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Epicapture_HybridizationCaptureStatsPlotting.r
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############################################################################
# Title: Epicapture hybridization capture stats
# Description: Script used to plot target coverage stats (R)
# Author: Miljana Tanic ([email protected])
# Created: September 2019
# Last edited: 18-04-2020 # barplots on coverage stats
###############################################################################
library(tidyr)
library(dplyr)
library(reshape2)
library(RColorBrewer)
library(beanplot)
library(RColorBrewer)
library("devtools")
# install_github("ndphillips/yarrr")
library("yarrr")
library(ggplot2)
library(wesanderson) # works in my R session only
#===================================
# Set working directory
#==================================
Epicapture <- "~/RDS_C2c/EpiCapture/"
setwd(Epicapture)
RESULTS <- paste0(Epicapture,"RESULTS/")
list.files(paste0(RESULTS, "3_TargetCaptureMetrics/"))
#========================================================
# Import hybcapture data output from Picard
#========================================================
# Import metric on target capture efficiencies for 3 hybridization platforms: Target enrichment stats sumarry_Final dataset.xlsx, sheet4
# hyb_capture.df <- read.delim("clipboard", header = TRUE, stringsAsFactors = TRUE )
hyb_capture.df <- read.delim(paste0(RESULTS, "3_TargetCaptureMetrics/Target enricment stats_4R.txt"), header=TRUE, stringsAsFactors = TRUE)
dim(hyb_capture.df)
str(hyb_capture.df)
save(hyb_capture.df, file=paste0(RESULTS,"3_TargetCaptureMetrics/hyb_capture.RData"))
load(paste0(RESULTS,"3_TargetCaptureMetrics/hyb_capture.RData"))
#========================================================
# Plot coverage by sequencing depth per sample for each platform
#========================================================
# Arrange for 3 plots - 1 per row/ 3 per row
par(mfrow=c(1,1))
# plot for each Platform
names(hyb_capture.df)
Agilent="Agilent \n SureSelect MethylSeq"
Roche= "Roche \n SeqCap EpiGiant"
Illumina="Illumina \n TruSeq Methyl Cature EPIC"
head(hyb_capture.df[,c(1:4,21:28)])
pct_cov.df <- hyb_capture.df[,c("PLATFORM_SAMPLE","Platform", "SAMPLE","PF_READS", "PCT_TARGET_BASES_1X", "PCT_TARGET_BASES_2X", "PCT_TARGET_BASES_10X", "PCT_TARGET_BASES_20X", "PCT_TARGET_BASES_30X", "PCT_TARGET_BASES_40X", "PCT_TARGET_BASES_50X", "PCT_TARGET_BASES_100X")]
head(pct_cov.df)
library(dplyr)
x <- c(1,2,10,20,30,40,50,100)
#-------------------
# Agilent:
#-------------------
pct_cov.df %>% filter(Platform=="Agilent") -> y
rownames(y) <- y[,3]
colnames(y)
y <- y[,5:12]
# set color pallete:
names(wes_palettes)
col = wes_palette("Zissou1", 16, type = "continuous")
# plot the first line:
plot(x,y[1,], type="l", lty=1, xaxt='n', col= col[1], ylab = "Fraction of capture targets >= Depth", xlab = "Depth of coverege", main = Agilent)
# plot x-axis
depth <- c( "1x", "2x","10x","20x", "30x","40x","50x", "100x")
axis(side = 1, at = x, labels = depth, tcl = -0.2)
abline(v=x, col="gray", lty=2)
abline(h=0.8, col="red", lty=1)
abline(h=0.6, col="blue", lty=1)
# Add a second line
for (i in 2:16){
lines(x, y[i,], type = "l", lty=1, col= col[i])
}
# Add a legend to the plot
legend("topright", legend=rownames(y),
col=col, lty = 1, cex=0.7)
#----------------
# Roche:
#----------------
pct_cov.df %>% filter(Platform=="Roche") -> y
rownames(y) <- y[,3]
colnames(y)
y <- y[,5:12]
# set color pallete:
colors <- RColorBrewer::brewer.pal(16,)
names(wes_palettes)
col = wes_palette("Zissou1", 16, type = "continuous")
# plot the first line:
plot(x,y[1,], type="l", lty=1, xaxt='n', col= col[1], ylab = "Fraction of targets", xlab = "Depth of coverege", main = Roche)
# plot x-axis
depth <- c( "1x", "2x","10x","20x", "30x","40x","50x", "100x")
axis(side = 1, at = x, labels = depth, tcl = -0.2)
abline(v=x, col="gray", lty=2)
abline(h=0.8, col="red", lty=1)
abline(h=0.6, col="blue", lty=1)
# Add a second line
for (i in 2:16){
lines(x, y[i,], type = "l", lty=1, col= col[i])
}
# Add a legend to the plot
legend("topright", legend=rownames(y),
col=col, lty = 1, cex=0.7)
#------------
# Illumina:
#------------
pct_cov.df %>% filter(Platform=="Illumina") -> y
rownames(y) <- y[,3]
colnames(y)
y <- y[,5:12]
# set color pallete:
colors <- RColorBrewer::brewer.pal(16,)
names(wes_palettes)
col = wes_palette("Zissou1", 16, type = "continuous")
# plot the first line:
plot(x,y[1,], type="l", lty=1, xaxt='n', col= col[1], ylab = "Fraction of targets", xlab = "Depth of coverege", main = Illumina)
# plot x-axis
depth <- c( "1x", "2x","10x","20x", "30x","40x","50x", "100x")
axis(side = 1, at = x, labels = depth, tcl = -0.2)
abline(v=x, col="gray", lty=2)
abline(h=0.8, col="red", lty=1)
abline(h=0.6, col="blue", lty=1)
# Add a second line
for (i in 2:16){
lines(x, y[i,], type = "l", lty=1, col= col[i])
}
# Add a legend to the plot
legend("topright", legend=rownames(y),
col=col, lty = 1, cex=0.7)
#========================================================
# Plot agregated hybridization stats for each platform
#========================================================
#------------------------------------------
# make new dataset and calculate summaries
#------------------------------------------
# hyb_summary <- data.frame(total_unq_mapped=numeric(), pct_on_target=numeric(), pct_off_target=numeric(), pct_on_near_bait_vs_selected=numeric(), mean_bait_cov=numeric(), median_target_cov=numeric(), median_target_cov=numeric(), pct_bases_onbait=numeric(), pct_bases_ontarget=numeric(), fold_enrichment=numeric(), unifirmity=numeric())
# conveert to tibble
hyb_capture <- as_tibble(hyb_capture.df)
names(hyb_capture)
# group by platform
by_platform <- hyb_capture %>%
group_by(Platform)
groups(by_platform)
group_vars(by_platform)
# calculate mean and median for one columns
hyb_capture %>%
group_by(Platform) %>%
summarise(total_unq_mapped.mean=mean(PF_UQ_READS_ALIGNED),
total_unq_mapped.sd=sd(PF_UQ_READS_ALIGNED))
# calculate mean and median for all numeric columns
hyb_summary <- hyb_capture %>%
group_by(Platform) %>%
summarise_if(is.numeric, list(~mean(.), ~sd(.)), na.rm = TRUE)
#-----------------
# Set colors
#----------------
# Selecting colors using yarr (pirateplot)
piratepal(palette= "all")
piratepal("google")
# blue red yellow green
# "#3D79F3FF" "#E6352FFF" "#F9B90AFF" "#34A74BFF"
col.platforms <- c("#3D79F3FF", "#E6352FFF", "#34A74BFF", "#7570b3" , "#F9B90AFF") # GOOD LOKING DIVERGING PALLETE
# make color plaette transparent uing yarr transparent() function:
col.platforms <- transparent(orig.col = col.platforms, trans.val = 0.3) # BEAUTIFUL :)
# check
barplot(x, col= col.platforms2)
legend(x = 'topleft', legend=col.platforms, fill=col.platforms, cex = 0.8)
#------------------------------------------
# plot hybridization efficiency
#------------------------------------------
# make dataset for plotting and set levels for Platform
data <- data.frame(platform=factor(hyb_summary$Platform, levels=c("Agilent", "Illumina", "Roche")), pct_on_target.mean=hyb_summary$PCT_SELECTED_BASES_mean, pct_on_target.sd=hyb_summary$PCT_SELECTED_BASES_sd)
# plot barchart with error bars - Standard deviation
# single color
ggplot(data) +
geom_bar( aes(x=platform, y=pct_on_target.mean), stat="identity", fill="forestgreen", alpha=0.5) +
geom_errorbar( aes(x=platform, ymin=pct_on_target.mean-pct_on_target.sd, ymax=pct_on_target.mean+pct_on_target.sd), width=0.4, colour="orange", alpha=0.9, size=1) + # add error bars
ggtitle("hybridization efficiency") +
xlab("platform") +
ylab("Fraction of reads on/near bait") +
theme_classic()
# by platform colors
ggplot(data) +
geom_bar( aes(x=platform, y=pct_on_target.mean, fill=platform), stat="identity", alpha=0.5) +
geom_errorbar( aes(x=platform, ymin=pct_on_target.mean-pct_on_target.sd, ymax=pct_on_target.mean+pct_on_target.sd), width=0.4, colour="gray56", alpha=0.9, size=0.8) + # add error bars
scale_fill_manual(values=col.platforms[1:3]) +
scale_y_continuous(breaks=c(0.2, 0.4, 0.6, 0.8, 1), labels=c("20", "40", "60", "80", "100")) +
ggtitle("hybridization efficiency") +
xlab("platform") +
ylab("Reads (percent)") +
theme_classic()
#------------------------------------------
# plot hybridization efficiency
#------------------------------------------
# make dataset for plotting and set levels for Platform
data.mean <- data.frame(platform=factor(hyb_summary$Platform, levels=c("Agilent", "Illumina", "Roche")),
pct_on_target.mean=hyb_summary$PCT_SELECTED_BASES_mean,
pct_off_target.mean=hyb_summary$PCT_OFF_BAIT_mean)
data.sd <- data.frame(platform=factor(hyb_summary$Platform, levels=c("Agilent", "Illumina", "Roche")),
pct_on_target.sd=hyb_summary$PCT_SELECTED_BASES_sd,
pct_off_target.sd=hyb_summary$PCT_OFF_BAIT_sd)
# transform to long format
data.mean_long <- gather(data.mean, key = "mean", value = "mean.pct", c("pct_on_target.mean", "pct_off_target.mean"), na.rm = FALSE, convert = FALSE, factor_key = TRUE)
data.sd_long <- gather(data.sd, key = "sd", value = "sd.pct", c("pct_on_target.sd", "pct_off_target.sd"), na.rm = FALSE, convert = FALSE, factor_key = TRUE)
data_long <- cbind(data.mean_long, data.sd_long[,2:3])
data_long
# plot barchart with error bars - Standard deviation
cols <- c("forestgreen","tomato")
ggplot(data_long, aes(x=platform, y=mean.pct, fill=mean)) +
geom_bar(stat="identity", position=position_dodge(), alpha=0.5) +
geom_errorbar( aes(x=platform, ymin=mean.pct-sd.pct, ymax=mean.pct+sd.pct), position=position_dodge(0.9), width=0.4, colour="gray21", alpha=0.9, size=0.5) + # add error bars
scale_fill_manual(values=cols, name="", labels=c("on target", "off target")) +
scale_y_continuous(breaks=c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1), labels=c("10", "20","30", "40", "50", "60", "70", "80","90", "100")) +
ggtitle("hybridization efficiency") +
xlab("") +
ylab("Reads (percent)") +
theme_classic()
#--------------------------------------------------------
# plot mean target coverage, Fold Enrichment, Uniformity
#--------------------------------------------------------
# make dataset for plotting and set levels for Platform
data.mean <- data.frame(platform=factor(hyb_summary$Platform, levels=c("Agilent", "Illumina", "Roche")),
mean_coverage.mean=hyb_summary$MEAN_TARGET_COVERAGE_mean,
fold_enrichment.mean=hyb_summary$FOLD_ENRICHMENT_mean,
uniformity.mean=hyb_summary$FOLD_80_BASE_PENALTY_mean)
data.sd <- data.frame(platform=factor(hyb_summary$Platform, levels=c("Agilent", "Illumina", "Roche")),
mean_coverage.sd=hyb_summary$MEAN_TARGET_COVERAGE_sd,
fold_enrichment.sd=hyb_summary$FOLD_ENRICHMENT_sd,
uniformity.sd=hyb_summary$FOLD_80_BASE_PENALTY_sd)
# transform to long format
data.mean_long <- gather(data.mean, key = "mean", value = "mean.pct", c("mean_coverage.mean", "fold_enrichment.mean", "uniformity.mean"), na.rm = FALSE, convert = FALSE, factor_key = TRUE)
data.sd_long <- gather(data.sd, key = "sd", value = "sd.pct", c("mean_coverage.sd", "fold_enrichment.sd", "uniformity.sd"), na.rm = FALSE, convert = FALSE, factor_key = TRUE)
data_long <- cbind(data.mean_long, data.sd_long[,2:3])
data_long
# plot barchart with error bars - Standard deviation
cols <- c("lightsteelblue3","thistle3", "snow3")
# ggplot(data_long, aes(x=platform, y=mean.pct, fill=mean)) +
# geom_bar(stat="identity", position=position_dodge(), alpha=0.5) +
# geom_errorbar( aes(x=platform, ymin=mean.pct-sd.pct, ymax=mean.pct+sd.pct), position=position_dodge(0.9), width=0.4, colour="gray21", alpha=0.9, size=0.5) + # add error bars
# # scale_fill_manual(values=cols, name="", labels=c("on target", "off target")) +
# # scale_y_continuous(breaks=c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1), labels=c("10", "20","30", "40", "50", "60", "70", "80","90", "100")) +
# # ggtitle("hybridization efficiency") +
# xlab("") +
# ylab("Reads (percent)") +
# theme_classic()
ggplot(data_long, aes(x=platform, y=mean.pct, fill=mean)) +
facet_grid( . ~ mean) +
geom_bar(stat="identity", position=position_dodge(), alpha=0.9) +
geom_errorbar( aes(x=platform, ymin=mean.pct-sd.pct, ymax=mean.pct+sd.pct), position=position_dodge(0.9), width=0.4, colour="gray21", alpha=0.9, size=0.5) + # add error bars
scale_fill_manual(values=cols, name="", labels=c("Mean Coverage", "Fold Enrichment", "Uniformity")) +
# scale_y_continuous(breaks=c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1), labels=c("10", "20","30", "40", "50", "60", "70", "80","90", "100")) +
# ggtitle("hybridization efficiency") +
xlab("") +
ylab("") +
theme_classic()
#--------------------------------------------------------
# plot mean target coverage
#--------------------------------------------------------
data_long %>%
filter(mean=="mean_coverage.mean") %>%
ggplot(aes(x=platform, y=mean.pct, fill=mean)) +
geom_bar( stat="identity", fill="lightsteelblue3", alpha=0.9) +
geom_errorbar( aes(x=platform, ymin=mean.pct-sd.pct, ymax=mean.pct+sd.pct), position=position_dodge(0.9), width=0.4, colour="gray21", alpha=0.9, size=0.5) + # add error bars
scale_y_continuous(breaks=c(10, 20, 30, 40, 50), labels=c("10x", "20x", "30x", "40x", "50x")) +
ggtitle("Mean coverage") +
xlab("") +
ylab("Coverage") +
theme_classic()
#--------------------------------------------------------
# plot fold enrichment
#--------------------------------------------------------
data_long %>%
filter(mean=="fold_enrichment.mean") %>%
ggplot(aes(x=platform, y=mean.pct, fill=mean)) +
geom_bar( stat="identity", fill="thistle3", alpha=0.9) +
geom_errorbar( aes(x=platform, ymin=mean.pct-sd.pct, ymax=mean.pct+sd.pct), position=position_dodge(0.9), width=0.4, colour="gray21", alpha=0.9, size=0.5) + # add error bars
scale_y_continuous(breaks=c(5, 10, 15, 20, 25, 30)) +
ggtitle("Fold Enrichment") +
xlab("") +
ylab("Fold") +
theme_classic()
#--------------------------------------------------------
# plot uniformity
#--------------------------------------------------------
data_long %>%
filter(mean=="uniformity.mean") %>%
ggplot(aes(x=platform, y=mean.pct, fill=mean)) +
geom_bar( stat="identity", fill="snow3", alpha=0.9) +
geom_errorbar( aes(x=platform, ymin=mean.pct-sd.pct, ymax=mean.pct+sd.pct), position=position_dodge(0.9), width=0.4, colour="gray21", alpha=0.9, size=0.5) + # add error bars
scale_y_continuous(breaks=c(1, 2, 3, 4)) +
ggtitle("Uniformity") +
xlab("") +
ylab("") +
theme_classic()
#------------------------------------------
# plot average coverage per platform
#------------------------------------------
# make dataset for plotting and set levels for Platform
data <- data.frame(platform=factor(hyb_summary$Platform, levels=c("Agilent", "Illumina", "Roche")),
cov_1x.mean=hyb_summary$PCT_TARGET_BASES_1X_mean, cov_1x.sd=hyb_summary$PCT_TARGET_BASES_1X_sd,
cov_2x.mean=hyb_summary$PCT_TARGET_BASES_2X_mean, cov_2x.sd=hyb_summary$PCT_TARGET_BASES_2X_sd,
cov_10x.mean=hyb_summary$PCT_TARGET_BASES_10X_mean, cov_10x.sd=hyb_summary$PCT_TARGET_BASES_10X_sd,
cov_20x.mean=hyb_summary$PCT_TARGET_BASES_20X_mean, cov_20x.sd=hyb_summary$PCT_TARGET_BASES_20X_sd,
cov_30x.mean=hyb_summary$PCT_TARGET_BASES_30X_mean, cov_30x.sd=hyb_summary$PCT_TARGET_BASES_30X_sd,
cov_40x.mean=hyb_summary$PCT_TARGET_BASES_40X_mean, cov_40x.sd=hyb_summary$PCT_TARGET_BASES_40X_sd,
cov_50x.mean=hyb_summary$PCT_TARGET_BASES_50X_mean, cov_50x.sd=hyb_summary$PCT_TARGET_BASES_50X_sd,
cov_100x.mean=hyb_summary$PCT_TARGET_BASES_100X_mean, cov_100x.sd=hyb_summary$PCT_TARGET_BASES_100X_sd)
# make dataset for plotting and set levels for Platform
data.mean <- data.frame(platform=factor(hyb_summary$Platform, levels=c("Agilent", "Illumina", "Roche")),
cov_1x.mean=hyb_summary$PCT_TARGET_BASES_1X_mean,
cov_2x.mean=hyb_summary$PCT_TARGET_BASES_2X_mean,
cov_10x.mean=hyb_summary$PCT_TARGET_BASES_10X_mean,
cov_20x.mean=hyb_summary$PCT_TARGET_BASES_20X_mean,
cov_30x.mean=hyb_summary$PCT_TARGET_BASES_30X_mean,
cov_40x.mean=hyb_summary$PCT_TARGET_BASES_40X_mean,
cov_50x.mean=hyb_summary$PCT_TARGET_BASES_50X_mean,
cov_100x.mean=hyb_summary$PCT_TARGET_BASES_100X_mean)
data.sd <- data.frame(platform=factor(hyb_summary$Platform, levels=c("Agilent", "Illumina", "Roche")),
cov_1x.sd=hyb_summary$PCT_TARGET_BASES_1X_sd,
cov_2x.sd=hyb_summary$PCT_TARGET_BASES_2X_sd,
cov_10x.sd=hyb_summary$PCT_TARGET_BASES_10X_sd,
cov_20x.sd=hyb_summary$PCT_TARGET_BASES_20X_sd,
cov_30x.sd=hyb_summary$PCT_TARGET_BASES_30X_sd,
cov_40x.sd=hyb_summary$PCT_TARGET_BASES_40X_sd,
cov_50x.sd=hyb_summary$PCT_TARGET_BASES_50X_sd,
cov_100x.sd=hyb_summary$PCT_TARGET_BASES_100X_sd)
# transform to long format
data.mean_long <- gather(data.mean, key = "mean", value = "mean.pct", 2:9, na.rm = FALSE, convert = FALSE, factor_key = TRUE)
data.sd_long <- gather(data.sd, key = "sd", value = "sd.pct", 2:9, na.rm = FALSE, convert = FALSE, factor_key = TRUE)
data_long <- cbind(data.mean_long, data.sd_long[,2:3])
data_long
# plot by platform colors
# New facet label names for supp variable
mean.labs <- c( "1x", "2x", "10x", "20x", "30x", "40x", "50x", "100x")
names(mean.labs) <- c("cov_1x.mean", "cov_2x.mean", "cov_10x.mean", "cov_20x.mean","cov_30x.mean","cov_40x.mean", "cov_50x.mean", "cov_100x.mean")
# Create the plot
library(scales)
ggplot(data_long, aes(x=platform, y=mean.pct, fill=platform)) +
facet_grid(. ~ mean, labeller =labeller(mean=mean.labs)) +
geom_bar(stat="identity", position=position_dodge(), alpha=0.5) +
geom_errorbar( aes(x=platform, ymin=mean.pct-sd.pct, ymax=mean.pct+sd.pct), position=position_dodge(0.9), width=0.4, colour="gray21", alpha=0.9, size=0.5) + # add error bars
scale_fill_manual(values=col.platforms[1:3]) +
# scale_y_continuous(breaks=c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1), labels=c("10", "20","30", "40", "50", "60", "70", "80","90", "100")) +
scale_y_continuous(breaks=c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1), labels=percent) +
ggtitle("Percent of Targets covered at specific depth") +
xlab("") +
ylab("percent") +
theme_classic() +
theme(axis.text.x = element_blank())
geom_pirate(aes(colour = Platform, fill=Platform), points = FALSE, bars = TRUE, violins = TRUE, # Each of the layers can be turned off, e.g. for just means and confidence intervals:
points_params = list(shape = 19, alpha = 0.2),
lines_params = list(size = 0.8))