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Copy pathserver.R
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server.R
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#dependencies
library(shiny)
library(shinyBS)
library(RColorBrewer)
library(DT)
#sqlite libraries
library(DBI)
library(RSQLite)
#CircosDiagram
library(RCircos)
#load utility scripts
source("utility/read_vcf_for_sample.R")
source("utility/readConfig.R")
#get database from config file
databases <- config::get("SQLiteDB")
#create handle to sqlite file
dbhandle <- dbConnect(RSQLite::SQLite(), databases[1])
#established p and q boundaries
pq <- read.table(pqDataFilePath, sep="\t", stringsAsFactors = FALSE,
header = TRUE)
#read in the chromosome sizes for a genome of interest
chrom_sizes <- read.table(chromSizesFilePath, sep = "\t",
stringsAsFactors = FALSE, header = FALSE)
chrom_sizes <- c(chrom_sizes$V2, 0)
#cumulative sums of the chromosome sizes
cumulative_chrom_sizes <- cumsum(chrom_sizes)
cumulative_chrom_sizes <- c(0, cumulative_chrom_sizes)
cumulative_chrom_sizes <- cumulative_chrom_sizes[1:25]
#read groups from database
query <- "SELECT chrom, start, end, id from GENOM_BEDFILES where type = 'groups'"
genome_bed <- dbGetQuery(dbhandle, query, stringsAsFactors = FALSE)
#remove chrom prefix
genome_bed$chrom <- gsub("chr", "", genome_bed$chrom)
#genes in the blacklist bed file will be hidden by default
query <- "SELECT chrom, start, end, id from GENOM_BEDFILES "
query <- paste0(query, "where type = 'blacklist'")
blacklist <- dbGetQuery(dbhandle, query, stringsAsFactors = F)
if (nrow(blacklist) == 0) {
blacklist <- data.frame(V1 = "N", V2 = -1, V3 = -1, stringsAsFactors = FALSE)
} else {
colnames(blacklist) <- c("V1","V2","V3","V4")
}
blacklist$V1[blacklist$V1 %in% "X"] <- "23"
blacklist$V1[blacklist$V1 %in% "Y"] <- "24"
# Define server logic for application
shinyServer(function(input, output, session) {
#hide the progress indicator if the user requests
observeEvent(input$noprogress, {
if(input$noprogress) {
js$progressToggle(1)
} else{
js$progressToggle(0)
}
})
#when the database changes redraw
observeEvent(input$database, {
dbDisconnect(dbhandle)
dbhandle <<- dbConnect(RSQLite::SQLite(), input$database)
res = dbGetQuery(dbhandle, "SELECT sample_id from GENOM_SAMPLE")
#update sample list
updateSelectInput(session, "sample", label="Sample:",
choices = res$sample_id, selected = res$sample_id[1])
#update gene groups
query <- "SELECT chrom, start, end, id from GENOM_BEDFILES "
query <- paste0(query, "where type = 'groups'")
genome_bed <<- dbGetQuery(dbhandle, query, stringsAsFactors = FALSE)
#remove chrom prefix
genome_bed$chrom <<- gsub("chr", "", genome_bed$chrom)
#reload db
query <- "SELECT chrom, start, end, id from GENOM_BEDFILES "
query <- paste0(query, "where type = 'blacklist'")
blacklist <<- dbGetQuery(dbhandle, query, stringsAsFactors = F)
if (nrow(blacklist) == 0) {
blacklist <<- data.frame(V1 = "N", V2 = -1, V3 = -1, stringsAsFactors = FALSE)
} else {
colnames(blacklist) <<- c("V1","V2","V3","V4")
}
blacklist$V1[blacklist$V1 %in% "X"] <- "23"
blacklist$V1[blacklist$V1 %in% "Y"] <- "24"
})
#returns all input chromosomes for normalization
get_chrom_normalization_input <- function() {
if (!input$chromnorm) {
1:22
} else {
ids <- paste0("chrom", 1:22)
x <- sapply(ids, function(x) input[[x]])
x <- ids[which(x == TRUE)]
x <- as.numeric(gsub("chrom", "", x))
if (length(x) == 0) {
1:22
} else {
x
}
}
}
# generate a plot of copy number data.
draw_copy_number_plot <- function() {
#load the data
query <- "SELECT chrom, start, end, id, normal_mean, tumor_mean, "
query <- paste0(query, "tumor_depth from GENOM_DEPTH where sample_id = '")
query <- paste0(query, input$sample,"'")
genome_depth <- dbGetQuery(dbhandle, query, stringsAsFactors = FALSE)
#cast to proper data types
genome_depth$start <- as.numeric(genome_depth$start)
genome_depth$end <- as.numeric(genome_depth$end)
genome_depth$normal_mean <- as.numeric(genome_depth$normal_mean)
genome_depth$tumor_mean <- as.numeric(genome_depth$tumor_mean)
#if the chr prefix is on chrom, remove it
genome_depth$chrom <- gsub("chr", "", genome_depth$chrom)
#remove the sex chromosomes and where the mean == 0
genome_depth <- genome_depth[genome_depth$tumor_mean != 0, ]
if(!input$hideXY) {
genome_depth$chrom[genome_depth$chrom %in% "X"] <- "23"
genome_depth$chrom[genome_depth$chrom %in% "Y"] <- "24"
} else {
genome_depth <- genome_depth[genome_depth$chrom != "X",]
genome_depth <- genome_depth[genome_depth$chrom != "Y",]
}
#sort by chrom, start
genome_depth <- genome_depth[order(genome_depth$chrom, genome_depth$start), ]
#this hides genes on the blacklist
if (!input$show_blacklist) {
for (i in 1:nrow(blacklist)) {
genome_depth <- genome_depth[!(genome_depth$start >= blacklist$V2[i] &
genome_depth$end <= blacklist$V3[i] &
genome_depth$chrom %in% blacklist$V1[i]), ]
}
}
#set up x-axis
xaxis <- genome_depth$start +
cumulative_chrom_sizes[as.numeric(genome_depth$chrom)]
#set up y-axis, if there's no normalization flag,
#generate a base normalization
#if tumor depth is set in the database, use those values initially
if (!input$chromnorm &&
length(genome_depth$tumor_depth[!is.na(genome_depth$tumor_depth)]) > 0) {
genome_depth <- genome_depth[!is.na(genome_depth$tumor_depth), ]
xaxis <- genome_depth$start +
cumulative_chrom_sizes[as.numeric(genome_depth$chrom)]
yaxis <- genome_depth$tumor_depth
} else {
chromlist <- get_chrom_normalization_input()
n_fac <- mean(genome_depth$normal_mean[genome_depth$chrom %in% chromlist])
t_fac <- mean(genome_depth$tumor_mean[genome_depth$chrom %in% chromlist])
yaxis <- (genome_depth$tumor_mean / t_fac) /
(genome_depth$normal_mean / n_fac)
}
#set min and max limits
maxlim <- 2
minlim <- 0
if (input$nolim)
{
temp_y <- yaxis[!is.infinite(yaxis)]
if (as.numeric(input$maxlim) > 0) {
maxlim <- as.numeric(input$maxlim)
} else {
maxlim <- max(temp_y)
if (maxlim < 2) {
maxlim <- 2
}
}
}
#this controls upper and lower bounds
upper_bound <- 1.25
lower_bound <- 0.75
#this colors the control genes gray
genecolors <- rep("black", length(xaxis))
genecolors[yaxis >= upper_bound] = "#e41a1c"
genecolors[yaxis <= lower_bound] = "#377eb8"
if (input$type == 1) {
#display pandq boundaries
pandq <- c()
if (input$pandq) {
pandq <- pq$pqbound + cumulative_chrom_sizes[as.numeric(pq$chrom)]
}
#count chromosome label positions
my_breaks <- chrom_sizes / 2 + cumulative_chrom_sizes
end = 22
if(!input$hideXY)
{
end = 24
}
plot(NA, axes = FALSE, ylim = c(minlim, maxlim),
xlim = c(0, max(cumulative_chrom_sizes[1:end])), xlab = "Chromosome",
ylab = "Mean Ratio")
#shading on the chromosomes
for (i in 1:end) {
if (i %% 2 == 0) {
polygon(c(cumulative_chrom_sizes[i], cumulative_chrom_sizes[i],
cumulative_chrom_sizes[i + 1],
cumulative_chrom_sizes[i + 1]),
c(0, maxlim, maxlim, 0), col = "#d9d9d9", border = FALSE)
} else {
polygon(c(cumulative_chrom_sizes[i], cumulative_chrom_sizes[i],
cumulative_chrom_sizes[i + 1],
cumulative_chrom_sizes[i + 1]),
c(0, maxlim, maxlim, 0), col = "#f0f0f0", border = FALSE)
}
}
points(xaxis, as.numeric(yaxis), pch = 16, cex = 0.7, col = genecolors)
axis(side = 1, at = my_breaks[1:end], tick = FALSE, labels = c(1:22, "X", "Y")[1:end],
las = 2)
abline(v = pandq, col = "#FFFFFF", lty = 2)
abline(h = c(lower_bound, upper_bound), col = "#A6ACA7", lty = 2)
if (input$normline) {
abline(h = 1, col = "black", lty = 2)
}
} else {
#set up x-axis
xaxis <- genome_depth$start[genome_depth$chrom == input$chromnum]
yaxis <- yaxis[genome_depth$chrom == input$chromnum]
genecolors <- rep("black", length(xaxis))
pchtype <- rep(21, length(xaxis))
#set a common boundary so the aspect ratio will remain constant
boundary <- 100000
if (input$continuous & input$showlast) {
plot(NA, axes = FALSE, ylim = c(-0.25, maxlim + 1),
xlim = c(0, boundary), xlab = paste("Chromosome", input$chromnum),
ylab = "Mean Ratio")
} else {
plot(NA, axes = FALSE, ylim = c(-0.25, maxlim), xlim = c(0, boundary),
xlab = paste("Chromosome", input$chromnum), ylab = "Mean Ratio")
}
polygon(c(0, 0, boundary, boundary), c(0, maxlim, maxlim, 0),
col = "#FFFFFF", border = FALSE)
last_gene_line <- c()
last_gene_name <- c()
group <- genome_bed[genome_bed$chrom == input$chromnum, ]
pal <- c("#1f78b4", "#33a02c", "#e31a1c", "#ff7f00", "#6a3d9a",
"#757780", "#b15928")
pal <- rep(pal, 1000)
pal <- pal[1:dim(group)[1]]
group <- cbind(group, pal)
for (i in 1:dim(group)[1]) {
if (length(which(xaxis >= group$start[i] & xaxis<=group$end[i])) > 0) {
genecolors[which(xaxis >= group$start[i] & xaxis <= group$end[i])] <-
as.character(group$pal[i])
last_gene_name <- c(last_gene_name, group$id[i])
last_gene_line <- c(last_gene_line, min(which(xaxis >= group$start[i]
& xaxis<=group$end[i])))
} else {
genecolors[which(xaxis >= group$start[i] & xaxis <= group$end[i])] <-
"black"
}
}
if (input$chromnum == 8) {
legend(0, 0, legend = group$id, col = as.character(group$pal), pch = 16,
horiz = TRUE, bty = "n", cex = 0.75)
} else {
legend(0, 0, legend = group$id, col = as.character(group$pal), pch = 16,
horiz = TRUE, bty = "n", cex = 1.0)
}
if (input$continuous) {
spacer <- floor(chrom_sizes[input$chromnum] / length(xaxis))
xaxis <- 1:length(xaxis) * spacer
}
pandq <- c()
if (input$pandq & !(input$continuous)) {
pandq <- pq$pqbound[as.numeric(pq$chrom) == input$chromnum]
pandq <- (pandq / chrom_sizes[input$chromnum]) * boundary
}
if (input$pandq & input$continuous) {
pandq <- pq$pqbound[as.numeric(pq$chrom) == input$chromnum]
pandq <- (xaxis[min(which(genome_depth$start[genome_depth$chrom ==
input$chromnum] > pandq))]
/ chrom_sizes[input$chromnum]) * boundary
spacer <- floor(chrom_sizes[input$chromnum] / length(xaxis))
pandq <- pandq - ((spacer / 2) / chrom_sizes[input$chromnum] * boundary)
}
points((xaxis / chrom_sizes[input$chromnum]) * boundary, as.numeric(yaxis),
pch = pchtype, cex = 1.3, col = "black", bg = genecolors)
abline(v = pandq, col = "#000000", lty = 2)
abline(h = c(lower_bound, upper_bound), col = "#00000080", lty = 5,
lwd = 1)
abline(h = c(0.25,1.75), col = "#000000BF", lty = 5, lwd = 1)
if (maxlim == 2) {
axis(side = 2, las = 1, at = c(0:4) * 0.5)
axis(side = 4, at = c(0, 0.5, 1.0, 1.5, 2.0),
labels = c("0", "1", "2", "3", "4"), las = 1)
} else {
axis(side = 2, at = 0:(floor(maxlim / 0.4) - 1) * 0.5, las = 1)
axis(side = 4, at = 0:(floor(maxlim / 0.4) - 1) * 0.5,
labels = 0:(floor(maxlim / 0.4) - 1), las = 1)
}
stagger <- 0
if (input$continuous & input$showlast) {
for (i in 1:length(last_gene_line)) {
if (stagger %% 5 == 0) {
text(x = xaxis[last_gene_line[i]] / chrom_sizes[input$chromnum] *
boundary, y = maxlim + 1, adj = c(0.5, 0.5),
labels = last_gene_name[i], col = "black")
segments(x0 = xaxis[last_gene_line[i]] / chrom_sizes[input$chromnum]
* boundary, x1 = xaxis[last_gene_line[i]] /
chrom_sizes[input$chromnum] * boundary, y0 = 0.17,
y1 = maxlim + 0.9, col = "#00000080", lty = 3, lwd = 0.5)
} else if (stagger %% 4 == 0) {
text(x = xaxis[last_gene_line[i]] / chrom_sizes[input$chromnum]
* boundary, y = maxlim + 0.8, adj = c(0.5, 0.5),
labels = last_gene_name[i], col = "black")
segments(x0 = xaxis[last_gene_line[i]] / chrom_sizes[input$chromnum]
* boundary, x1 = xaxis[last_gene_line[i]] /
chrom_sizes[input$chromnum] * boundary, y0 = 0.17,
y1 = maxlim + 0.7, col = "#00000080", lty = 3, lwd = 0.5)
} else if (stagger %% 3 == 0) {
text(x = xaxis[last_gene_line[i]] / chrom_sizes[input$chromnum] *
boundary, y = maxlim + 0.6, adj = c(0.5, 0.5),
labels = last_gene_name[i], col = "black")
segments(x0 = xaxis[last_gene_line[i]] / chrom_sizes[input$chromnum]
* boundary, x1 = xaxis[last_gene_line[i]] /
chrom_sizes[input$chromnum] * boundary, y0=0.17,
y1=maxlim + 0.5, col = "#00000080", lty = 3, lwd = 0.5)
} else if (stagger %% 2 == 0) {
text(x = xaxis[last_gene_line[i]] / chrom_sizes[input$chromnum] *
boundary, y = maxlim + 0.4, adj = c(0.5, 0.5),
labels = last_gene_name[i], col = "black")
segments(x0 = xaxis[last_gene_line[i]] / chrom_sizes[input$chromnum]
* boundary, x1 = xaxis[last_gene_line[i]] /
chrom_sizes[input$chromnum] * boundary, y0 = 0.17,
y1 = maxlim + 0.3, col = "#00000080", lty = 3, lwd = 0.5)
} else {
text(x = xaxis[last_gene_line[i]] / chrom_sizes[input$chromnum] *
boundary, y = maxlim + 0.2, adj = c(0.5, 0.5),
labels = last_gene_name[i], col = "black")
segments(x0 = xaxis[last_gene_line[i]] / chrom_sizes[input$chromnum]
* boundary, x1 = xaxis[last_gene_line[i]] /
chrom_sizes[input$chromnum] * boundary, y0 = 0.17,
y1 = maxlim + 0.1, col = "#00000080", lty = 3, lwd = 0.5)
}
stagger <- stagger + 1
if (stagger > 6) {
stagger <- 1
}
}
}
if (input$normline) {
abline(h = 1, col = "black", lty = 2)
}
}
}
draw_snp_plot <- function() {
if (input$type != 1 & input$showsnps == 1) {
query <- "SELECT chrom, pos, ncount, nvaf, tcount, tvaf from "
query <- paste0(query, "GENOM_SNPDIFF where sample_id = '")
query <- paste0(query, input$sample,"'")
genome_snpdiff <- dbGetQuery(dbhandle, query, stringsAsFactors = FALSE)
#cast
genome_snpdiff$pos <- as.numeric(genome_snpdiff$pos)
genome_snpdiff$nvaf <- as.numeric(genome_snpdiff$nvaf)
genome_snpdiff$tvaf <- as.numeric(genome_snpdiff$tvaf)
genome_snpdiff$chrom <- as.character(genome_snpdiff$chrom)
#limit to only heterozygous
genome_snpdiff <- genome_snpdiff[genome_snpdiff$nvaf < 0.6 &
genome_snpdiff$nvaf > 0.4, ]
#remove the chr prefix
genome_snpdiff$chrom <- gsub("chr", "", genome_snpdiff$chrom)
#remove the sex chromosomes and where the mean == 0
if(!input$hideXY)
{
genome_snpdiff$chrom[genome_snpdiff$chrom %in% "X"] <- "23"
genome_snpdiff$chrom[genome_snpdiff$chrom %in% "Y"] <- "24"
}
genome_snpdiff <- genome_snpdiff[genome_snpdiff$chrom %in%
input$chromnum, ]
genome_snpdiff <- genome_snpdiff[order(genome_snpdiff$pos), ]
#hide blacklist genes unless specified otherwise
if (!input$show_blacklist) {
for (i in 1:nrow(blacklist)) {
genome_snpdiff <-
genome_snpdiff[!(genome_snpdiff$pos >= blacklist$V2[i] &
genome_snpdiff$pos <= blacklist$V3[i] &
genome_snpdiff$chrom %in% blacklist$V1[i]), ]
}
}
xaxis <- genome_snpdiff$pos
boundary <- 100000
plot(NA, axes = FALSE, ylim = c(0, 1), xlim = c(0, boundary),
xlab = paste("Chromosome ", input$chromnum, sep = ""), ylab = "VAF")
abline(h = 0.5, lty = 2)
axis(side = 2, las = 1)
#calculate the boundaries
draw_list_min <- c()
draw_list_max <- c()
draw_list_color <- c()
draw_list_names <- c()
group <- genome_bed[genome_bed$chrom == input$chromnum, ]
pal <- c("#1f78b4", "#33a02c", "#e31a1c", "#ff7f00", "#6a3d9a", "#757780",
"#b15928")
pal <- rep(pal, 1000)
pal <- pal[1:dim(group)[1]]
group <- cbind(group, pal)
#add padding
group$start <- group$start - 100
group$end <- group$end + 100
mypalette <- rep("red", dim(genome_snpdiff)[1])
for (i in 1:dim(group)[1]) {
if (length(which(genome_snpdiff$pos < group$end[i] &
genome_snpdiff$pos > group$start[i]))) {
mypalette[which(genome_snpdiff$pos < group$end[i] &
genome_snpdiff$pos > group$start[i])] <-
as.character(group$pal[i])
draw_list_min <- c(draw_list_min,
min(which(genome_snpdiff$pos < group$end[i] &
genome_snpdiff$pos > group$start[i])))
draw_list_max <- c(draw_list_max,
max(which(genome_snpdiff$pos < group$end[i] &
genome_snpdiff$pos > group$start[i])))
draw_list_color <- c(draw_list_color, as.character(group$pal[i]))
draw_list_names <- c(draw_list_names, group$id[i])
}
}
z <- genome_snpdiff
pandq <- c()
if (input$pandq) {
pandq <- pq$pqbound[as.numeric(input$chromnum)]
pandq - (pandq / chrom_sizes[input$chromnum]) * boundary
}
if (input$continuous) {
spacer <- floor(chrom_sizes[input$chromnum] /
length(genome_snpdiff$pos))
genome_snpdiff$pos <- 1:length(genome_snpdiff$pos) * spacer
genome_snpdiff$pos <- genome_snpdiff$pos - spacer / 2
}
if (input$pandq & input$continuous) {
spacer <- floor(chrom_sizes[input$chromnum] /
length(genome_snpdiff$pos))
pandq <- pq$pqbound[as.numeric(input$chromnum)]
pandq <- ((genome_snpdiff$pos[min(which(z$pos > pandq))] - spacer / 2) /
chrom_sizes[input$chromnum]) * boundary
}
abline(v = pandq, col = "#FFFFFF", lty = 2)
segments((genome_snpdiff$pos / chrom_sizes[input$chromnum]) * boundary,
genome_snpdiff$nvaf,
(genome_snpdiff$pos / chrom_sizes[input$chromnum]) * boundary,
genome_snpdiff$tvaf)
points((genome_snpdiff$pos / chrom_sizes[input$chromnum]) * boundary,
genome_snpdiff$nvaf, pch = 22, cex = 1.35, col = "black",
bg = "black")
points((genome_snpdiff$pos / chrom_sizes[input$chromnum]) * boundary,
genome_snpdiff$tvaf, pch = 21, cex = 1.35, col = "black",
bg = mypalette)
if (input$normline) {
abline(h = c(0, 0.5, 1), col = "gray", lty = 2)
}
}
}
get_rcircos_plot <- function() {
#do struct vars exist for this sample?
query <- "select * from GENOM_STRUCTVAR where sample_id = '"
query <- paste0(query, input$sample,"'")
structvar <- dbGetQuery(dbhandle, query, stringsAsFactors = FALSE)
#do mutations exist for this sample?
mutations <- get_mutations(all_chroms = 1)
#do copy number regions exist for this sample?
query <- "SELECT chrom, start, end, id, normal_mean, tumor_mean, "
query <- paste0(query, "tumor_depth from GENOM_DEPTH where sample_id = '")
query <- paste0(query, input$sample,"'")
copynumber <- dbGetQuery(dbhandle, query, stringsAsFactors = FALSE)
circos_cn_data <- data.frame()
circos_link_data <- data.frame()
circos_mut_data <- data.frame()
chr.exclude <- c("X","Y")
if(!input$hideXY)
{
chr.exclude = NULL
}
if (!input$type) {
chromnum = input$chromnum
if(chromnum == "23")
{
chromnum = "X"
}
if(chromnum == "24")
{
chromnum = "Y"
}
chr.exclude <- c(as.character(1:22), "X", "Y")
chr.exclude <- setdiff(chr.exclude, chromnum)
}
CytoBandIdeogram <- ""
if(configGenomeBuild %in% "hg19")
{
data(UCSC.HG19.Human.CytoBandIdeogram)
UCSC.HG19.Human.CytoBandIdeogram$Chromosome <-
gsub("chr", "" , UCSC.HG19.Human.CytoBandIdeogram$Chromosome)
CytoBandIdeogram <- UCSC.HG19.Human.CytoBandIdeogram
} else {
data(UCSC.HG38.Human.CytoBandIdeogram)
UCSC.HG38.Human.CytoBandIdeogram$Chromosome <-
gsub("chr", "" , UCSC.HG38.Human.CytoBandIdeogram$Chromosome)
CytoBandIdeogram <- UCSC.HG38.Human.CytoBandIdeogram
}
if (nrow(structvar) > 0) {
if (!input$show_failed_structvars) {
structvar <- structvar[structvar$filter %in% "PASS", ]
}
#cast as integer
structvar$pos <- as.numeric(structvar$pos)
#if it's not bnd the end should be in info
nonbnd <- structvar[!(grepl("BND",structvar$id)), ]
ends <- gsub("END=([0-9]*?);.*", "\\1", nonbnd$info)
ends <- as.numeric(ends)
circos_nonbnd <- data.frame(breakpoint1_chrom = nonbnd$chrom,
breakpoint1_start = nonbnd$pos,
breakpoint1_end = nonbnd$pos,
breakpoint2_chrom = nonbnd$chrom,
breakpoint2_start = ends,
breakpoint2_end = ends,
stringsAsFactors=FALSE)
#is this a BND event?
structvar <- structvar[grepl("BND",structvar$id), ]
buffer <- gsub("A|C|G|T|\\[|\\]", "", structvar$alt)
buffer <- strsplit(buffer, ":")
chrom <- unlist(lapply(buffer, "[", 1))
pos <- as.numeric(lapply(buffer, "[", 2))
circos_link_data <- data.frame(breakpoint1_chrom = structvar$chrom,
breakpoint1_start = structvar$pos,
breakpoint1_end = structvar$pos,
breakpoint2_chrom = chrom,
breakpoint2_start = pos,
breakpoint2_end = pos,
stringsAsFactors=FALSE)
circos_link_data <- rbind(circos_link_data, circos_nonbnd)
#don't keep the non-standard chroms
circos_link_data <- circos_link_data[circos_link_data$breakpoint1_chrom %in% c(as.character(1:22), "X", "Y"),]
circos_link_data <- circos_link_data[circos_link_data$breakpoint2_chrom %in% c(as.character(1:22), "X", "Y"),]
if (!input$type) {
chromnum = input$chromnum
if(chromnum == "23")
{
chromnum = "X"
}
if(chromnum == "24")
{
chromnum = "Y"
}
if (input$show_inter_svs) {
circos_link_data <-
circos_link_data[circos_link_data$breakpoint1_chrom %in%
chromnum |
circos_link_data$breakpoint2_chrom %in%
chromnum, ]
involved <- unique(c(circos_link_data$breakpoint1_chrom,
circos_link_data$breakpoint2_chrom))
chr.exclude <- setdiff(chr.exclude, involved)
} else {
circos_link_data <-
circos_link_data[circos_link_data$breakpoint1_chrom %in%
chromnum &
circos_link_data$breakpoint2_chrom %in%
chromnum, ]
}
}
}
if (nrow(mutations) > 0) {
circos_mut_data <- data.frame(breakpoint1_chrom = mutations$chrom,
breakpoint1_start = mutations$pos,
breakpoint1_end = mutations$pos,
symbol = mutations$symbol,
PlotColor="black", stringsAsFactors=FALSE)
if (!input$type) {
circos_mut_data <- circos_mut_data[!(circos_mut_data$breakpoint1_chrom
%in% chr.exclude), ]
}
}
if (nrow(copynumber) > 0) {
#cast to proper data types
copynumber$start <- as.numeric(copynumber$start)
copynumber$end <- as.numeric(copynumber$end)
copynumber$normal_mean <- as.numeric(copynumber$normal_mean)
copynumber$tumor_mean <- as.numeric(copynumber$tumor_mean)
#if the chr prefix is on chrom, remove it
copynumber$chrom <- gsub("chr", "", copynumber$chrom)
#remove the Y chromosome and where the mean == 0
copynumber <- copynumber[copynumber$tumor_mean != 0, ]
#this hides genes on the blacklist
if (!input$show_blacklist) {
for (i in 1:nrow(blacklist)) {
copynumber <- copynumber[!(copynumber$start >= blacklist$V2[i] &
copynumber$end <= blacklist$V3[i] &
copynumber$chrom %in% blacklist$V1[i]), ]
}
}
#set up x-axis
if(!input$hideXY)
{
copynumber$chrom[copynumber$chrom %in% "X"] <- "23"
copynumber$chrom[copynumber$chrom %in% "Y"] <- "24"
} else {
copynumber <- copynumber[copynumber$chrom != "X",]
copynumber <- copynumber[copynumber$chrom != "Y",]
}
xaxis <- copynumber$start +
cumulative_chrom_sizes[as.numeric(copynumber$chrom)]
if(!input$hideXY)
{
copynumber$chrom[copynumber$chrom %in% "23"] <- "X"
copynumber$chrom[copynumber$chrom %in% "24"] <- "Y"
}
#if no data on copy number exclude the chromosome
if(nrow(copynumber[copynumber$chrom %in% "X",]) == 0)
{
if(!("X" %in% chr.exclude))
{
chr.exclude = c(chr.exclude, "X")
}
}
if(nrow(copynumber[copynumber$chrom %in% "Y",]) == 0)
{
if(!("Y" %in% chr.exclude))
{
chr.exclude = c(chr.exclude, "Y")
}
}
print(chr.exclude)
#if tumor depth is set in the database, use those values initially
if (!input$chromnorm &&
length(copynumber$tumor_depth[!is.na(copynumber$tumor_depth)]) > 0) {
copynumber <- copynumber[!is.na(copynumber$tumor_depth),]
xaxis <- copynumber$start +
cumulative_chrom_sizes[as.numeric(copynumber$chrom)]
yaxis <- copynumber$tumor_depth
} else {
chromlist <- get_chrom_normalization_input()
n_fac <- mean(copynumber$normal_mean[copynumber$chrom %in% chromlist])
t_fac <- mean(copynumber$tumor_mean[copynumber$chrom %in% chromlist])
yaxis <- (copynumber$tumor_mean / t_fac) /
(copynumber$normal_mean / n_fac)
}
circos_cn_data <- data.frame(breakpoint1_chrom = copynumber$chrom,
breakpoint1_start = copynumber$start,
breakpoint1_end = copynumber$end,
ratio = yaxis,
PlotColor = "black",
point.type = 16, stringsAsFactors = FALSE)
circos_cn_data$PlotColor[circos_cn_data$ratio >= 1.25] <- "red"
circos_cn_data$PlotColor[circos_cn_data$ratio <= 0.75] <- "blue"
if (!input$type) {
circos_cn_data <-
circos_cn_data[!(circos_cn_data$breakpoint1_chrom %in% chr.exclude), ]
}
}
tracks.inside <- 5
tracks.outside <- 0
RCircos.Set.Core.Components(cyto.info = CytoBandIdeogram,
chr.exclude, tracks.inside, tracks.outside)
RCircos.Set.Plot.Area()
RCircos.Chromosome.Ideogram.Plot()
rcircos.params <- RCircos.Get.Plot.Parameters()
rcircos.params$point.type <- 16
rcircos.params$point.size <- 1
rcircos.params$text.size <- 1
RCircos.Reset.Plot.Parameters(rcircos.params)
if (nrow(circos_link_data) > 0) {
RCircos.Link.Plot(circos_link_data, track.num = 1, by.chromosome = TRUE,
genomic.columns = 3, is.sorted = FALSE)
}
if (nrow(circos_cn_data) > 0) {
RCircos.Scatter.Plot(circos_cn_data, 4, track.num = 2, min.value = 0,side="in",
max.value = 2)
}
if (nrow(circos_mut_data) > 0 && input$hide_circos_muts == 0) {
RCircos.Gene.Connector.Plot(circos_mut_data, 3)
RCircos.Gene.Name.Plot(circos_mut_data, 4, 4)
}
}
get_mutations <- function(all_chroms = 0) {
#load the raw vcf
if (input$all_mutation_information) {
query <- "SELECT * from GENOM_MUTATIONS where sample_id = '"
query <- paste0(query, input$sample,"'")
genome_muts <- dbGetQuery(dbhandle, query, stringsAsFactors = FALSE)
genome_muts$mut_id <- NULL
genome_muts$sample_id <- NULL
} else { #otherwise, parse out the data
#load the data
#get the samples that are in the database
query <- "SELECT distinct mut_tool from GENOM_MUTATIONS "
query <- paste0(query, "where sample_id = '",input$sample,"'")
mut_tools <- dbGetQuery(dbhandle, query, stringsAsFactors=FALSE)
mut_tools <- mut_tools[,1]
genome_muts <- c()
for (i in mut_tools) {
vcf <- read_vcf_for_sample(dbhandle, input$sample, i)
vcf <- make_table(vcf)
genome_muts <- rbind(genome_muts, vcf)
}
if (input$hide_blank_mutations) {
genome_muts <- genome_muts[!(genome_muts$symbol %in% ""), ]
}
}
#remove chrom prefix
genome_muts$chrom = gsub("chr", "", genome_muts$chrom)
if(!input$type && all_chroms == 0) {
genome_muts <- genome_muts[genome_muts$chrom %in% input$chromnum, ]
}
if (!input$show_failed_mutations) {
genome_muts <- genome_muts[genome_muts$filter %in% "PASS", ]
}
#this hides genes on the blacklist
if (!input$show_blacklist) {
for (i in 1:nrow(blacklist)) {
genome_muts <- genome_muts[!(genome_muts$pos >= blacklist$V2[i] &
genome_muts$pos <= blacklist$V3[i] &
genome_muts$chrom %in% blacklist$V1[i]),]
}
}
genome_muts
}
output$plot <- renderPlot(
{
draw_copy_number_plot()
})
output$plot2 <- renderPlot(
{
draw_snp_plot()
})
output$rcircos <- renderPlot(width=750, height=750, {
get_rcircos_plot()
})
output$AutoCN <-
DT::renderDataTable(DT::datatable(rownames = FALSE,
options = list(paging = FALSE,
searching = TRUE), {
# load the data
query <- "select chrom, start, end, id, normal_mean, tumor_mean, "
query <- paste0(query, "tumor_depth from GENOM_DEPTH where sample_id = '")
query <- paste0(query, input$sample,"'")
genome_depth <- dbGetQuery(dbhandle, query, stringsAsFactors = FALSE)
#cast to proper data types
genome_depth$start <- as.numeric(genome_depth$start)
genome_depth$end <- as.numeric(genome_depth$end)
genome_depth$normal_mean <- as.numeric(genome_depth$normal_mean)
genome_depth$tumor_mean <- as.numeric(genome_depth$tumor_mean)
#remove chr prefix if it exists
genome_depth$chrom <- gsub("chr","",genome_depth$chrom)
#remove the sex chromosomes and where the mean == 0
genome_depth <- genome_depth[genome_depth$tumor_mean != 0, ]
if(!input$hideXY) {
genome_depth$chrom[genome_depth$chrom %in% "X"] <- "23"
genome_depth$chrom[genome_depth$chrom %in% "Y"] <- "24"
} else {
genome_depth <- genome_depth[genome_depth$chrom != "X",]
genome_depth <- genome_depth[genome_depth$chrom != "Y",]
}
#hide blacklist genes unless specified otherwise
if (!input$show_blacklist) {
for (i in 1:nrow(blacklist)) {
genome_depth <-
genome_depth[!(genome_depth$start >= blacklist$V2[i] &
genome_depth$end <= blacklist$V3[i] &
genome_depth$chrom %in% blacklist$V1[i]), ]
}
}
#load normalization, if there's no normalization flag,
#generate a base normalization
if (!input$chromnorm &&
length(genome_depth$tumor_depth[!is.na(genome_depth$tumor_depth)]) > 0) {
genome_depth <- genome_depth[!is.na(genome_depth$tumor_depth), ]
norm_depth <- genome_depth$tumor_depth
} else {
chromlist <- get_chrom_normalization_input()
n_fac <- mean(genome_depth$normal_mean[genome_depth$chrom %in% chromlist])
t_fac <- mean(genome_depth$tumor_mean[genome_depth$chrom %in% chromlist])
norm_depth <- (genome_depth$tumor_mean / t_fac) /
(genome_depth$normal_mean / n_fac)
}
#regions above this are considered a gain
upper_bound <- 1.25
#regions below this are considered a loss
lower_bound <- 0.75
genome_depth$norm_depth <- norm_depth
genome_depth$cn_status <- "normal"
genome_depth$cn_status[norm_depth >= upper_bound] <- "gain"
genome_depth$cn_status[norm_depth <= lower_bound] <- "loss"
genome_depth$normal_mean <- NULL
genome_depth$tumor_mean <- NULL
genome_depth$tumor_depth <- NULL
#only show the chromosome we're looking at if
#a specific chromosome is indicated
if (!input$type) {
genome_depth <- genome_depth[genome_depth$chrom %in% input$chromnum, ]
}
genome_depth
}))
output$AutoCNGroups <-
DT::renderDataTable(DT::datatable(rownames = FALSE,
options = list(paging = FALSE,
searching = TRUE), {
# load the data
query <- "select chrom, start, end, id, normal_mean, tumor_mean, "
query <- paste0(query, "tumor_depth from GENOM_DEPTH where sample_id = '")
query <- paste0(query, input$sample,"'")
genome_depth <- dbGetQuery(dbhandle, query, stringsAsFactors = FALSE)
#cast to proper data types
genome_depth$start <- as.numeric(genome_depth$start)
genome_depth$end <- as.numeric(genome_depth$end)
genome_depth$normal_mean <- as.numeric(genome_depth$normal_mean)
genome_depth$tumor_mean <- as.numeric(genome_depth$tumor_mean)
#remove the sex chromosomes and where the mean == 0
genome_depth <- genome_depth[genome_depth$tumor_mean != 0, ]
if(!input$hideXY) {
genome_depth$chrom[genome_depth$chrom %in% "X"] <- "23"
genome_depth$chrom[genome_depth$chrom %in% "Y"] <- "24"
} else {
genome_depth <- genome_depth[genome_depth$chrom != "X",]
genome_depth <- genome_depth[genome_depth$chrom != "Y",]
}
#hide blacklist genes unless specified otherwise
if (!input$show_blacklist) {
for (i in 1:nrow(blacklist)) {
genome_depth <-
genome_depth[!(genome_depth$start >= blacklist$V2[i] &
genome_depth$end <= blacklist$V3[i] &
genome_depth$chrom %in% blacklist$V1[i]), ]
}
}
#load the curator normalization
curator_norm <- 1:22
n_fac <- mean(genome_depth$normal_mean[genome_depth$chrom %in% curator_norm])
t_fac <- mean(genome_depth$tumor_mean[genome_depth$chrom %in% curator_norm])
norm_depth <-
(genome_depth$tumor_mean / t_fac) / (genome_depth$normal_mean / n_fac)
# load snp data
query <- "SELECT chrom, pos, ncount, nvaf, tcount, tvaf from GENOM_SNPDIFF "
query <- paste0(query, "where sample_id = '", input$sample,"'")
snp <- dbGetQuery(dbhandle, query, stringsAsFactors = FALSE)
#cast to proper data types
snp$nvaf <- as.numeric(snp$nvaf)
snp$tvaf <- as.numeric(snp$tvaf)
#calc diff
snp$snp_diff <- abs(snp$nvaf - snp$tvaf)
#regions above this are considered a gain
upper_bound <- 1.25
#regions below this are considered a loss
lower_bound <- 0.75
genome_depth$norm_depth <- norm_depth
genome_depth$cn_status <- "normal"
genome_depth$cn_status[norm_depth >= upper_bound] <- "gain"
genome_depth$cn_status[norm_depth <= lower_bound] <- "loss"
#define groups
results <- c()
for (i in 1:dim(genome_bed)[1]) {
cn_status <-
genome_depth$cn_status[(genome_depth$start >= genome_bed$start[i] &
genome_depth$end <= genome_bed$end[i] &
genome_depth$chrom %in% genome_bed$chrom[i])]
snp_status <-
snp$snp_diff[(snp$pos >= genome_bed$start[i] &
snp$pos <= genome_bed$end[i] &
snp$chrom %in% genome_bed$chrom[i])]
drift <- 0
if (length(snp_status) > 0) {
drift <- median(snp_status)
}
#we want at least two consecutive regions of loss/gain
cn_regions_count <- rle(cn_status)[[1]]
cn_regions_type <- rle(cn_status)[[2]]
#get the regions that aren't normal
if (any(cn_regions_count[cn_regions_type != "normal"] >= 2)) {
cn_status <- cn_status[cn_status != "normal"]
} else {
cn_status <- "normal"
}
cn_status <- unique(cn_status)
if(cn_status == "normal" & drift >= 0.2) {
cn_status = "CNN_LOH"
}
if(cn_status == "gain" & drift >= 0.3) {
cn_status = "GAIN_LOH"
}
cn_status <- paste(unique(cn_status),collapse=",")
results <- rbind(results, c(genome_bed$id[i], cn_status))
}
results
}))
output$downloadDataCN <- downloadHandler(