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Copy path2.0_MafSummary_TMB_calculation.R
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2.0_MafSummary_TMB_calculation.R
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library(Cairo)
library(ggthemes)
library(ggpubr)
library(magrittr)
library(ggsignif)
library(readxl)
library(tidyverse)
library(rstatix)
dir= './picture'
{ # MafSummary
se_gene = getGeneSummary(clin.EOCRC)[1:50]$Hugo_Symbol
CairoPDF(paste(dir,'/2_oncoplot_Cairo.pdf',sep = ""), width = 12, height = 6)
oncoplot(maf = rt_panel_common_genes,
top = 30,
fontSize = 0.6,
showTumorSampleBarcodes = F,
sepwd_samples = 0,
clinicalFeatures = c('Age_class','Country'),
bgCol = "#FFFFFF",
sortByAnnotation = TRUE,
groupAnnotationBySize = FALSE)
dev.off()
}
{
#EOCRC_TMB_calculation
TMB_EOCRC_mutload <- left_join(getSampleSummary(clin.EOCRC)[, .(Tumor_Sample_Barcode,total)],
[email protected][,.(Number,Tumor_Sample_Barcode,Panel)],
by='Tumor_Sample_Barcode')
TMB_EOCRC_mutload <- left_join(TMB_EOCRC_mutload,capture_size,by = 'Panel')
table(is.na(TMB_EOCRC_mutload$Panel))
TMB_EOCRC_mutload$TMB <- (TMB_EOCRC_mutload$total) / TMB_EOCRC_mutload$total_size
TMB_EOCRC_mutload$logTMB <- log10(TMB_EOCRC_mutload$TMB)
TMB_EOCRC_mutload = TMB_EOCRC_mutload[order(TMB, decreasing = FALSE)]
TMB_EOCRC = TMB_EOCRC_mutload$logTMB
TMB_EOCRC_hyper_sample = TMB_EOCRC_mutload$Tumor_Sample_Barcode[TMB_EOCRC_mutload$TMB > 15]
TMB_EOCRC_nonhyper_sample = TMB_EOCRC_mutload$Tumor_Sample_Barcode[TMB_EOCRC_mutload$TMB <= 15]
#LOCRC_TMB_calculation
TMB_LOCRC_mutload <- left_join(getSampleSummary(clin.LOCRC)[, .(Tumor_Sample_Barcode,total)],
[email protected][,.(Number,Tumor_Sample_Barcode,Panel)],
by='Tumor_Sample_Barcode')
TMB_LOCRC_mutload <- left_join(TMB_LOCRC_mutload,capture_size,by = 'Panel')
table(is.na(TMB_LOCRC_mutload$Panel))
TMB_LOCRC_mutload$TMB <- (TMB_LOCRC_mutload$total) / TMB_LOCRC_mutload$total_size
TMB_LOCRC_mutload$logTMB <- log10(TMB_LOCRC_mutload$TMB)
TMB_LOCRC_mutload = TMB_LOCRC_mutload[order(TMB, decreasing = FALSE)]
TMB_LOCRC = TMB_LOCRC_mutload$logTMB
TMB_LOCRC_hyper_sample = TMB_LOCRC_mutload$Tumor_Sample_Barcode[TMB_LOCRC_mutload$TMB > 15]
TMB_LOCRC_nonhyper_sample = TMB_LOCRC_mutload$Tumor_Sample_Barcode[TMB_LOCRC_mutload$TMB <= 15]
TMB_mutload = rbind(TMB_LOCRC_mutload,TMB_EOCRC_mutload) %>%
left_join([email protected][,.(Age,Age_class,Country,Center,
Sex,Race,Tumor_site,
Histology_type,Sample_type,Tumor_Sample_Barcode)]
,by='Tumor_Sample_Barcode')
#MSS
# TMB_mutload_model = lm(logTMB~Age_class+Panel+Sex+Tumor_site+Histology_type,
# data = TMB_mutload)
TMB_mutload_model = lm(logTMB~Age_class+Panel+Race+Sex+Tumor_site+Sample_type+Histology_type,
data = TMB_mutload)
TMB_mutload_regression = TMB_mutload %>%
mutate(residuals = residuals(TMB_mutload_model))
write.csv(TMB_mutload,file = './table/2.0_TMB_mutload.csv')
}
{
hist((TMB_EOCRC), breaks = 200,plot=T)
hist((TMB_LOCRC), breaks = 200,plot=T)
}
palette <- c('#deebf7','#3182bd')
{#TMB_across_panels_for_EOandLO
picture_acorss_panels <-
ggplot(TMB_mutload, aes(x = Age_class, y = logTMB, fill = Age_class)) +
geom_boxplot(outlier.shape = T, width = 0.5) +
facet_wrap(~ Panel, scales = "free_y",nrow = 3) +
theme_few() +
scale_fill_manual(values = palette) +
theme(
axis.text.x = element_text(color = "black", size = 10, angle = 45,hjust = 1),
axis.text.y = element_text(color = "black", size = 10, angle = 0),
axis.title.x = element_text(color = "black", size = 14, angle = 0),
axis.title.y = element_text(color = "black", size = 14, angle = 90),
legend.position = 'right',
axis.line.y = element_line(color = "black", linetype = "solid"),
axis.line.x = element_line (color = "black",linetype = "solid"),
panel.border = element_rect(linetype = "solid", linewidth = 1.2,fill = NA),
strip.text = element_text(size = 8, color = "black", face = "bold")
)
}
pdf(file = './picture/2.TMB_across_panels_for_EOandLO.pdf',width = 22,height = 6)
picture_acorss_panels
dev.off()
{
#nonhyper regression_TMB
nonhyper_regression_TMB = TMB_mutload_regression %>%
filter(TMB<=15) %>%
mutate(Age_class = factor(Age_class,levels = c('LOCRC','EOCRC')))
picture_nonhyper_regression <- ggplot(nonhyper_regression_TMB,
aes(x = Age_class,
y = residuals, fill = Age_class)) +
geom_boxplot(outlier.shape = T,
width =0.5) +
scale_fill_manual(values = palette)+
scale_y_continuous(labels = scales::number_format(accuracy = 0.05),
limits = c(-2,1.5)) +
theme_pubr() +
labs(title = "TMB_EOCRC_vs_LOCRC",
subtitle = "Non-hypermutated",
x = "",
y = "adjusted TMB(residuals)") +
geom_signif(comparisons = list(c("EOCRC", "LOCRC")),
y_position =1.3,
tip_length =0.02,
test = wilcox.test,
textsize = 5) +
theme(
plot.title = element_text(color = "black", size = 16, hjust = 0.5),
plot.subtitle = element_text(color = "black", size = 16,hjust = 0.5),
plot.caption = element_text(color = "black", size = 16,face = "italic", hjust = 1),
axis.text.x = element_text(color = "black", size = 16, angle = 0),
axis.text.y = element_text(color = "black", size = 16, angle = 0),
axis.title.x = element_text(color = "black", size = 16, angle = 0),
axis.title.y = element_text(color = "black", size = 16, angle = 90),
legend.position = "none",
axis.line.y = element_line(color = "black", linetype = "solid"),
axis.line.x = element_line (color = "black",linetype = "solid"),
panel.border = element_rect(linetype = "solid", linewidth = 1.2,fill = NA)
)
}
pdf(file = './picture/2.regression_nonhyepr_TMB.pdf',
width = 4.5,height = 5.5)
picture_nonhyper_regression
dev.off()
{
#hyper regression_TMB
hyper_regression_TMB = TMB_mutload_regression %>%
filter(TMB>15) %>%
mutate(Age_class = factor(Age_class,levels = c('LOCRC','EOCRC')))
picture_hyper_regression <- ggplot(hyper_regression_TMB,
aes(x = Age_class,
y = residuals, fill = Age_class)) +
geom_boxplot(outlier.shape = T,
width =0.5) +
scale_fill_manual(values = palette)+
scale_y_continuous(labels = scales::number_format(accuracy = 0.05),
limits = c(-0.1,2.8)) +
theme_pubr() +
labs(title = "TMB_EOCRC_vs_LOCRC",
subtitle = "Hypermutated",
x = "",
y = "adjusted TMB(residuals)") +
geom_signif(comparisons = list(c("EOCRC", "LOCRC")),
y_position = 2.6,
tip_length = 0.02,
test = wilcox.test,
textsize = 5) +
theme(
plot.title = element_text(color = "black", size = 16, hjust = 0.5),
plot.subtitle = element_text(color = "black", size = 16,hjust = 0.5),
plot.caption = element_text(color = "black", size = 16,face = "italic", hjust = 1),
axis.text.x = element_text(color = "black", size = 16, angle = 0),
axis.text.y = element_text(color = "black", size = 16, angle = 0),
axis.title.x = element_text(color = "black", size = 16, angle = 0),
axis.title.y = element_text(color = "black", size = 16, angle = 90),
legend.position = "none",
axis.line.y = element_line(color = "black", linetype = "solid"),
axis.line.x = element_line (color = "black",linetype = "solid"),
panel.border = element_rect(linetype = "solid", linewidth = 1.2,fill = NA)
)
}
pdf(file = './picture/2.regression_hyepr_TMB.pdf',
width = 4.5,height = 5.5)
picture_hyper_regression
dev.off()
regression_EO_vs_LO_data <- data.frame(
TMB_Status = c('Hyper','Non-hyper'),
EO_number = c(nrow(hyper_regression_TMB[Age_class == 'EOCRC']),nrow(nonhyper_regression_TMB[Age_class == 'EOCRC'])),
LO_number = c(nrow(hyper_regression_TMB[Age_class == 'LOCRC']),nrow(nonhyper_regression_TMB[Age_class == 'LOCRC'])),
pval = c(wilcox.test(hyper_regression_TMB[Age_class == 'LOCRC']$residuals,
hyper_regression_TMB[Age_class == 'EOCRC']$residuals)$p.value,
wilcox.test(nonhyper_regression_TMB[Age_class == 'LOCRC']$residuals,
nonhyper_regression_TMB[Age_class == 'EOCRC']$residuals)$p.value ),
EO_mean = c(summary(hyper_regression_TMB[Age_class == 'EOCRC']$residuals)['Mean'],
summary(nonhyper_regression_TMB[Age_class == 'EOCRC']$residuals)['Mean']),
LO_mean = c(summary(hyper_regression_TMB[Age_class == 'LOCRC']$residuals)['Mean'],
summary(nonhyper_regression_TMB[Age_class == 'LOCRC']$residuals)['Mean']),
EO_median = c(summary(hyper_regression_TMB[Age_class == 'EOCRC']$residuals)['Median'],
summary(nonhyper_regression_TMB[Age_class == 'EOCRC']$residuals)['Median']),
LO_median = c(summary(hyper_regression_TMB[Age_class == 'LOCRC']$residuals)['Median'],
summary(nonhyper_regression_TMB[Age_class == 'LOCRC']$residuals)['Median']),
Effect_size = c(cliff.delta( hyper_regression_TMB[Age_class == 'EOCRC']$residuals,
hyper_regression_TMB[Age_class == 'LOCRC']$residuals)$estimate,
cliff.delta(nonhyper_regression_TMB[Age_class == 'EOCRC']$residuals,
nonhyper_regression_TMB[Age_class == 'LOCRC']$residuals)$estimate),
Magnitude = c( as.character(cliff.delta( hyper_regression_TMB[Age_class == 'EOCRC']$residuals,
hyper_regression_TMB[Age_class == 'LOCRC']$residuals)$magnitude),
as.character(cliff.delta( nonhyper_regression_TMB[Age_class == 'EOCRC']$residuals,
nonhyper_regression_TMB[Age_class == 'LOCRC']$residuals)$magnitude)
)
)
write_csv(regression_EO_vs_LO_data,file = './table/2.0_regression_EO_vs_LO_data.csv')
{#TMB_mutload_worldwide
TMB_mutload_binded = TMB_mutload %>%
dplyr::arrange(desc(TMB)) %>%
mutate( order = c(1:length(Tumor_Sample_Barcode)) )
palette = c('black')
last_total_perMB_15 <- TMB_mutload_binded %>%
filter(TMB == 15) %>%
slice_head(n = 1)
if (nrow(last_total_perMB_15) == 0) {
closest_above <- TMB_mutload_binded %>%
filter(TMB > 15) %>%
slice_tail(n = 1)
closest_below <- TMB_mutload_binded %>%
filter(TMB < 15) %>%
slice_head(n = 1)
if (nrow(closest_above) > 0 & nrow(closest_below) > 0) {
x_position <- mean(c(closest_above$order, closest_below$order))
} else if (nrow(closest_above) > 0) {
x_position <- closest_above$order
} else if (nrow(closest_below) > 0) {
x_position <- closest_below$order
} else {
x_position <- NA
}
} else {
x_position <- last_total_perMB_15$order
}
ggplot(TMB_mutload_binded,aes(x=order,y=logTMB))+
geom_point() +
geom_hline(yintercept = log10(15),
linetype=5,
lwd=1,
color='red')+
geom_vline(xintercept = x_position,
linetype=5,
color='red',
lwd=1) +
theme_few()+
labs(title = "Tumor Mutation Burden Distribution",
x = "",
y = "log10(TMB)") +
theme(
plot.title = element_text(color = "black", size = 14, hjust = 0.5),
plot.caption = element_text(color = "black", size = 12,face = "italic", hjust = 1),
axis.text.y = element_text(color = "black", size = 12, angle = 0),
axis.title.y = element_text(color = "black", size = 12, angle = 90),
axis.line.y = element_line(color = "black", linetype = "solid"),
axis.line.x = element_line (color = "black",linetype = "solid"),
panel.border = element_rect(linetype = "solid", linewidth = 1.2,fill = NA) )
ggsave(filename ='./picture/2_TMB_binded.pdf',
width = 7,height = 4.7,dpi = 300)
}
{#TMB_mutload_each_country
plot_tmb_distribution <- function(country_name, rt_clinical_data, TMB_mutload) {
country_tmb_sample <- rt_clinical_data$Tumor_Sample_Barcode[rt_clinical_data$Country == country_name]
country_tmb <- TMB_mutload[TMB_mutload$Tumor_Sample_Barcode %in% country_tmb_sample,]
country_tmb <- country_tmb %>% dplyr::arrange(desc(TMB))
country_tmb$order <- 1:nrow(country_tmb)
last_total_perMB_15 <- country_tmb %>%
filter(TMB == 15) %>%
slice_head(n = 1)
if (nrow(last_total_perMB_15) == 0) {
closest_above <- country_tmb %>%
filter(TMB >15) %>%
slice_tail(n = 1)
closest_below <- country_tmb %>%
filter(TMB < 15) %>%
slice_head(n = 1)
if (nrow(closest_above) > 0 & nrow(closest_below) > 0) {
x_position <- mean(c(closest_above$order, closest_below$order))
} else if (nrow(closest_above) > 0) {
x_position <- closest_above$order
} else if (nrow(closest_below) > 0) {
x_position <- closest_below$order
} else {
x_position <- NA
}
} else {
x_position <- last_total_perMB_15$order
}
ggplot(country_tmb, aes(x = order, y = logTMB)) +
geom_point() +
geom_hline(yintercept = log10(15), linetype = 5, lwd = 1, color = 'red') +
geom_vline(xintercept = x_position, linetype = 5, color = 'red', lwd = 1) +
theme_few() +
labs(title = paste("Tumor Mutation Burden Distribution in", country_name),
x = "",
y = "log10(TMB)"
) +
theme(
plot.title = element_text(color = "black", size = 14, hjust = 0.5),
plot.caption = element_text(color = "black", size = 12, face = "italic", hjust = 1),
axis.text.x = element_blank(),
axis.text.y = element_text(color = "black", size = 12, angle = 0),
axis.title.y = element_text(color = "black", size = 12, angle = 90),
axis.line.y = element_line(color = "black", linetype = "solid"),
axis.line.x = element_line(color = "black", linetype = "solid"),
panel.border = element_rect(linetype = "solid", linewidth = 1.2, fill = NA)
)
ggsave(filename = paste(dir,'/2_',country_name,'_TMB_distribution.pdf',sep = ''),width = 6,height = 4,dpi = 300)
}
plot_tmb_distribution("Canada", [email protected], TMB_mutload)
plot_tmb_distribution("US", [email protected], TMB_mutload)
plot_tmb_distribution("China", [email protected], TMB_mutload)
plot_tmb_distribution("Nigeria", [email protected], TMB_mutload)
plot_tmb_distribution("Netherlands", [email protected], TMB_mutload)
plot_tmb_distribution("Spain", [email protected], TMB_mutload)
plot_tmb_distribution("Korea", [email protected], TMB_mutload)
plot_tmb_distribution("France", [email protected], TMB_mutload)
}