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3a_Volcano_age_eo.R
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3a_Volcano_age_eo.R
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# Author: Marcos Diaz-Gay
# Date: Aug 11, 2024
# RStudio
library(tidyverse)
library(scales)
library(ggrepel)
library(broom)
library(logistf)
###################
status = 'MSS'
# status = 'MSI'
# variable = 'age_diag'
variable = 'age_eo'
###################
if (variable == 'age_diag') {
title = 'Age of onset enrichment'
subtitle = 'Adjusted by sex, country, and tumor subsite'
}
if (variable == 'age_eo') {
title = 'Age of onset enrichment'
subtitle = 'Adjusted by sex, country, and tumor subsite'
}
met = read.delim('../data_for_figures/Metadata_TMB_Signatures_SBS_ID_DBS_CN_SV_Drivers_2024AUG21.tsv')
df = met %>% filter(Status == status)
all_sigsint = grep('_c$', colnames(met), value = T)
sigsint = all_sigsint[colSums(apply(df[,all_sigsint],2,is.na))!=nrow(df)]
sigsint = sigsint[!str_detect(sigsint, 'Unknown')]
df$sex = factor(df$sex)
df$sex = relevel(df$sex, 'Male')
df$tumorsite_group = factor(df$tumorsite_group)
df$tumorsite_group = relevel(df$tumorsite_group, 'Proximal colon')
df$country = factor(df$country, levels = c('Brazil', 'Iran',
'Colombia', 'Thailand',
'Argentina', 'Russia',
'Canada', 'Poland',
'Czech Republic', 'Serbia',
'Japan'))
df$age_eo = factor(df$age_eo)
df$age_eo = relevel(df$age_eo, '0-49')
# Dichotomizing cases over 70% prevalence by above/below median
dfmodel = as_tibble(df[,c('donor_id',sigsint)])
threshold = 0.7
prevalence = (df %>%
summarise(across(all_of(sigsint),~sum(. > 0))) / nrow(df))
sv_sigs = grep('SV', sigsint, value = T)
cn_sigs = grep('CN', sigsint, value = T)
prevalence[,cn_sigs] = df %>%
summarise(across(all_of(cn_sigs),~sum(. > 0, na.rm = T))) / sum(!is.na(df$CN1_c))
prevalence[,sv_sigs] = df %>%
summarise(across(all_of(sv_sigs),~sum(. > 0, na.rm = T))) / sum(!is.na(df$SV3_c))
sigs_high_prev = names(prevalence)[prevalence > threshold]
sigs_low_prev = names(prevalence)[prevalence <= threshold]
dfmodel_low_prev = dfmodel[,c('donor_id',sigs_low_prev)]
dfmodel_low_prev[,sigs_low_prev] = dfmodel_low_prev[,sigs_low_prev]>0
dfmodel_high_prev = dfmodel[,c('donor_id',sigs_high_prev)]
dfmodel_high_prev[,sigs_high_prev] = dfmodel_high_prev[,sigs_high_prev] %>%
mutate(across(all_of(sigs_high_prev),~(. > median(., na.rm=T))))
dfmodel = cbind(dfmodel_high_prev, dfmodel_low_prev[,-1])
covdata = df %>% select(donor_id, age_eo, sex, tumorsite_group, country, purity)
number_sigs_by_vartype = tibble(name = sigsint) %>%
mutate(vartype = substr(name,1,2)) %>%
group_by(vartype) %>%
summarise(n_sigs_vartype = n())
testdata <- dfmodel %>%
pivot_longer(-donor_id) %>%
left_join(covdata) %>%
group_by(name) %>%
mutate(value=as.factor(value)) %>%
do(tresult = safely(stats::glm)(value ~ age_eo + sex + country + tumorsite_group + purity,family = binomial(),data=.)) %>%
mutate(tresult_null = map_lgl(tresult['result'], is.null)) %>%
filter(!tresult_null)
testdata = testdata %>%
mutate(fit = list(tidy(tresult[['result']],exponentiate=TRUE)))
testdata = testdata %>%
select(name,fit) %>%
unnest(cols = c(fit)) %>%
ungroup() %>%
mutate(vartype = substr(name,1,2)) %>%
left_join(number_sigs_by_vartype) %>%
filter(str_detect(term,variable)) %>%
mutate(label = paste0(str_replace(term,variable,''), ' ',
# mutate(label = paste0(country_to_test, ' ',
str_replace(name,'_c','')),
signature = str_replace(name,'_c',''),
# country = country_to_test
) %>%
rename(p_val = p.value,
independent_vars = term,
OR = estimate
) %>%
# select(independent_vars, signature, OR, p_val, vartype, label, country)
select(independent_vars, signature, OR, p_val, vartype, label)
testdata_GLM = testdata
# New testdata using the Firth method
testdata <- dfmodel %>%
pivot_longer(-donor_id) %>%
left_join(covdata) %>%
group_by(name) %>%
mutate(value=as.factor(value)) %>%
do(tresult = safely(logistf::logistf)(value ~ age_eo + sex + country + tumorsite_group + purity,
data=., control = logistf.control(maxit = 1000))) %>%
mutate(tresult_null = map_lgl(tresult['result'], is.null)) %>%
filter(!tresult_null)
testdata$pre_fit = lapply(testdata$tresult,'[[','result')
list_to_testdata_fit = list()
for (j in 1:length(testdata$pre_fit)){
list_to_testdata_fit[[j]] = tibble(
term = testdata$pre_fit[[j]]$terms,
estimate = exp(testdata$pre_fit[[j]]$coefficients),
p.value = testdata$pre_fit[[j]]$prob
)
}
testdata$fit = list_to_testdata_fit
testdata = testdata %>%
select(name,fit) %>%
unnest(cols = c(fit)) %>%
ungroup() %>%
mutate(vartype = substr(name,1,2)) %>%
left_join(number_sigs_by_vartype) %>%
filter(str_detect(term,variable)) %>%
mutate(label = paste0(str_replace(term,variable,''), ' ',
# mutate(label = paste0(country_to_test, ' ',
str_replace(name,'_c','')),
signature = str_replace(name,'_c',''),
# country = country_to_test
) %>%
rename(p_val = p.value,
independent_vars = term,
OR = estimate
) %>%
# select(independent_vars, signature, OR, p_val, vartype, label, country)
select(independent_vars, signature, OR, p_val, vartype, label)
testdata_FIRTH = testdata
# countries_ordered = unique(testdata_GLM$country)
signatures_ordered = unique(testdata_GLM$signature)
complete_separation = NULL
# for (i in 1:length(countries_ordered)){
# country = countries_ordered[i]
# print(country)
for (j in 1:length(signatures_ordered)){
signature = signatures_ordered[j]
print(signature)
if (paste0(signature,'_c') %in% c(sigs_high_prev)){
is_complete_separation = as.logical(
length(
which(table(df[,paste0(signature,'_c')]>median(df[,paste0(signature,'_c')], na.rm = T),
df$age_eo)<=0)
) + length(
which(table(df[,paste0(signature,'_c')]>median(df[,paste0(signature,'_c')], na.rm = T),
df$country)<=0)
) +
length(
which(table(df[,paste0(signature,'_c')]>median(df[,paste0(signature,'_c')], na.rm = T),
df$sex)<=0)
) +
length(
which(table(df[,paste0(signature,'_c')]>median(df[,paste0(signature,'_c')], na.rm = T),
df$tumorsite_group)<=0)
)
)
} else {
is_complete_separation = as.logical(
length(
which(table(df[,paste0(signature,'_c')]>0,
df$age_eo)<=0)
) +
length(
which(table(df[,paste0(signature,'_c')]>0,
df$country)<=0)
) +
length(
which(table(df[,paste0(signature,'_c')]>0,
df$sex)<=0)
) +
length(
which(table(df[,paste0(signature,'_c')]>0,
df$tumorsite_group)<=0)
)
)
}
complete_separation = rbind(complete_separation,
c(#as.character(country),
signature, is_complete_separation))
}
# }
complete_separation = tibble(data.frame(complete_separation))
colnames(complete_separation) = c(#'country',
'signature', 'complete_separation')
testdata = NULL
model_used = NULL
for (i in 1:nrow(testdata_GLM)){
# country_glm = testdata_GLM$country[i]
# country_firth = testdata_FIRTH$country[i]
# if (country_firth == country_glm) {
# country = as.character(country_glm)
# } else {
# warning('The order of testdata data frames is different!')
# }
# print(country)
sig_glm = testdata_GLM$signature[i]
sig_firth = testdata_FIRTH$signature[i]
if (sig_firth == sig_glm) {
sig = sig_glm
} else {
warning('The order of testdata data frames is different!')
}
print(sig)
is_cs = complete_separation$complete_separation[
which(#complete_separation$country == country &
complete_separation$signature == sig)
]
if (is_cs) {
testdata = rbind(testdata, testdata_FIRTH[i,])
model_used = c(model_used, "Firth's Bias-Reduced Logistic Regression")
} else {
testdata = rbind(testdata, testdata_GLM[i,])
model_used = c(model_used, "Regular Logistic Regression")
}
}
testdata$model_used = model_used
# Only for age (using OR for 10 years)
# if (variable == 'age_diag'){
# testdata$OR = exp(testdata$OR * 10)
# }
# if (variable == 'age_eo'){
# testdata$OR = exp(testdata$OR)
# }
# Volcano plot LR
testdata = testdata %>%
arrange(signature) %>%
mutate(q_val = p.adjust(p_val, method='fdr')) %>%
mutate(q_val_2 = c(p.adjust(p_val[which(str_detect(signature, 'CN'))],
method = 'fdr'),
p.adjust(p_val[which(str_detect(signature, 'DBS'))],
method = 'fdr'),
p.adjust(p_val[which(str_detect(signature, 'ID'))],
method = 'fdr'),
p.adjust(p_val[which(str_detect(signature, 'SBS'))],
method = 'fdr'),
p.adjust(p_val[which(str_detect(signature, 'SV'))],
method = 'fdr')
)
)
complete_testdata = testdata
testdata$OR[testdata$OR>1e5] = Inf
testdata$OR[testdata$OR<1e-5] = 0
testdata$q_val[testdata$q_val<1e-6] = 1e-6
testdata$q_val_2[testdata$q_val_2<1e-6] = 1e-6
if (status == 'MSS'){
testdata$signature[testdata$signature=='SBS288F_MSS'] = 'SBS_F'
testdata$signature[testdata$signature=='SBS288H_MSS'] = 'SBS_H'
testdata$signature[testdata$signature=='SBS288M_MSS'] = 'SBS_M'
testdata$signature[testdata$signature=='SBS288O_MSS'] = 'SBS_O'
testdata$signature[testdata$signature=='ID83J_MSS'] = 'ID_J'
testdata$signature[testdata$signature=='CN68F_MSS'] = 'CN_F'
testdata$signature[testdata$signature=='SV38B_MSS'] = 'SV_B'
testdata$signature[testdata$signature=='SV38D_MSS'] = 'SV_D'
} else {
testdata$signature[testdata$signature=='SBS288I_MSI'] = 'SBS_MSI_I'
testdata$signature[testdata$signature=='SBS288M_MSI'] = 'SBS_MSI_M'
testdata$signature[testdata$signature=='SBS288N_MSI'] = 'SBS_MSI_N'
testdata$signature[testdata$signature=='SBS288O_MSI'] = 'SBS_MSI_O'
testdata$signature[testdata$signature=='DBS78B_MSI'] = 'DBS_MSI_B'
testdata$signature[testdata$signature=='DBS78F_MSI'] = 'DBS_MSI_F'
}
testdata %>%
mutate(
col_or = ifelse(OR<1, 'Enriched in early-onset patients',
'Enriched in late-onset patients'),
col_or = ifelse(q_val_2<0.05, col_or, NA)) %>%
mutate(vartype = str_replace(vartype, 'SB', 'SBS'),
vartype = str_replace(vartype, 'DB', 'DBS'),
vartype = str_replace(vartype, 'ID', 'ID'),
vartype = str_replace(vartype, 'CN', 'CN'),
vartype = str_replace(vartype, 'SV', 'SV'),
vartype = factor(vartype, levels = c('SBS',
'DBS',
'ID',
'CN',
'SV'))) %>%
ggplot() +
aes(x = log2(OR), y = -log10(q_val_2), col = col_or) +
facet_grid(.~vartype, ) +
geom_point(size = 4) +
geom_hline(yintercept = -log10(0.05),
linetype = 'dashed', col = 'orange') +
geom_hline(yintercept = -log10(0.01),
linetype = 'dashed', col = 'red') +
geom_vline(xintercept = 0, col = 'grey') +
theme_bw() +
scale_x_continuous() +
scale_y_continuous(breaks = c(0,2,4,6),
labels = c('0','2','4','>6')) +
scale_color_manual(breaks = c('Enriched in early-onset patients',
'Enriched in late-onset patients'
),
values = c('#8C3FC0','#73C03F'),
na.value = 'grey80') +
geom_text_repel(data = . %>%
mutate(signature = ifelse(q_val_2 < 0.05, signature, '')),
aes(label = signature), size = 5, seed = 2, nudge_y = 0.4) +
{if (variable=='age_diag') labs(x = 'Odds ratio (10 years; log2)') else labs(x = 'Odds ratio (log2)')} +
labs(y = 'q-value (-log10)',
col = 'Age of onset enrichment',
subtitle = ' Adjusted by sex, country, tumor subsite, and purity') +
theme(plot.title = element_text(size = 18),
plot.subtitle = element_text(size = 14, vjust = -16),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14),
legend.title = element_text(size = 16, face ='bold'),
legend.text = element_text(size = 14),
legend.position = 'top',
legend.justification = 'left',
legend.box.spacing = unit(25,'pt'),
strip.background = element_blank(),
strip.text.x = element_text(size = 14, face = 'bold'),
panel.spacing.x = unit(1.5,'lines')
) +
guides(col = guide_legend(override.aes = list(label = ''),))
# Exported 1600 x 500
# Supplementary table
testdata = complete_testdata
testdata$signature[testdata$signature=='SBS288F_MSS'] = 'SBS_F'
testdata$signature[testdata$signature=='SBS288H_MSS'] = 'SBS_H'
testdata$signature[testdata$signature=='SBS288M_MSS'] = 'SBS_M'
testdata$signature[testdata$signature=='SBS288O_MSS'] = 'SBS_O'
testdata$signature[testdata$signature=='ID83J_MSS'] = 'ID_J'
testdata$signature[testdata$signature=='CN68F_MSS'] = 'CN_F'
testdata$signature[testdata$signature=='SV38B_MSS'] = 'SV_B'
testdata$signature[testdata$signature=='SV38D_MSS'] = 'SV_D'
signature_order = gtools::mixedsort(unique(testdata$signature))
signature_order = signature_order[c(which(str_detect(signature_order, 'SBS')),
which(str_detect(signature_order, 'DBS')),
which(str_detect(signature_order, 'ID')),
which(str_detect(signature_order, 'CN')),
which(str_detect(signature_order, 'SV')))]
testdata_to_st = testdata %>%
mutate(#country = factor(country, levels = c(country_order)),
signature = factor(signature,levels = signature_order)) %>%
arrange(signature,#country
) %>%
select(signature, #country,
OR, p_val, q_val_2, model_used) %>%
rename(#Country = country,
Signature = signature,
'p-value' = p_val,
'q-value' = q_val_2,
'Model' = model_used)
write.csv(testdata_to_st, '../supplementary_tables_regressions/ST19.csv', quote=F, row.names=F)