plotObjAnnotOncoFinemap() else NA,
if("Gene" %in% input$ShowHideTracks) plotObjGenePlot() else NA
)
trackHeights <- c(
100, # Chromosome
300, # ManhattanPvalues
120, # ManhattanPostProbs
50, # SNPType
20 * length(input$HitSNPs), # LD
60, # wgEncodeBroadHistone
80, # annotOncoFinemap,
min(c(240, 30 * plotObjGene()$geneCnt)) # Gene
)
ixKeep <- !is.na(plots)
# return list of plots and heights
list(Tracks = plots[ ixKeep ],
Heights = trackHeights[ ixKeep ])
})
trackColours <- reactive({
myCols <-
if(input$PlotTheme == "1"){
c(input$PlotThemeColour1, input$PlotThemeColour2)
} else { c("#FFFFFF", "#FFFFFF") }
res <- cbind(plotList()$Heights, myCols)[, 2][ 1:length(plotList()$Heights) ]
#chromosome background must be white
if("Chromosome" %in% input$ShowHideTracks){res <- head(c("white", res), -1)}
return(res)
})
plotObjMerge <- reactive({
# Create a Progress object
progress <- shiny::Progress$new()
progress$set(message = "Plotting please wait...", value = 0)
# Close the progress when this reactive exits (even if there's an error)
on.exit(progress$close())
tracks(
plotList()$Tracks,
heights = plotList()$Heights,
padding = unit(-0.2, "lines"),
track.plot.color = trackColours(),
title = if(!is.null(input$downloadPlotTitle) &
nchar(input$downloadPlotTitle) > 0) { input$downloadPlotTitle
} else { NULL }
) +
if(input$PlotTheme == "2") {
theme(panel.border = element_rect(fill = NA, color = 'grey80')) }
})
output$plotMerge <- renderPlot({
# Create a Progress object
progress <- shiny::Progress$new()
progress$set(message = "Almost there...", value = 80)
# Close the progress when this reactive exits (even if there's an error)
on.exit(progress$close())
print(plotObjMerge())
})
#plot merge height is dynamic, based on seleceted tracks
output$ui_plotMergeUI <- renderUI({
plotOutput("plotMerge", width = 800, height = sum(plotList()$Heights))
#plotOutput("plotMerge", width = 800, height = 1000)
})
# Output to a file --------------------------------------------------------
# Get the selected download file type.
downloadPlotType <- reactive({input$downloadPlotType})
observe({
plotType <- input$downloadPlotType
plotTypePDF <- plotType %in% c("pdf","svg")
plotUnit <- ifelse(plotTypePDF, "inches", "pixels")
plotUnitDefHeight <- ifelse(plotTypePDF, 12, 1200)
plotUnitDefWidth <- ifelse(plotTypePDF, 10, 1000)
#plotUnitDefMin <- ifelse(plotTypePDF, 5, 800)
#plotUnitDefMax <- ifelse(plotTypePDF, 50, 10000)
plotUnitDefStep <- ifelse(plotTypePDF, 1, 100)
updateSliderInput( session,
inputId = "downloadPlotHeight",
label = sprintf("Height (%s)", plotUnit),
value = plotUnitDefHeight,
step = plotUnitDefStep)
updateSliderInput(
session,
inputId = "downloadPlotWidth",
label = sprintf("Width (%s)", plotUnit),
value = plotUnitDefWidth,
step = plotUnitDefStep)
})
downloadPlotHeight <- reactive({ input$downloadPlotHeight })
downloadPlotWidth <- reactive({ input$downloadPlotWidth })
downloadPlotFileName <- reactive({ input$downloadPlotFileName })
downloadPlotRes <- reactive({ input$downloadPlotRes })
downloadPlotPaper <- reactive({ input$downloadPlotPaper })
downloadPlotType <- reactive({ input$downloadPlotType })
downloadPlotPointSize <- reactive({ input$downloadPlotPointSize })
downloadPlotQuality <- reactive({ input$downloadPlotQuality })
downloadPlotTypeJPEG <- reactive({ input$downloadPlotTypeJPEG })
downloadPlotTypeCompression <- reactive({ input$downloadPlotCompression })
output$downloadPlot <- downloadHandler(
filename = function() {
paste(downloadPlotFileName(), downloadPlotType(), sep = ".")
},
# The argument content below takes filename as a function
# and returns what's printed to it.
content = function(con) {
# Gets the name of the function to use from the
# downloadFileType reactive element. Example:
# returns function pdf() if downloadFileType == "pdf".
plotFunction <- match.fun(downloadPlotType())
switch(input$downloadPlotType,
pdf = {plotFunction(con,
width = downloadPlotWidth(),
height = downloadPlotHeight(),
pointsize = downloadPlotPointSize(),
paper = downloadPlotPaper(),
useDingbats = FALSE)},
svg = {plotFunction(con,
width = downloadPlotWidth(),
height = downloadPlotHeight(),
pointsize = downloadPlotPointSize())},
jpeg = {plotFunction(con,
width = downloadPlotWidth(),
height = downloadPlotHeight(),
pointsize = downloadPlotPointSize(),
res = downloadPlotRes(),
quality = downloadPlotQuality(),
type = downloadPlotTypeJPEG())},
tiff = {plotFunction(con,
width = downloadPlotWidth(),
height = downloadPlotHeight(),
pointsize = downloadPlotPointSize(),
res = downloadPlotRes(),
type = downloadPlotTypeJPEG(),
compression = downloadPlotTypeCompression())}
)
print(plotObjMerge())
dev.off(which=dev.cur())
}) #downloadHandler downloadPlot
# Observe update ----------------------------------------------------------
# maximum of 5 SNPs can be selected to display LD, minimum 1 must be ticked.
observe({
# if(length(input$HitSNPs) < 1){
# updateCheckboxGroupInput(
# session, "HitSNPs",
# #selected = RegionHits()[1:min(5, length(RegionHits()))]
# selected = RegionHits()[1]
# )}
if(length(c(input$ShowHideTracks,