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cpdm_sample_processing.R
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setwd("~/Documents/Low_pass/")
library(biomaRt)
library(dplyr)
library(ggplot2)
library(gridExtra)
library(RColorBrewer)
dt <- read.table("CNV_ONCDRS_output_data_Taylor_Data.txt", sep = "\t",
header = T)
seg <- read.table("Tyalor_CPDM_request_Oncopanel.seg", header = T)
cr <- read.table("Tyalor_CPDM_request_Oncopanel.log2ratio", header = T)
cr$gene_symbol <- sapply(strsplit(cr$gene_exon, "_"), "[", 1)
#### look at all genes ####
top100names <- names(sort(table(cr$gene_symbol), decreasing = T))[1:100]
top100 <- cr[which(cr$gene_symbol %in% top100names),]
meanTop100 <- top100 %>%
group_by(Sample, gene_symbol) %>%
summarise(mean = mean(log2.ratio))
ggplot(cr[which(cr$gene_symbol %in% top100names),],
aes(gene_symbol, log2.ratio)) +
geom_point(aes(col = Sample)) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Log2 Ratio for top 100 most common genes in samples")
ggplot(meanTop100, aes(gene_symbol, mean)) +
geom_point(aes(col = Sample)) +
theme(axis.text.x = element_text(angle = 90)) +
ggtitle("Mean Log2 Ratio for top 100 most common genes")
# HA genes to admire
#haGenes <- unique(dt[which(dt$CNV_TYPE_CD == "HA"),2])
haGenes <- unique(dt[which(dt$CNV_TYPE_CD == "HA"),1:2])
#### amplifications ####
mart <- useMart(biomart="ENSEMBL_MART_ENSEMBL",
host="grch37.ensembl.org",
path="/biomart/martservice",
dataset="hsapiens_gene_ensembl")
haTable <- getBM(attributes = c("hgnc_symbol","chromosome_name",
"start_position", "end_position"),
filters = c("hgnc_symbol"),
values = list(haGenes[,2]), mart = mart)
haMerge <- merge.data.frame(haGenes, haTable,
by.x = "GENE", by.y = "hgnc_symbol")
haSegments <- data.frame(ID=NULL, chrom=NULL, start=NULL, end=NULL,
seg.mean=NULL, gene.symbol=NULL)
for (row in 1:dim(haMerge)[1]) {
rawSegment <- seg[which(seg$chrom == haMerge[row,3] &
seg$loc.start <= haMerge[row,4] &
seg$loc.end >= haMerge[row,5] &
seg$ID == haMerge[row, 2]),]
df <- cbind(rawSegment,
gene.symbol = rep(haMerge[row,1], dim(rawSegment)[1]))
haSegments <- rbind(haSegments, df)
}
haSegments$copyNum <- sapply(haSegments$seg.mean, function(x) {return((2^x)*2)})
# plot segment values
getPalette <- colorRampPalette(brewer.pal(6, "Set3"))
getPalette2 <- colorRampPalette(brewer.pal(6, "Set1"))
# png("amplified.png", width = 1024, height = 768, units = "px")
ggplot(haSegments, aes(gene.symbol, copyNum)) +
geom_point(aes(col = factor(ID)), size = 4) +
geom_hline(yintercept = 2, col = "red", linetype = "dashed") +
geom_hline(yintercept = 0) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values=c(getPalette2(3),
getPalette(3)))
# dev.off()
#### regions ####
seg[which(seg$chrom == haMerge[row,3] &
seg$loc.start <= haMerge[row,4] &
seg$loc.end >= haMerge[row,5]),]
findOverlap <- function(segmented, chrom, start, end, margin, mean=0) {
start <- start/margin
end <- end*margin
# contain
positions <- which(segmented$loc.start >= start &
segmented$loc.end <= end &
segmented$chrom == chrom &
segmented$seg.mean >= mean)
return(positions)
}
plotRegions <- function(row) {
# tempPlot <- seg[which(seg$chrom == haMerge[row,3] &
# seg$loc.start <= haMerge[row,4] &
# seg$loc.end >= haMerge[row,5]),]
tempPlot <- seg[findOverlap(seg, haMerge[row,3],
haMerge[row,4], haMerge[row,5],
1.05, 0),]
p <- ggplot(tempPlot, aes(x = factor(ID), y = loc.start,
xend = factor(ID), yend = loc.end)) +
geom_segment(aes(col = seg.mean), size = 4) +
geom_segment(aes(x = factor(haMerge[row,1]), y = haMerge[row,4],
xend = factor(haMerge[row,1]), yend=haMerge[row,5]),
size = 4, col = "red") +
# geom_segment(aes(x = factor("mean"), y=mean(loc.start),
# xend = factor("mean"), yend=mean(loc.end)),
# size = 4, col = "green") +
xlab("CPDM ID") + ylab ("Position") +
theme_minimal() +
ggtitle(haMerge[row,1]) +
coord_flip() +
theme(axis.text.x = element_text(angle = 27.5))
return(p)
}
# png("avail_regions.png", width = 2048, height = 768, units = "px")
# grid.arrange(plotRegions(1), plotRegions(2), plotRegions(5), plotRegions(17),
# plotRegions(29), plotRegions(32), plotRegions(36), plotRegions(40),
# plotRegions(44), plotRegions(47), plotRegions(52), plotRegions(60),
# ncol = 4, nrow = 3)
grid.arrange(plotRegions(1), plotRegions(2), plotRegions(5),
plotRegions(10), plotRegions(13), plotRegions(17),
plotRegions(22), plotRegions(24), ncol = 4, nrow = 2)
# dev.off()
# png("detail.png", width = 1024, height = 768, units = "px")
plotRegions(1)
# dev.off()
#### save regions ####
regions <- data.frame(ID=NULL, chrom=NULL, loc.start=NULL, loc.end=NULL,
seg.mean=NULL, gene=NULL)
for (i in c(1, 2, 13)) {
df <- seg[findOverlap(seg, haMerge[i,3], haMerge[i,4], haMerge[i,5],
1.05, 0),]
df$gene <- haMerge[i, 1]
regions <- rbind(regions, df[which(df$seg.mean == max(df$seg.mean)),])
}
for (i in c(5, 10)) {
df <- seg[findOverlap(seg, haMerge[i,3], haMerge[i,4], haMerge[i,5],
1, 0),]
df$gene <- haMerge[i, 1]
regions <- rbind(regions, df[which(df$ID == "CPDM_1883X"),])
}
df <- seg[findOverlap(seg, haMerge[17,3], haMerge[17,4], haMerge[17,5],1),]
df$gene <- haMerge[17, 1]
regions <- rbind(regions, df[which(df$ID == "CPDM_1717X"),])
df <- seg[findOverlap(seg, haMerge[22,3], haMerge[22,4], haMerge[22,5],1),]
df$gene <- haMerge[22, 1]
regions <- rbind(regions, df)
df <- seg[findOverlap(seg, haMerge[24,3], haMerge[24,4], haMerge[24,5],1.01),]
df$gene <- haMerge[24, 1]
regions <- rbind(regions, df[which(df$ID == "CPDM_0890X"),])
# write.csv(x = regions, file = "gene_regions")
#### amplifcation graphs ####
bigDt <- dt
genesMart <- getBM(attributes = c("hgnc_symbol","chromosome_name",
"start_position", "end_position"),
filters = c("hgnc_symbol"),
values = list(bigDt$GENE), mart = mart)
# get rid of non-standard genes
genesMart <- genesMart[-which(grepl("_", genesMart$chromosome_name)),]
orderedGenes <- data.frame(hgnc_symbol=NULL, chromosome_name=NULL,
start_position=NULL, end_position=NULL)
for (row in 1:length(bigDt$GENE)) {
newRow <- genesMart[which(genesMart$hgnc_symbol == bigDt$GENE[row]),]
if (dim(newRow)[1] == 0) {
newRow <- data.frame(hgnc_symbol="NULL", chromosome_name=0,
start_position=0, end_position=0)
print(newRow)
}
orderedGenes <- rbind(orderedGenes, newRow)
}
bigDtPos <- cbind(bigDt, orderedGenes)
bigDtPos <- bigDtPos[-which(bigDtPos$hgnc_symbol == "NULL"),]
observedSeg <- data.frame(ID=NULL, chrom=NULL, loc.start=NULL,
loc.end=NULL, num.mark=NULL, seg.mean=NULL)
for (row in 1:length(bigDtPos$CPDM_ID)) {
tempSeg <- seg[findOverlap(seg, bigDtPos[row,15],
bigDtPos[row,16], bigDtPos[row, 17], 1, 0),]
newRow <- tempSeg[which(tempSeg$ID == bigDtPos$CPDM_ID[row]),]
if (dim(newRow)[1] == 0) {
newRow <- data.frame(ID=NA, chrom=NA, loc.start=NA, loc.end=NA,
num.mark=NA, seg.mean=NA)
}
observedSeg <- rbind(observedSeg, newRow[1,])
}
names(observedSeg) <- c("ID", "chrom", "seg.start", "seg.end", "num.mark", "seg.mean")
beegDt <- cbind(bigDtPos, observedSeg)
smolDt <- beegDt[which(!is.na(beegDt$seg.mean)),]
smolDt2 <- smolDt[which(smolDt$CNV_TYPE_CD %in% c("HA", "LA")),]
LAmean <- mean(smolDt$seg.mean[which(smolDt$CNV_TYPE_CD == "LA")])
HAmean <- mean(smolDt$seg.mean[which(smolDt$CNV_TYPE_CD == "HA")])
colorVec <- rep("black", 49)
colorVec[c(8, 9, 22, 24, 27, 35, 41)] <- "red"
ggplot(smolDt2, aes(GENE, seg.mean)) +
geom_rect(aes(xmin=-Inf, xmax = Inf, ymin=0, ymax=1),
fill = "cyan", alpha = 0.005) +
geom_rect(aes(xmin=-Inf, xmax = Inf, ymin=1, ymax=Inf),
fill = "pink", alpha = 0.005) +
geom_point(aes(col = CNV_TYPE_CD)) +
theme_minimal() +
ylab("log 2 copy number ratio") +
# geom_hline(yintercept = LAmean) +
# geom_hline(yintercept = HAmean) +
# theme(axis.text.x = element_text(angle = 90)) +
theme(axis.text.x = element_text(
angle = 45,
color = colorVec))