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benchmark_analysis.R
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benchmark_analysis.R
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library(ggplot2)
library(reshape2)
library(dplyr)
library(cowplot)
library(colorRamps)
library(lattice)
library(colorspace)
library(ggsci)
library(scales)
library(stringr)
library(ggrepel)
setwd('/media/array/callers_proj/')
mycol1 = rgb(110, 224, 255, maxColorValue = 255)
mycol2 = rgb(255, 192, 78, maxColorValue = 255)
mycol3 = rgb(255, 177, 177, maxColorValue = 255)
mycol4 = rgb(221, 221, 221, maxColorValue = 255)
mycol5 = rgb(100, 89, 89, maxColorValue = 255)
mycol6 = rgb(0, 189, 189, maxColorValue = 255)
########################Coverage analysis######################################
cov_data = read.table('./All_HS.metrics', sep='\t', header=T)
head(cov_data)
cov_data$TYPE = ifelse(grepl('EXOME', cov_data$SAMPLE) | grepl('RUSZ', cov_data$SAMPLE), 'WES', 'WGS')
ggplot(cov_data, aes(x=SAMPLE, y=MEAN_BAIT_COVERAGE, fill=TYPE)) +
geom_bar(stat='identity', col='black', position='dodge') +
theme_bw() + theme(panel.grid=element_blank())
ggplot(cov_data, aes(x=SAMPLE, y=PCT_TARGET_BASES_10X, fill=TYPE)) +
geom_bar(stat='identity', col='black', position='dodge') +
theme_bw() + theme(panel.grid=element_blank())
cov_scatter <- ggplot(cov_data[1:14, ], aes(x=MEAN_BAIT_COVERAGE, y=PCT_TARGET_BASES_10X, col=TYPE)) +
geom_point(size=4) + theme_bw() + scale_color_npg() +
xlab('Mean CDS coverage (all CDS)') +
ylab('% 10x bases')
print(cov_scatter)
cov_scatter_ng <- ggplot(cov_data[15:20, ], aes(x=MEAN_BAIT_COVERAGE, y=PCT_TARGET_BASES_10X, col=TYPE)) +
geom_point(size=4) + theme_bw() + scale_color_npg() +
xlab('Mean CDS coverage (all CDS)') +
ylab('% 10x bases')
print(cov_scatter_ng)
var_counts = read.table('v4_variant_counts.tsv', sep='\t', header=F)
head(var_counts)
var_counts$sample = sapply(strsplit(var_counts$V1, '_'), function(x) x[1])
var_counts$TYPE = ifelse(str_extract(var_counts$V1, '[A-Z]+OME') == 'EXOME',
'WES', 'WGS')
colnames(var_counts) = c('sample', 'TYPE', 'aligner', 'caller', 'V2')
pf_counts <- ggplot(var_counts, aes(x=sample, y=V2/1000, fill=TYPE)) +
geom_boxplot() + theme_bw() + scale_fill_npg() +
xlab('Sample') + ylab('PASS variants (x1000)')
plot_grid(cov_scatter, pf_counts, nrow=2)
s1b_rev <- pf_counts + scale_y_continuous(limits=c(0, 35)) +
theme(axis.text.x = element_text(angle=45, hjust=1), legend.position='bottom')
aggregate(V2~sample+TYPE, var_counts, median)
mean(aggregate(V2~sample, aggregate(V2~sample+TYPE, var_counts, median),
IQR)[, 'V2'])
cov_data_nods = read.table('./GIAB_CDS_GIABhiconf_nodownsample.metrics', sep='\t', header=T)
head(cov_data_nods)
cov_data_nods$TYPE = ifelse(grepl('EXOME', cov_data_nods$SAMPLE), 'WES', 'WGS')
cov_data_nods = rbind(cov_data_nods, cov_data[cov_data$SAMPLE == 'HG001_EXOME_BWA', ])
cov_data_nods$SAMPLE[11] = 'HG001_DS_EXOME_BWA'
cov_data_nods$TOTAL_READS = round(cov_data_nods$TOTAL_READS/1000000, digits=0)
s1a <- ggplot(cov_data_nods, aes(x=SAMPLE, y=TOTAL_READS, fill=TYPE)) +
geom_bar(stat='identity', col='black', position='dodge') +
theme_bw() + theme(axis.text.x=element_text(angle=90, hjust=1))
s1b <- ggplot(cov_data[grepl('HG00', cov_data$SAMPLE), ],
aes(x=SAMPLE, y=PCT_TARGET_BASES_20X, fill=TYPE)) +
geom_bar(stat='identity', col='black', position='dodge') +
theme_bw() + theme(axis.text.x=element_text(angle=90, hjust=1),
legend.position='bottom') +
scale_y_continuous(limits=c(0, 1)) + xlab('Sample') +
ylab('% CDS bases with 20x coverage')
s1b
s1c <- ggplot(cov_data_nods, aes(x=SAMPLE, y=MEAN_BAIT_COVERAGE, fill=TYPE)) +
geom_bar(stat='identity', col='black', position='dodge') +
theme_bw() + theme(axis.text.x=element_text(angle=90, hjust=1))
plot_grid(s1a, s1b, s1c, nrow=2)
# Revision Figure S1
plot_grid(s1b, s1b_rev, labels=c('a', 'b'), nrow=1)
###################Reading and formatting benchmarking data####################
#bdata = read.table('./all_stats_by_strata_rework.tsv', sep='\t', header=T)
bdata = read.table('./all_stats_v4_by_strata_rework.tsv', sep='\t', header=T)
bdata$Precision.AM = (bdata$QUERY.TP + bdata$FP.gt)/(bdata$QUERY.TP + bdata$QUERY.FP)
bdata$Recall.AM = (bdata$QUERY.TP + bdata$FP.gt)/(bdata$QUERY.TP + bdata$FP.gt + bdata$TRUTH.FN)
bdata$F1_Score.AM = 2/(1/bdata$Precision.AM + 1/bdata$Recall.AM)
bdata$F1_Score.AM[bdata$METRIC.F1_Score == 0] = 0
hist(bdata$Recall.AM)
hist(bdata$Precision.AM)
ggplot(bdata, aes(x=METRIC.F1_Score, y=F1_Score.AM)) + geom_point()
str(bdata)
unique(bdata$Type)
unique(bdata$Subset)
unique(bdata$CallerFilter)
unique(bdata$Aligner)
aliases = data.frame(src = c(unique(bdata$CallerFilter), unique(bdata$Aligner)),
alias = c('CL', 'DV', 'FB', 'G1', 'G2', 'GH', 'OF', 'OS', 'ST',
'BT-LOC', 'BT-E2E', 'BW', 'IS', 'NO'))
bdata$Aligner = sapply(bdata$Aligner,
function(x) aliases[aliases$src == as.character(x), 'alias'])
bdata$CallerFilter = sapply(bdata$CallerFilter,
function(x) aliases[aliases$src == as.character(x), 'alias'])
#######Overall performance comparison#################
# All exons, with padding
pad_data = bdata[bdata$Subset == "*" & bdata$Subtype == "*", ]
snp_pass_pad = pad_data[pad_data$Type == 'SNP' & pad_data$Filter == 'PASS', ]
snp_all_pad = pad_data[pad_data$Type == 'SNP' & pad_data$Filter == 'ALL', ]
indel_pass_pad = pad_data[pad_data$Type == 'INDEL' & pad_data$Filter == 'PASS', ]
indel_all_pad = pad_data[pad_data$Type == 'INDEL' & pad_data$Filter == 'ALL', ]
# Precision
ggplot(snp_all_pad, aes(x=Aligner, y=METRIC.Precision, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons()
prec_snp_pass <- ggplot(snp_pass_pad, aes(x=Aligner, y=METRIC.Precision, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons() + theme_bw() + guides(fill=F)
ggplot(indel_all_pad, aes(x=Aligner, y=METRIC.Precision, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons()
prec_indel_pass <- ggplot(indel_pass_pad, aes(x=Aligner, y=METRIC.Precision, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons() + theme_bw() + guides(fill=F)
plot_grid(prec_snp_pass, prec_indel_pass, nrow=2)
# Recall
ggplot(snp_all_pad, aes(x=Aligner, y=METRIC.Recall, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons() + theme_bw() + guides(fill=F)
recall_snp_pass <- ggplot(snp_pass_pad, aes(x=Aligner, y=METRIC.Recall, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons() + theme_bw() + guides(fill=F)
ggplot(indel_all_pad, aes(x=Aligner, y=METRIC.Recall, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons() + theme_bw() + guides(fill=F)
recall_indel_pass <- ggplot(indel_pass_pad, aes(x=Aligner, y=METRIC.Recall, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons() + theme_bw() + guides(fill=F)
plot_grid(recall_snp_pass, recall_indel_pass, nrow=2)
# F1 Score
sap <- ggplot(snp_all_pad, aes(x=Aligner, y=METRIC.F1_Score, fill=CallerFilter)) +
geom_boxplot() + scale_y_continuous(limits=c(0.3, 1)) + theme_bw() +
theme(legend.position="bottom", panel.grid=element_blank()) +
geom_label(data=generate_label_df(0.4), aes(label=Label),
position=position_dodge(width=0.8), label.size=0.075) +
scale_fill_simpsons()
spp <- ggplot(snp_pass_pad, aes(x=Aligner, y=METRIC.F1_Score, fill=CallerFilter)) +
geom_boxplot() + scale_y_continuous(limits=c(0.3, 1)) + theme_bw() +
theme(panel.grid=element_blank()) + guides(fill=F) +
geom_label(data=generate_label_df(0.4), aes(label=Label),
position=position_dodge(width=0.8), label.size=0.075) +
scale_fill_simpsons()
iap <- ggplot(indel_all_pad, aes(x=Aligner, y=METRIC.F1_Score, fill=CallerFilter)) +
geom_boxplot() + scale_y_continuous(limits=c(0.3, 1)) + theme_bw() +
theme(legend.position="bottom", panel.grid=element_blank()) +
geom_label(data=generate_label_df(0.4), aes(label=Label),
position=position_dodge(width=0.8), label.size=0.075) +
scale_fill_simpsons()
ipp <- ggplot(indel_pass_pad, aes(x=Aligner, y=METRIC.F1_Score, fill=CallerFilter)) +
geom_boxplot() + scale_y_continuous(limits=c(0.3, 1)) + theme_bw() +
theme(panel.grid=element_blank()) + guides(fill=F) +
geom_label(data=generate_label_df(0.4), aes(label=Label),
position=position_dodge(width=0.8), label.size=0.075) +
scale_fill_simpsons()
print(sap)
print(iap)
plot_grid(spp, ipp, nrow=2)
##### Main analysis ################################################
# protein-coding CDS only
pc_data = bdata[bdata$Subset == "GRCh37_proteincoding_only.bed.gz" &
bdata$Subtype == "*", ]
pc_data = pc_data[pc_data$Aligner != 'BT-LOC', ]
pc_data$Aligner = as.factor(as.character(pc_data$Aligner))
snp_pass = pc_data[pc_data$Type == 'SNP' & pc_data$Filter == 'PASS', ]
snp_all = pc_data[pc_data$Type == 'SNP' & pc_data$Filter == 'ALL', ]
indel_pass = pc_data[pc_data$Type == 'INDEL' & pc_data$Filter == 'PASS', ]
indel_all = pc_data[pc_data$Type == 'INDEL' & pc_data$Filter == 'ALL', ]
generate_label_df <- function(positioning) {
df <- data.frame(Aligner = rep(unique(pc_data$Aligner), each = 9),
CallerFilter = rep(unique(bdata$CallerFilter), 4),
METRIC.Precision = rep(positioning, 36),
METRIC.Recall = rep(positioning, 36),
METRIC.F1_Score = rep(positioning, 36),
Label = rep(c('CL', 'DV', 'FB', 'G1', 'G2', 'GH', 'OF', 'OS', 'ST')))
return(df)
}
# Precision
ggplot(snp_all, aes(x=Aligner, y=METRIC.Precision, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons() + theme_bw() + guides(fill=F)
prec_snp_pc_pass <- ggplot(snp_pass, aes(x=Aligner, y=METRIC.Precision, fill=CallerFilter)) +
geom_boxplot() + scale_fill_brewer(palette='Set3') + theme_bw() + guides(fill=F)
ggplot(indel_all, aes(x=Aligner, y=METRIC.Precision, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons() + theme_bw() + guides(fill=F)
prec_indel_pc_pass <- ggplot(indel_pass, aes(x=Aligner, y=METRIC.Precision, fill=CallerFilter)) +
geom_boxplot() + scale_fill_brewer(palette='Set3') + theme_bw() + guides(fill=F)
plot_grid(prec_snp_pc_pass, prec_indel_pc_pass, nrow=2)
# Recall
ggplot(snp_all, aes(x=Aligner, y=METRIC.Recall, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons() + theme_bw() + guides(fill=F)
recall_snp_pc_pass <- ggplot(snp_pass, aes(x=Aligner, y=METRIC.Recall, fill=CallerFilter)) +
geom_boxplot() + scale_fill_brewer(palette='Set3') + theme_bw() + guides(fill=F)
ggplot(indel_all, aes(x=Aligner, y=METRIC.Recall, fill=CallerFilter)) +
geom_boxplot() + scale_fill_simpsons() + theme_bw() + guides(fill=F)
recall_indel_pc_ass <- ggplot(indel_pass, aes(x=Aligner, y=METRIC.Recall, fill=CallerFilter)) +
geom_boxplot() + scale_fill_brewer(palette='Set3')+ theme_bw() + guides(fill=F)
plot_grid(recall_snp_pc_pass, recall_indel_pc_ass, nrow=2)
# F1 Score
sa <- ggplot(snp_all, aes(x=Aligner, y=METRIC.F1_Score, fill=CallerFilter)) +
geom_boxplot() + scale_y_continuous(limits=c(0.4, 1)) + theme_bw() +
theme(legend.position="bottom", panel.grid=element_blank()) +
geom_label(data=generate_label_df(0.45), aes(label=Label),
position=position_dodge(width=0.75), size=2.25) +
scale_fill_brewer(palette='Set3')
sp <- ggplot(snp_pass, aes(x=Aligner, y=METRIC.F1_Score, fill=CallerFilter)) +
geom_boxplot() + scale_y_continuous(limits=c(0.4, 1)) + theme_bw() +
theme(panel.grid=element_blank()) + guides(fill=F) +
geom_label(data=generate_label_df(0.45), aes(label=Label),
position=position_dodge(width=0.75), size=2.25) +
ylab('F1 Score (SNP)') +
scale_fill_brewer(palette='Set3')
ia <- ggplot(indel_all, aes(x=Aligner, y=METRIC.F1_Score, fill=CallerFilter)) +
geom_boxplot() + scale_y_continuous(limits=c(0.4, 1)) + theme_bw() +
theme(legend.position="bottom", panel.grid=element_blank()) +
geom_label(data=generate_label_df(0.45), aes(label=Label),
position=position_dodge(width=0.75), size=2.25) +
scale_fill_brewer(palette='Set3')
ip <- ggplot(indel_pass, aes(x=Aligner, y=METRIC.F1_Score, fill=CallerFilter)) +
geom_boxplot() + scale_y_continuous(limits=c(0.4, 1)) + theme_bw() +
theme(panel.grid=element_blank()) + guides(fill=F) +
geom_label(data=generate_label_df(0.45), aes(label=Label),
position=position_dodge(width=0.75), size=2.25) +
ylab('F1 Score (INDEL)') +
scale_fill_brewer(palette='Set3')
print(sa)
print(ia)
f1 <- plot_grid(sp, ip, nrow=2)
print(f1)
plot_grid(sa, ia, nrow=2)
#######Pairwise comparison of tool performance#################
lattice.options(
layout.heights=list(bottom.padding=list(x=0), top.padding=list(x=0)),
layout.widths=list(left.padding=list(x=0), right.padding=list(x=0))
)
drawCoolHM = function(df, p_df){
myPanel_a <- function(x, y, z, ...) {
panel.levelplot(x,y,z,...)
panel.text(x, y, p_df[cbind(x,y)]) ## use handy matrix indexing
}
return(levelplot(df, col.regions=jet,
at=seq(min(df), max(df), length.out=100),
aspect='fill', colorkey=list(width=3, labels=list(cex=1.0)),
scales=list(x=list(rot=45)), xlab=list(label=''),
ylab=list(label=''), panel=myPanel_a))
}
mypal = colorRampPalette(c('white', '#ecad2f'))
mypal = colorRampPalette(c('white', '#d50f0dff'))
jet = diverge_hsv(100)
#jet = mypal(100)
aligner_pairwise = matrix(0, nrow=4, ncol=4)
rownames(aligner_pairwise) = unique(snp_pass$Aligner)
colnames(aligner_pairwise) = unique(snp_pass$Aligner)
pvals = matrix("", nrow=4, ncol=4)
rownames(pvals) = unique(snp_pass$Aligner)
colnames(pvals) = unique(snp_pass$Aligner)
for (i in unique(snp_pass$Aligner)) {
for (j in unique(snp_pass$Aligner)) {
left = as.numeric(snp_pass[snp_pass$Aligner == i, 'METRIC.F1_Score'])
right = as.numeric(snp_pass[snp_pass$Aligner == j, 'METRIC.F1_Score'])
difference = median(left - right)
if (i == j){
pvals[i, j] = ""
} else {
pval = wilcox.test(left, right, paired=T)$p.value
aligner_pairwise[i, j] = difference
pvals[i, j] = ifelse(pval < 0.001, "***", ifelse(pval < 0.01, "**",
ifelse(pval < 0.05, "*", "")))
}
}
}
snp_aln <- drawCoolHM(aligner_pairwise, pvals)
print(snp_aln)
# Variant callers
caller_pairwise = matrix(0, nrow=9, ncol=9)
rownames(caller_pairwise) = unique(snp_pass$CallerFilter)
colnames(caller_pairwise) = unique(snp_pass$CallerFilter)
pvals_callers = matrix("", nrow=9, ncol=9)
rownames(pvals_callers) = unique(snp_pass$CallerFilter)
colnames(pvals_callers) = unique(snp_pass$CallerFilter)
for (i in unique(snp_pass$CallerFilter)) {
for (j in unique(snp_pass$CallerFilter)) {
left = as.numeric(snp_pass[snp_pass$CallerFilter == i, 'METRIC.F1_Score'])
right = as.numeric(snp_pass[snp_pass$CallerFilter == j, 'METRIC.F1_Score'])
difference = median(left - right)
if (i == j){
pvals_callers[i, j] = ""
} else {
pval = wilcox.test(left, right, paired=T)$p.value
caller_pairwise[i, j] = difference
pvals_callers[i, j] = ifelse(pval < 0.001, "***", ifelse(pval < 0.01, "**",
ifelse(pval < 0.05, "*", "")))
}
}
}
snp_call <- drawCoolHM(caller_pairwise, pvals_callers)
print(snp_call)
aligner_pairwise_id = matrix(0, nrow=4, ncol=4)
rownames(aligner_pairwise_id) = unique(snp_pass$Aligner)
colnames(aligner_pairwise_id) = unique(snp_pass$Aligner)
pvals_aln_id = matrix("", nrow=4, ncol=4)
rownames(pvals_aln_id) = unique(snp_pass$Aligner)
colnames(pvals_aln_id) = unique(snp_pass$Aligner)
for (i in unique(snp_pass$Aligner)) {
for (j in unique(snp_pass$Aligner)) {
left = as.numeric(indel_pass[indel_pass$Aligner == i, 'METRIC.F1_Score'])
right = as.numeric(indel_pass[indel_pass$Aligner == j, 'METRIC.F1_Score'])
difference = median(left - right)
if (i == j){
pvals_aln_id[i, j] = ""
} else {
pval = wilcox.test(left, right, paired=T)$p.value
aligner_pairwise_id[i, j] = difference
pvals_aln_id[i, j] = ifelse(pval < 0.001, "***", ifelse(pval < 0.01, "**",
ifelse(pval < 0.05, "*", "")))
}
}
}
indel_aln <- drawCoolHM(aligner_pairwise_id, pvals_aln_id)
print(indel_aln)
# Variant callers
caller_pairwise_id = matrix(0, nrow=9, ncol=9)
rownames(caller_pairwise_id) = unique(snp_pass$CallerFilter)
colnames(caller_pairwise_id) = unique(snp_pass$CallerFilter)
pvals_callers_id = matrix("", nrow=9, ncol=9)
rownames(pvals_callers_id) = unique(snp_pass$CallerFilter)
colnames(pvals_callers_id) = unique(snp_pass$CallerFilter)
for (i in unique(snp_pass$CallerFilter)) {
for (j in unique(snp_pass$CallerFilter)) {
left = as.numeric(indel_pass[indel_pass$CallerFilter == i, 'METRIC.F1_Score'])
right = as.numeric(indel_pass[indel_pass$CallerFilter == j, 'METRIC.F1_Score'])
difference = median(left - right)
if (i == j){
pvals_callers_id[i, j] = ""
} else {
pval = wilcox.test(left, right, paired=T)$p.value
caller_pairwise_id[i, j] = difference
pvals_callers_id[i, j] = ifelse(pval < 0.001, "***", ifelse(pval < 0.01, "**",
ifelse(pval < 0.05, "*", "")))
}
}
}
indel_call <- drawCoolHM(caller_pairwise_id, pvals_callers_id)
print(indel_call)
## Plotting Figure 2
bottom_left <- plot_grid(snp_aln, indel_aln, nrow=2, labels=c('b', 'c'))
bottom <- plot_grid(bottom_left, snp_call, indel_call,
ncol=3, rel_widths = c(1, 1.05, 1), labels=c('', 'd', 'e'))
#bottom <- plot_grid(snp_call, indel_call, filtr_stats, nrow=1,
# rel_widths = c(1, 1, 0.95), labels=c('d', 'e', 'f'))
plot_grid(f1, bottom, nrow=2, rel_heights = c(1, 0.8), labels=c('a', ''))
########################################################################################
#print(f1)
all_data = pc_data[pc_data$Filter == 'ALL', ]
pass_data = pc_data[pc_data$Filter == 'PASS', ]
all_data$PrecisionGain = pass_data$METRIC.Precision - all_data$METRIC.Precision
all_data$RecallLoss = pass_data$METRIC.Recall - all_data$METRIC.Recall
gain_loss = melt(all_data, id.vars=c('Sample', 'ExperimentType', 'Aligner',
'CallerFilter', 'Type'), measure.vars = c('PrecisionGain', 'RecallLoss'))
head(gain_loss)
mean_diff = aggregate(value ~ variable + CallerFilter + Type, gain_loss, median)
#se_diff = aggregate(value ~ variable + CallerFilter + Type, gain_loss,
# function(x) IQR(x)/sqrt(length(x)))
#mean_diff$se = se_diff$value
mean_diff$y_min = aggregate(value ~ variable + CallerFilter + Type, gain_loss,
function(x) quantile(x)[2])$value
mean_diff$y_max = aggregate(value ~ variable + CallerFilter + Type, gain_loss,
function(x) quantile(x)[4])$value
mean_diff$variable = factor(rep(c('Precision', 'Recall'), nrow(mean_diff)/2))
raw_f1s = aggregate(METRIC.F1_Score ~ CallerFilter + Type, all_data, median)
print(raw_f1s)
print(mean_diff)
filtr_stats <- ggplot(mean_diff, aes(x=CallerFilter, y=value,
fill=variable, ymin=y_min, ymax=y_max)) +
geom_bar(stat='identity', col='black', position='dodge') +
geom_errorbar(width=0.4, position=position_dodge(width=0.9)) + theme_bw() +
theme(panel.grid = element_blank(), legend.position='top') +
ylab('Change in metric\nvalue after filtering') +
scale_fill_manual(values=c(mycol1, mycol3)) +
facet_wrap(~Type, nrow=1)
print(filtr_stats)
gain_loss$variable = factor(as.character(gain_loss$variable), levels=c('RecallLoss', 'PrecisionGain'))
gain_loss = gain_loss[gain_loss$Aligner != 'BT-E2E', ]
filtr_stats_box <- ggplot(gain_loss, aes(x=CallerFilter, y=abs(value), col=variable)) +
geom_boxplot(position='dodge') + theme_bw() +
theme(panel.grid = element_blank(), legend.position='bottom') +
ylab('Change in metric\nvalue after filtering') +
scale_color_simpsons() + scale_y_continuous(limits=c(0, 0.4)) +
facet_grid(vars(Type), vars(ExperimentType))
print(filtr_stats_box)
mean_diff_pub = aggregate(value ~ variable + CallerFilter + Type + ExperimentType, gain_loss, median)
mean_diff_pub
################ Spread across aligners ########################
caller_perf = aggregate(METRIC.F1_Score ~ ExperimentType + Type + Sample + CallerFilter, pc_data, max)
caller_perf$min_f1 <- aggregate(METRIC.F1_Score ~ ExperimentType + Type + Sample + CallerFilter,
pc_data, min)$METRIC.F1_Score
caller_perf$spread = caller_perf$METRIC.F1_Score - caller_perf$min_f1
wbwt <- ggplot(caller_perf, aes(x=CallerFilter, y=spread, fill=CallerFilter)) +
geom_boxplot() + theme_bw() +
scale_fill_brewer(palette = "Set3") +
facet_wrap(~Type) + scale_y_continuous(limits=c(0, 0.3)) +
ylab('Diff. between aligners\n(with Bowtie2)')
print(wbwt)
caller_perf_b = aggregate(METRIC.F1_Score ~ ExperimentType + Type + Sample + CallerFilter,
pc_data[pc_data$Aligner != 'BT-E2E', ], max)
caller_perf_b$min_f1 <- aggregate(METRIC.F1_Score ~ ExperimentType + Type + Sample + CallerFilter,
pc_data[pc_data$Aligner != 'BT-E2E', ], min)$METRIC.F1_Score
caller_perf_b$spread = caller_perf_b$METRIC.F1_Score - caller_perf_b$min_f1
wobwt <- ggplot(caller_perf_b, aes(x=CallerFilter, y=spread, fill=CallerFilter)) +
geom_boxplot() + theme_bw() +
scale_fill_brewer(palette = "Set3") +
facet_wrap(~Type) + scale_y_continuous(limits=c(0, 0.3)) +
ylab('Diff. between aligners\n(w/o Bowtie2)')
print(wobwt)
plot_grid(wbwt, wobwt, nrow=2)
# filter_boxes <- ggplot(gain_loss, aes(x=CallerFilter, y=value,
# fill=variable)) +
# geom_boxplot(outlier.shape=NA, position='dodge') +
# theme_bw() +
# theme(panel.grid = element_blank(), legend.position='top') +
# ylab('Change in metric\nvalue after filtering') +
# scale_fill_manual(values=c(mycol1, mycol3)) +
# facet_wrap(~Type, nrow=1) +
# scale_y_continuous(limits=c(-0.5, 0.25))
# print(filter_boxes)
pass_data$Pipeline = paste(pass_data$Aligner, pass_data$CallerFilter, sep='_')
head(pass_data)
aggregated_stat = aggregate(METRIC.F1_Score ~ Pipeline + Type, pass_data, median)
aggregated_stat$Precision = aggregate(METRIC.Precision ~ Pipeline + Type,
pass_data, median)$METRIC.Precision
aggregated_stat$Recall = aggregate(METRIC.Recall ~ Pipeline + Type,
pass_data, median)$METRIC.Recall
print(aggregated_stat[order(aggregated_stat$METRIC.F1_Score, decreasing = T), ])
# Best performing is BW_DV, next non-DV solution is NO_GH, then the best for Strelka is IS_ST
aggregated_stat2 = aggregate(METRIC.F1_Score ~ Pipeline + Type + ExperimentType,
pass_data, median)
aggregated_stat2$Precision = aggregate(METRIC.Precision ~ Pipeline + Type + ExperimentType,
pass_data, median)$METRIC.Precision
aggregated_stat2$Recall = aggregate(METRIC.Recall ~ Pipeline + Type + ExperimentType,
pass_data, median)$METRIC.Recall
stats_ordered = aggregated_stat2[order(aggregated_stat2$METRIC.F1_Score, decreasing = T), ]
print(stats_ordered[grepl('NO_', stats_ordered$Pipeline), ])
wgs_perf <- aggregated_stat2[aggregated_stat2$ExperimentType == 'GENOME', ]
wes_perf = aggregated_stat2[aggregated_stat2$ExperimentType == "EXOME",]
wgs_perf$WES_F1 = wes_perf$METRIC.F1_Score
wgs_perf$WES_WGS_diff = wgs_perf$METRIC.F1_Score - wgs_perf$WES_F1
aggregate(WES_WGS_diff~Type, wgs_perf, median)
####################Analysis of covariates#####################################
snp_all_aln <- ggplot(snp_pass, aes(x=ExperimentType, y=METRIC.F1_Score, col=ExperimentType)) +
geom_boxplot() + theme_bw() +
theme(panel.grid=element_blank(), axis.text.x=element_blank()) +
facet_grid(vars(Aligner), vars(CallerFilter)) + scale_color_npg()
indel_all_aln <- ggplot(indel_pass, aes(x=ExperimentType, y=METRIC.F1_Score, col=ExperimentType)) +
geom_boxplot() + theme_bw() +
theme(panel.grid=element_blank(), axis.text.x=element_blank()) +
facet_grid(vars(Aligner), vars(CallerFilter)) + scale_color_npg()
plot_grid(snp_all_aln, indel_all_aln, nrow=2)
ggplot(indel_pass, aes(x=ExperimentType, y=METRIC.F1_Score, col=ExperimentType)) +
geom_boxplot() + geom_jitter() + theme_bw() + theme(panel.grid=element_blank()) +
facet_grid(vars(Aligner), vars(CallerFilter))
snp_gve <- ggplot(snp_pass[snp_pass$Aligner == 'NO', ],
aes(x=ExperimentType, y=METRIC.F1_Score, col=ExperimentType)) +
geom_boxplot() + #geom_jitter() +
theme_bw() + theme(panel.grid=element_blank(),
axis.text.x=element_blank(),
axis.title.x=element_blank()) +
facet_wrap(~CallerFilter, nrow=1) + scale_color_npg() +
scale_y_continuous(limits=c(0.7, 1)) +
ylab('F1 Score (SNP)') + guides(color=F)
indel_gve <- ggplot(indel_pass[indel_pass$Aligner == 'NO', ],
aes(x=ExperimentType, y=METRIC.F1_Score, col=ExperimentType)) +
geom_boxplot() + #geom_jitter() +
theme_bw() + theme(panel.grid=element_blank(),
axis.text.x=element_blank(),
axis.title.x=element_blank()) +
facet_wrap(~CallerFilter, nrow=1) + scale_color_npg() +
scale_y_continuous(limits=c(0.7, 1)) +
ylab('F1 Score (INDEL)') + guides(color=F)
gve_f1 <- plot_grid(snp_gve, indel_gve, nrow=2)
print(gve_f1)
# Coverage strata
unique(bdata$Subset)
bdata$genome_cov_level = as.numeric(sapply(bdata$Subset, function(x)
ifelse(grepl('genome_mean_cov', x), strsplit(x, '_')[[1]][4], NA)))
bdata$exome_cov_level = as.numeric(sapply(bdata$Subset, function(x)
ifelse(grepl('exome_mean_cov', x), strsplit(x, '_')[[1]][4], NA)))
#hist(bdata$genome_cov_level)
# Genome coverage stratification
genome_cov_strata = bdata[(!(is.na(bdata$genome_cov_level))) &
bdata$ExperimentType == 'GENOME' &
bdata$Filter == 'PASS' &
bdata$Subtype == '*' &
bdata$Aligner == 'NO' &
bdata$TRUTH.TOTAL > 10, ]
gcs_aggr = aggregate(METRIC.F1_Score ~ genome_cov_level+CallerFilter + Type,
genome_cov_strata, mean)
gcs_aggr$se = aggregate(METRIC.F1_Score ~ genome_cov_level+CallerFilter + Type,
genome_cov_strata, function(x) sd(x)/sqrt(length(x)))$METRIC.F1_Score
gcs <- ggplot(gcs_aggr, aes(x=genome_cov_level, y=METRIC.F1_Score,
ymin=METRIC.F1_Score-se, ymax=METRIC.F1_Score+se,
col=CallerFilter)) +
geom_line(lwd=1) + #geom_errorbar(width=0.2, lwd=1.1) +
geom_point(size=3) + theme_bw() +
theme(panel.grid=element_blank()) + scale_color_brewer(palette = "Set3") +
facet_wrap(~Type, nrow=1) + scale_y_continuous(limits=c(0, 1)) +
scale_x_continuous(limits=c(-0.1, 2.1)) +
ylab('F1 Score')
# Exome coverage stratification
exome_cov_strata = bdata[(!(is.na(bdata$exome_cov_level))) &
bdata$ExperimentType == 'EXOME' &
bdata$Filter == 'PASS' &
bdata$Subtype == '*' &
bdata$Aligner == 'NO' &
bdata$TRUTH.TOTAL > 10, ]
ecs_aggr = aggregate(METRIC.F1_Score ~ exome_cov_level+CallerFilter + Type,
exome_cov_strata, mean)
ecs_aggr$se = aggregate(METRIC.F1_Score ~ exome_cov_level+CallerFilter + Type,
exome_cov_strata, function(x) sd(x)/sqrt(length(x)))$METRIC.F1_Score
ecs <- ggplot(ecs_aggr, aes(x=exome_cov_level, y=METRIC.F1_Score,
ymin=METRIC.F1_Score-se, ymax=METRIC.F1_Score+se,
col=CallerFilter)) +
geom_line(lwd=1) + #geom_errorbar(width=0.2, lwd=1.1) +
geom_point(size=3) + theme_bw() +
theme(panel.grid=element_blank()) + scale_color_brewer(palette = "Set3") +
facet_wrap(~Type, nrow=1) + scale_y_continuous(limits=c(0, 1)) +
scale_x_continuous(limits=c(-0.1, 2.1)) +
ylab('F1 Score')
cov_strata <- plot_grid(gcs, ecs, nrow=2)
print(cov_strata)
gcs_aggr$ExperimentType = 'GENOME'
colnames(gcs_aggr)[1] = 'normcov'
ecs_aggr$ExperimentType = 'EXOME'
colnames(ecs_aggr)[1] = 'normcov'
all_cov_stats = rbind(gcs_aggr, ecs_aggr)
cov_plot <- ggplot(all_cov_stats, aes(x=normcov, y=METRIC.F1_Score,
ymin=METRIC.F1_Score-se, ymax=METRIC.F1_Score+se,
col=CallerFilter)) +
geom_line(lwd=1) + #geom_errorbar(width=0.2, lwd=1.1) +
geom_point(size=2) + theme_bw() +
theme(panel.grid=element_blank(), legend.position = "bottom") +
scale_color_brewer(palette = "Set3") +
facet_grid(vars(Type), vars(ExperimentType)) +
scale_y_continuous(limits=c(0, 1)) +
scale_x_continuous(limits=c(-0.1, 2.0)) +
ylab('F1 Score') + guides(color=F)
print(cov_plot)
# By GC - stratifications provided by GIAB (custom GC-stratified data below)
bdata$GC_pct = ifelse(grepl('GRCh37_GC', bdata$Subset),
as.numeric(sapply(bdata$Subset,
function(x) tail(strsplit(strsplit(as.character(x),
'GC_0')[[1]][2],
'.bed')[[1]][1], n=1))), NA)
hist(bdata$GC_pct)
unique(bdata$GC_pct)
by_gc = bdata[(!(is.na(bdata$GC_pct))) &
bdata$Filter == 'PASS' &
bdata$Subtype == '*' &
bdata$Aligner == 'NO' &
bdata$TRUTH.TOTAL > 10, ]
gc_aggr = aggregate(METRIC.F1_Score ~ ExperimentType + Type + CallerFilter + GC_pct,
by_gc, mean)
gc_aggr$se = aggregate(METRIC.F1_Score ~ ExperimentType + Type + CallerFilter + GC_pct,
by_gc, function(x) sd(x)/sqrt(length(x)))$METRIC.F1_Score
gc_plot <- ggplot(gc_aggr, aes(x=GC_pct, y=METRIC.F1_Score,
ymin=METRIC.F1_Score-se, ymax=METRIC.F1_Score+se,
col=CallerFilter)) +
geom_line(lwd=1) + #geom_errorbar(width=0.2, lwd=1.1) +
geom_point(size=2) + theme_bw() +
theme(panel.grid=element_blank(), legend.position = "bottom") +
scale_color_brewer(palette = "Set3") +
facet_grid(vars(Type), vars(ExperimentType)) +
scale_y_continuous(limits=c(0, 1)) +
scale_x_continuous(limits=c(0, 100)) +
ylab('F1 Score') + guides(color=F)
print(gc_plot)
strat_plots <- plot_grid(cov_plot, gc_plot, nrow=1, labels=c('c', 'd'))
print(strat_plots)
exo_data = read.table('./all_exo_q10_mf.tsv', sep='\t', header=T, row.names=1)
hist(exo_data$GC)
hist(exo_data$MCOV_agilent)
# Stratify by MF
bdata$genome_mf = as.numeric(sapply(bdata$Subset, function(x)
ifelse(grepl('genome_mean_mf', x), strsplit(x, '_')[[1]][4], NA)))
bdata$exome_mf = as.numeric(sapply(bdata$Subset, function(x)
ifelse(grepl('exome_mean_mf', x), strsplit(x, '_')[[1]][4], NA)))
genome_mf_strata = bdata[(!(is.na(bdata$genome_mf))) &
bdata$ExperimentType == 'GENOME' &
bdata$Filter == 'PASS' &
bdata$Subtype == '*' &
bdata$Aligner == 'NO' &
bdata$TRUTH.TOTAL > 10 &
bdata$Type == "SNP", ]
gmfs_aggr = aggregate(METRIC.F1_Score ~ genome_mf+CallerFilter + Type,
genome_mf_strata, mean)
gmfs_aggr$se = aggregate(METRIC.F1_Score ~ genome_mf+CallerFilter + Type,
genome_mf_strata, function(x) sd(x)/sqrt(length(x)))$METRIC.F1_Score
exome_mf_strata = bdata[(!(is.na(bdata$exome_mf))) &
bdata$ExperimentType == 'EXOME' &
bdata$Filter == 'PASS' &
bdata$Subtype == '*' &
bdata$Aligner == 'NO' &
bdata$TRUTH.TOTAL > 10 &
bdata$Type == "SNP", ]
emfs_aggr = aggregate(METRIC.F1_Score ~ exome_mf+CallerFilter + Type,
exome_mf_strata, mean)
emfs_aggr$se = aggregate(METRIC.F1_Score ~ exome_mf+CallerFilter + Type,
exome_mf_strata, function(x) sd(x)/sqrt(length(x)))$METRIC.F1_Score
gmfs_aggr$ExperimentType = 'GENOME'
colnames(gmfs_aggr)[1] = 'MF'
emfs_aggr$ExperimentType = 'EXOME'
colnames(emfs_aggr)[1] = 'MF'
all_mf_stats = rbind(gmfs_aggr, emfs_aggr)
mfs <- ggplot(all_mf_stats, aes(x=MF, y=METRIC.F1_Score,
ymin=METRIC.F1_Score-se, ymax=METRIC.F1_Score+se,
col=CallerFilter)) +
geom_line(lwd=1) + #geom_errorbar(width=0.2, lwd=1.1) +
geom_point(size=2) + theme_bw() +
theme(panel.grid=element_blank(), legend.position = "bottom") +
scale_color_brewer(palette = "Set3") +
facet_wrap(~ExperimentType, nrow=2) + scale_y_continuous(limits=c(0, 1)) +
scale_x_continuous(limits=c(-0.1, 0.9)) +
ylab('F1 Score') + guides(color=F)
print(mfs)
f3_bottom <- plot_grid(strat_plots, mfs, rel_widths = c(1, 0.35), labels=c('', 'e'))
print(f3_bottom)
###Padding
pad_data = bdata[grepl('vicinity', bdata$Subset), ]
pad_data = pad_data[pad_data$Subtype == '*' &
pad_data$Filter == "PASS" &
pad_data$Aligner == "NO" &
pad_data$Caller %in% c('DV', 'ST', 'GH'), ]
unique(pad_data$Subset)
dist_df = data.frame(sets=unique(pad_data$Subset),
distancia=c(0, 100, 125, 150, 25, 50, 75))
pad_data$Distance = sapply(as.character(pad_data$Subset),
function(x) dist_df[dist_df$sets == x, 'distancia'])
dist_aggr = aggregate(METRIC.F1_Score~Type+ExperimentType+Distance, pad_data, median)
dist_aggr$se = aggregate(METRIC.F1_Score~Type+ExperimentType+Distance, pad_data,
function(x) sd(x)/sqrt(length(x)))$METRIC.F1_Score
distances <- ggplot(dist_aggr[dist_aggr$Distance <= 125, ], aes(x=Distance, y=METRIC.F1_Score,
ymin=METRIC.F1_Score-se, ymax=METRIC.F1_Score+se,
col=ExperimentType)) +
geom_line(lwd=1) + #geom_errorbar(width=0.2, lwd=1.1) +
geom_point(size=2) + theme_bw() +
theme(panel.grid=element_blank(), legend.position = "bottom") +
scale_color_npg() +
facet_wrap(~Type, nrow=2) + scale_y_continuous(limits=c(0.75, 1)) +
scale_x_continuous(limits=c(-1, 126)) +
ylab('F1 Score') + guides(color=F)
print(distances)
f3_top = plot_grid(gve_f1, distances, nrow=1, rel_widths = c(1, 0.35), labels=c('a', 'b'))
plot_grid(f3_top, f3_bottom, nrow=2)
# Distributions of some values
exones_gc_dist <- ggplot(exo_data, aes(x=GC)) + theme_bw() +
geom_histogram(bins = 20, col='black', fill='gray')
plot_grid(exones_gc_dist, exones_gc_dist, nrow=2)
exones_mcov <- ggplot(exo_data, aes(x=MCOV_agilent)) + theme_bw() +
geom_histogram(bins = 20, col='black', fill='gray') +
scale_x_continuous(limits=c(0, 2))
print(exones_mcov)
genomes_mcov <- ggplot(exo_data, aes(x=MCOV_wgs)) + theme_bw() +
geom_histogram(bins = 20, col='black', fill='gray') +
scale_x_continuous(limits=c(0, 2))
plot_grid(exones_mcov, genomes_mcov, nrow=2)
genomes_mf <- ggplot(exo_data, aes(x=MF_wgs)) + theme_bw() +
geom_histogram(bins = 20, col='black', fill='gray') +
scale_x_continuous(limits=c(-0.1, 1))
print(genomes_mf)
plot_grid(genomes_mf, genomes_mf, nrow=2)
# Other stratifications
strata_to_compare = list('GRCh37_AllTandemRepeats_201to10000bp_slop5',
c('GRCh37_proteincoding_plus100.bed.gz',
'GRCh37_proteincoding_plus50.bed.gz',
'GRCh37_proteincoding_plus25.bed.gz',
'GRCh37_HG001_GIABv3.3.2_complexindel10bp_slop50.bed.gz',
'*'))
for (i in strata_to_compare){
piece_of_sheet = bdata[bdata$Filter == 'PASS' &
bdata$Subtype == '*' &
bdata$TRUTH.TOTAL > 10 &
bdata$Subset %in% c(i, 'GRCh37_proteincoding_only.bed.gz'), ]
if (length(unique(piece_of_sheet$Subset)) == 1) {
print(paste('Stratum', i, 'uselsss'))
next
}
p <- ggplot(piece_of_sheet, aes(x=Type, y=METRIC.F1_Score, fill=Subset)) +
geom_boxplot() + scale_y_continuous(limits=c(0, 1)) + theme_bw() +
theme(panel.grid=element_blank(), legend.position='top') + #guides(fill=F) +
# geom_label(data=generate_label_df(0.1), aes(label=Label),
# position=position_dodge(width=0.75), size=2.25) +
ylab('F1 Score (SNP)') +
scale_fill_simpsons() +
facet_grid(vars(Aligner), vars(CallerFilter))
print(p)
}
strata_to_compare = list('GRCh37_HG001_GIABv3.3.2_complexindel10bp_slop50.bed.gz')
for (i in strata_to_compare){
piece_of_sheet = bdata[bdata$Filter == 'PASS' &
bdata$Subtype == '*' &
bdata$TRUTH.TOTAL > 10 &
bdata$Subset %in% c(i, '*') &
bdata$Type == 'INDEL', ]
if (length(unique(piece_of_sheet$Subset)) == 1) {
print(paste('Stratum', i, 'uselsss'))
next
}
p <- ggplot(piece_of_sheet, aes(x=ExperimentType, y=METRIC.F1_Score, fill=Subset)) +
geom_boxplot() + theme_bw() +
theme(panel.grid=element_blank(), legend.position='top') + #guides(fill=F) +
# geom_label(data=generate_label_df(0.1), aes(label=Label),
# position=position_dodge(width=0.75), size=2.25) +
ylab('F1 Score (SNP)') +
scale_fill_simpsons() +
facet_grid(vars(Aligner), vars(CallerFilter))
print(p)
}
########## SI - precision and recall pairwise comparison
## Bowtie2 local vs e2e
bowties <- bdata[bdata$Subset == "GRCh37_proteincoding_only.bed.gz" &
bdata$Subtype == "*", ]
bowties <- bowties[bowties$Aligner %in% c('BT-E2E', 'BT-LOC'), ]
bo_snp_pass <- bowties[bowties$Type == 'SNP' & bowties$Filter == 'PASS', ]
bo_indel_pass <- bowties[bowties$Type == 'INDEL' & bowties$Filter == 'PASS', ]
bo_sp <- ggplot(bo_snp_pass, aes(x=Aligner, y=METRIC.F1_Score, fill=CallerFilter)) +
geom_boxplot() + scale_y_continuous(limits=c(0.4, 1)) + theme_bw() +
theme(panel.grid=element_blank()) + guides(fill=F) +
ylab('F1 Score (SNP)') +
scale_fill_brewer(palette='Set3')
bo_sp
bo_ip <- ggplot(bo_indel_pass, aes(x=Aligner, y=METRIC.F1_Score, fill=CallerFilter)) +
geom_boxplot() + scale_y_continuous(limits=c(0.4, 1)) + theme_bw() +
theme(panel.grid=element_blank()) + guides(fill=F) +
ylab('F1 Score (INDEL)') +
scale_fill_brewer(palette='Set3')
bt_box <- plot_grid(bo_sp, bo_ip, nrow=2)
bt_box
aligner_pairwise_bt = matrix(0, nrow=2, ncol=2)
rownames(aligner_pairwise_bt) = unique(bo_snp_pass$Aligner)
colnames(aligner_pairwise_bt) = unique(bo_snp_pass$Aligner)
pvals_bt = matrix("", nrow=2, ncol=2)
rownames(pvals_bt) = unique(bo_snp_pass$Aligner)
colnames(pvals_bt) = unique(bo_snp_pass$Aligner)
for (i in unique(bo_snp_pass$Aligner)) {
for (j in unique(bo_snp_pass$Aligner)) {
left = as.numeric(bo_snp_pass[bo_snp_pass$Aligner == i, 'METRIC.F1_Score'])
right = as.numeric(bo_snp_pass[bo_snp_pass$Aligner == j, 'METRIC.F1_Score'])
difference = median(left - right)
if (i == j){
pvals_bt[i, j] = ""
} else {
pval = wilcox.test(left, right, paired=T)$p.value
aligner_pairwise_bt[i, j] = difference
pvals_bt[i, j] = ifelse(pval < 0.001, "***", ifelse(pval < 0.01, "**",
ifelse(pval < 0.05, "*", "")))
}
}
}
snp_bt <- drawCoolHM(aligner_pairwise_bt, pvals_bt)
print(snp_bt)
aligner_pairwise_bt_id = matrix(0, nrow=2, ncol=2)
rownames(aligner_pairwise_bt_id) = unique(bo_snp_pass$Aligner)
colnames(aligner_pairwise_bt_id) = unique(bo_snp_pass$Aligner)
pvals_bt_id = matrix("", nrow=2, ncol=2)
rownames(pvals_bt_id) = unique(bo_snp_pass$Aligner)
colnames(pvals_bt_id) = unique(bo_snp_pass$Aligner)
for (i in unique(bo_indel_pass$Aligner)) {
for (j in unique(bo_indel_pass$Aligner)) {
left = as.numeric(bo_indel_pass[bo_indel_pass$Aligner == i, 'METRIC.F1_Score'])
right = as.numeric(bo_indel_pass[bo_indel_pass$Aligner == j, 'METRIC.F1_Score'])
difference = median(left - right)
if (i == j){
pvals_bt_id[i, j] = ""
} else {
pval = wilcox.test(left, right, paired=T)$p.value
aligner_pairwise_bt_id[i, j] = difference
pvals_bt_id[i, j] = ifelse(pval < 0.001, "***", ifelse(pval < 0.01, "**",
ifelse(pval < 0.05, "*", "")))
}