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survival_analysis.R
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library(ape)
library(phangorn)
library(BactDating)
library(survival)
library(RColorBrewer)
library(survminer)
library(survutils)
library(ggtree)
source ('~/Desktop/PhD/TB/pre_resistance/drug_resistance_phylogeny/scripts/final_scripts/tree_functions.R')
source ('~/Desktop/PhD/TB/pre_resistance/drug_resistance_phylogeny/scripts/final_scripts/survTree_functions.R')
dir_prefix = '/'
######## FILES NEEDED ##########
l2_tree_file = paste(dir_prefix, 'l2.assembly.arc.5e7.0.5.wg1_res.RData', sep = '')
l4_tree_file = paste(dir_prefix, 'l4.assembly.arc.1e7.0.5.wg2_res.RData', sep = '')
l2.anc.seq_file = paste(dir_prefix, 'l2.anc.ml.assemblyTree.RData', sep = '')
l4.anc.seq_file = paste(dir_prefix, 'l4.anc.ml.assemblyTree.RData', sep = '')
genome_idx_file = paste(dir_prefix, 'mtb.snps.dels.wg.withDR.assembly.finalSet.snpSites.masked.idx', sep = '')
tbdr_db_file = paste(dir_prefix, 'tb_profiler_db.complete.good.posStrRef.tsv', sep = '')
dna_file = paste(dir_prefix, 'dna_phyDat.assembly.RData', sep = '')
metadata_file = paste(dir_prefix, 'tb_1999_2016_noDups.metadata.csv', sep='')
###############################
######## FILES NEEDED (SAMARA) ##########
l2_tree_file = paste(dir_prefix, 'l2.samara.assembly.wg.raxml.tree', sep = '')
l4_tree_file = paste(dir_prefix, 'l4.samara.assembly.wg.raxml.tree', sep = '')
l2.anc.seq_file = paste(dir_prefix, 'l2.anc.ml.samara.assembly.RData', sep = '')
l4.anc.seq_file = paste(dir_prefix, 'l4.anc.ml.samara.assembly.RData', sep = '')
genome_idx_file = paste(dir_prefix, 'mtb.samara.snps.dels.wg.withDR.assembly.finalSet.snpSites.masked.idx', sep = '')
tbdr_db_file = paste(dir_prefix, 'tb_profiler_db.complete.good.posStrRef.tsv', sep = '')
dna_file = paste(dir_prefix, 'dna_phydat.samara.assembly.RData', sep = '')
metadata_file = paste(dir_prefix, 'samara_lineages.txt', sep='')
##########################################
###############################
#### Load Data ####
##############################
load(l2_tree_file)
l2_tree = reorder.phylo(res$tree, 'cladewise')
l2_tree$CI = res$CI
load(l4_tree_file)
l4_tree = reorder.phylo(res$tree, 'cladewise')
l4_tree$CI = res$CI
load(dna_file)
dna_phydat = dna_phydat[names(dna_phydat[which(names(dna_phydat) %in% c(l4_tree$tip.label, l2_tree$tip.label))])]
metadata = read.csv(metadata_file, stringsAsFactors = F, header = T)
metadata = metadata[!is.na(metadata$id),]
# Change names in metadata so it matchers the phylogeny
metadata$id = paste(metadata$id, '_', metadata$year_sample, sep='')
metadata = metadata[metadata$id %in% c(l4_tree$tip.label, l2_tree$tip.label),]
l2_edges = date_edges(l2_tree)
l4_edges = date_edges(l4_tree)
rif_phen = setNames(metadata$rif_final, metadata$id)
inh_phen = setNames(metadata$inh_final, metadata$id)
l4_phen_rif = rif_phen[l4_tree$tip.label]
l4_phen_inh = rif_phen[l4_tree$tip.label]
l2_phen_rif = rif_phen[l2_tree$tip.label]
l2_phen_inh = inh_phen[l2_tree$tip.label]
#############################################
#### Defining subclades ####
############################################
beijing = metadata[grepl('lineage2', metadata$snp_lineage),]
lam = metadata[grepl('lineage4.3', metadata$snp_lineage),]
lam3 = metadata[grepl('lineage4.3.2', metadata$snp_lineage),]
lam9 = metadata[grepl('lineage4.3.3', metadata$snp_lineage),]
lam11 = metadata[grepl('lineage4.3.4', metadata$snp_lineage),]
l4.3 = metadata[metadata$snp_lineage == 'lineage4.3',]
l4.3.1 = metadata[metadata$snp_lineage == 'lineage4.3.1',]
lam9_node = getMRCA(l4_tree, lam9$id)
lam9 = rbind(lam9, l4.3[l4.3$id %in% extract.clade(l4_tree, lam9_node)$tip.label,])
lam11_node = getMRCA(l4_tree, lam11$id)
lam11_node = l4_edges$V1[l4_edges$V2 == lam11_node]
lam11 = rbind(lam11, l4.3[l4.3$id %in% extract.clade(l4_tree, lam11_node)$tip.label,])
haarlem = metadata[grepl('lineage4.1.2.1', metadata$snp_lineage),]
l4.1.2 = metadata[metadata$snp_lineage == 'lineage4.1.2',]
haarlem = rbind (haarlem, l4.1.2)
x_type = metadata[grepl('lineage4.1.1', metadata$snp_lineage),]
l4.1 = metadata[metadata$snp_lineage == 'lineage4.1',]
x_type = rbind(x_type, l4.1)
l4.4 = metadata[grepl('lineage4.4', metadata$snp_lineage),]
tur = metadata[metadata$snp_lineage == 'lineage4.2.2',]
t_type = metadata[metadata$snp_lineage == 'lineage4' |
metadata$snp_lineage == 'lineage4.5' |
metadata$snp_lineage == 'lineage4.7' |
metadata$snp_lineage == 'lineage4.8' |
metadata$snp_lineage == 'lineage4.9'
,]
l2_subclades = list(beijing=beijing$id)
l4_subclades = list(lam3=lam3$id,
lam9=lam9$id,
lam11=lam11$id,
haarlem=haarlem$id,
x_type=x_type$id,
t_type=t_type$id)
l4_subclades = list(lam3=lam3$id,
lam9=lam9$id,
lam11=lam11$id,
haarlem=haarlem$id,
x_type=x_type$id,
l4.4=l4.4$id,
tur=tur$id,
t_type=t_type$id)
l2_subclades_nodes = sapply(names(l2_subclades),function(x) getMRCA(l2_tree, l2_subclades[[x]]))
l4_subclades_nodes = sapply(names(l4_subclades),function(x) getMRCA(l4_tree, l4_subclades[[x]]))
l2_subclades_tips = lapply(l2_subclades, function(x)
sapply(x, function(y) which(l2_tree$tip.label == y)))
l4_subclades_tips = lapply(l4_subclades, function(x)
sapply(x, function(y) which(l4_tree$tip.label == y)))
ggtree(tree, size=1, aes(color=group), mrsd = '2016-01-01') +
scale_color_manual(values=c('0'="grey40", col_vector[names(cls)])) +
theme_tree2() + coord_cartesian(clip = 'off') +
xlab('Year') +
theme(legend.position='left',
plot.margin = margin(0.5, 3, 0.5, 0.5, 'cm')) +
geom_cladelabel(node=getMRCA(l4_tree, tip=haarlem$id),
offset=30,offset.text=30,
label="Haarlem", align=T,barsize=2,
color=col_vector['haarlem'],fontsize=5) +
geom_cladelabel(node=getMRCA(l4_tree, tip=lam$id),
offset=30,offset.text=30,
label="LAM", align=T,barsize=2,
color=col_vector['lam11'],fontsize=5) +
geom_cladelabel(node=getMRCA(l4_tree, tip=t_type$id),
offset=30, offset.text=30,
label="T type", align=T,barsize=2,
color=col_vector['t_type'],fontsize=5) +
geom_cladelabel(node=getMRCA(l4_tree, tip=x_type$id),
offset=30,offset.text=30,
label="X type", align=T,barsize=2,
color=col_vector['x_type'],fontsize=5)
ggtree(tree, size=1, aes(color=group), mrsd = '2016-01-01') +
scale_color_manual(values=c('0'="grey40", col_vector[names(cls)])) +
theme_tree2() + coord_cartesian(clip = 'off') +
xlab('Year') +
xlim(1250, 2200) +
theme(legend.position='left',
plot.margin = margin(0.5, 3, 0.5, 0.5, 'cm')) +
geom_cladelabel(node=getMRCA(l2_tree, tip=beijing$id),
offset=20,offset.text=20,
label="Beijing", align=T,barsize=2,
color=col_vector['beijing'],fontsize=5)
## SAMARA ##
lineage2 = metadata[grepl('lineage2', metadata$snp_lineage),]
lineage4 = metadata[grepl('lineage4', metadata$snp_lineage),]
l2_subclades = list(lineage2=lineage2$id)
l4_subclades = list(lineage4=lineage4$id)
l2_subclades_nodes = sapply(names(l2_subclades),function(x) getMRCA(l2_tree, l2_subclades[[x]]))
l4_subclades_nodes = sapply(names(l4_subclades),function(x) getMRCA(l4_tree, l4_subclades[[x]]))
l2_subclades_tips = lapply(l2_subclades, function(x)
sapply(x, function(y) which(l2_tree$tip.label == y)))
l4_subclades_tips = lapply(l4_subclades, function(x)
sapply(x, function(y) which(l4_tree$tip.label == y)))
###################################################
##### Analysis using drug resistant snps #####
##################################################
###### 1. Ancestral state reconstruction ####
load (l2.anc.seq_file)
load (l4.anc.seq_file)
###### 2. DR metadata ######
genome_idx = read.table(genome_idx_file,
stringsAsFactors = F, header = T)
# TB-Profiler DataBase with the drug associated mutations
tbprof = read.table(tbdr_db_file,
stringsAsFactors = F, header = T)
phylo_snps = c(7539, 1674434, 2518919, 1472337)
tbprof = tbprof[apply(tbprof, 1, function(x) !all(strsplit(x['pos'], ';')[[1]] %in% phylo_snps)),]
# Remove indels and focus in SNPs (maybe change in future...)
tbprof = tbprof[nchar(tbprof$ref) == nchar(tbprof$alt),]
# For eficiency, only use those positions of resistant mutations present in our alignment
tbprof2 = tbprof[apply(tbprof, 1, function(x) all(strsplit(x['pos'], ';')[[1]] %in% genome_idx$pos)),]
# Weird way to do it to preserve the order of the pos1;pos2 and pos2;pos1
tbprof2$snp = sapply(tbprof2$pos, function(p)
paste(genome_idx$snp[unlist(lapply(strsplit(p, ';')[[1]], function(x)
which(genome_idx$pos %in% x)))], collapse = ';'))
# Just change rownames. A bit of OCD...
row.names(tbprof2) = 1:nrow(tbprof2)
###### 3. Time to resistance ######
# Skip this to see the resulting files from running this functions
l2_t_to_res_snp = t_to_res_snp (
edges = l2_edges,
tree = l2_tree,
subclades = l2_subclades,
subclades_tips = l2_subclades_tips,
subclades_nodes = l2_subclades_nodes,
year_threshold = FALSE,
bases = c('A', 'C', 'G', 'T', '-'),
anc.seq = l2.anc.ml,
drug_db = tbprof2[tbprof2$confidence %in% c('high', 'moderate', 'low'),]
)
l4_t_to_res_snp = t_to_res_snp (
edges = l4_edges,
tree = l4_tree,
subclades = l4_subclades,
subclades_tips = l4_subclades_tips,
subclades_nodes = l4_subclades_nodes,
year_threshold = FALSE,
bases = c('A', 'C', 'G', 'T', '-'),
anc.seq = l4.anc.ml,
drug_db = tbprof2[tbprof2$confidence %in% c('high', 'moderate', 'low'),]
)
l4_t_to_res_snp = l4_t_to_res_snp[l4_t_to_res_snp$lineage %in%
names(l4_subclades),]
l4_t_to_res_snp = l4_t_to_res_snp[!is.na(l4_t_to_res_snp$lineage),]
l2_t_to_res_snp$lineage = 'beijing'
mtb_t_to_res = rbind (l2_t_to_res_snp, l4_t_to_res_snp)
mtb_t_to_res$lineage = ifelse (mtb_t_to_res$lineage == 'beijing', 'lineage2', 'lineage4')
mtb_t_to_res$lineage = factor(mtb_t_to_res$lineage,
levels = c('lineage4', 'lineage2'))
mtb_t_to_res = mtb_t_to_res[(mtb_t_to_res$int_st == 0 & mtb_t_to_res$ext_st == 1) |
(mtb_t_to_res$int_st == 0 & mtb_t_to_res$ext_st == 0),]
mtb_t_to_res = mtb_t_to_res[,c('int_year', 'ext_year', 'ext_st', 'lineage')]
mtb_t_to_res$time = mtb_t_to_res$ext_year - mtb_t_to_res$int_year
colnames(mtb_t_to_res) = c('int_year', 'ext_year', 'status', 'lineage',
'time')
mtb_t_to_res$time = as.numeric(as.vector(mtb_t_to_res$time))
mtb_t_to_res = mtb_t_to_res[mtb_t_to_res$int_year >= 1940,]
sfit <- survfit(Surv(time, status)~lineage, data=mtb_t_to_res)
fit.coxph <- coxph(Surv(time, status) ~ lineage,
data = mtb_t_to_res)
##### PLOT WITH TWO LINEAGES #####
cols = brewer.pal(12, "Set3")[c(5,4)]
col.levels = c('lineage4', 'lineage2')
pdf ('/plots/l2_vs_l4.pdf', height = 6, width = 7, useDingbats = F)
prettySurvPlot(sfit = sfit,
fit.coxph = fit.coxph,
xlevels = c('lineage4', 'lineage2'),
kap_meier = TRUE,
haz = TRUE,
conf.int = TRUE,
km.ylim = c(0.60,1.01),
km.xlim = c(0,40),
km.xlab = 'Time (years)',
km.ylab = 'Probability of\nremaining susceptible',
cols = brewer.pal(12, "Set3")[c(5,4)],
col.levels = c('lineage4', 'lineage2')
)
dev.off()
##### PLOT WITH ALL LINEAGES #####
mtb_t_to_res = rbind (l2_t_to_res_snp, l4_t_to_res_snp)
mtb_t_to_res = mtb_t_to_res[(mtb_t_to_res$int_st == 0 & mtb_t_to_res$ext_st == 1) |
(mtb_t_to_res$int_st == 0 & mtb_t_to_res$ext_st == 0),]
mtb_t_to_res = mtb_t_to_res[,c('int_year', 'ext_year', 'ext_st', 'lineage')]
mtb_t_to_res$time = mtb_t_to_res$ext_year - mtb_t_to_res$int_year
colnames(mtb_t_to_res) = c('int_year', 'ext_year', 'status', 'lineage',
'time')
mtb_t_to_res$time = as.numeric(as.vector(mtb_t_to_res$time))
mtb_t_to_res = mtb_t_to_res[mtb_t_to_res$int_year >= 1940,]
mtb_t_to_res$lineage = factor(mtb_t_to_res$lineage,
levels = c('lam3', 'lam9',
'lam11',
'haarlem', 't_type','x_type',
'beijing'))
sfit <- survfit(Surv(time, status)~lineage, data=mtb_t_to_res)
fit.coxph <- coxph(Surv(time, status) ~ lineage,
data = mtb_t_to_res)
pdf ('/plots/allLin.pdf', height = 6, width = 7, useDingbats = F)
cols = brewer.pal(12, "Set3")[c(1,3,4,5,6,10,12)]
col.levels = c("beijing","haarlem","lam11","lam3",
"lam9","t_type", "x_type")
prettySurvPlot(sfit = sfit,
fit.coxph = fit.coxph,
kap_meier = TRUE,
haz = TRUE,
conf.int = F,
km.ylab = 'Probability of\nremaining susceptible',
km.ylim = c(0.6,1.01),
km.xlim = c(0,40),
cols = cols,haz.size = 0.35,km.size = 0.35,risk.size = 0.3,
col.levels = col.levels
)
dev.off()
# PLOT INH TO MDR
mtb_t_to_res = rbind(l2_t_to_res_snp, l4_t_to_res_snp)
mtb_t_to_res = mtb_t_to_res[mtb_t_to_res$int_year >= 1940,]
mtb_t_to_mdr = list()
drug1 = 'isoniazid'
drug2 = 'rifampicin'
for (i in 1:nrow(mtb_t_to_res)){
int_res_present = strsplit(as.character(mtb_t_to_res$int_drug[i]), ';')[[1]]
ext_res_present = strsplit(as.character(mtb_t_to_res$ext_drug[i]), ';')[[1]]
int_time = mtb_t_to_res$int_year[i]
ext_time = mtb_t_to_res$ext_year[i]
if (drug2 %in% int_res_present) {
next()
} else if (all(is.na(int_res_present)) &
!drug2 %in% ext_res_present) {
time = ext_time - int_time
mtb_t_to_mdr[['time']] = c(mtb_t_to_mdr[['time']], time)
mtb_t_to_mdr[['status']] = c(mtb_t_to_mdr[['status']], 0)
mtb_t_to_mdr[['background']] = c(mtb_t_to_mdr[['background']], 'sensitive')
} else if (all(is.na(int_res_present)) &
drug2 %in% ext_res_present) {
time = ext_time - int_time
mtb_t_to_mdr[['time']] = c(mtb_t_to_mdr[['time']], time)
mtb_t_to_mdr[['status']] = c(mtb_t_to_mdr[['status']], 1)
mtb_t_to_mdr[['background']] = c(mtb_t_to_mdr[['background']], 'sensitive')
} else if (all(int_res_present %in% drug1) &
!drug2 %in% ext_res_present) {
time = ext_time - int_time
mtb_t_to_mdr[['time']] = c(mtb_t_to_mdr[['time']], time)
mtb_t_to_mdr[['status']] = c(mtb_t_to_mdr[['status']], 0)
mtb_t_to_mdr[['background']] = c(mtb_t_to_mdr[['background']],
drug1)
} else if (all(int_res_present %in% drug1) &
drug2 %in% ext_res_present) {
time = ext_time - int_time
mtb_t_to_mdr[['time']] = c(mtb_t_to_mdr[['time']], time)
mtb_t_to_mdr[['status']] = c(mtb_t_to_mdr[['status']], 1)
mtb_t_to_mdr[['background']] = c(mtb_t_to_mdr[['background']],
drug1)
}
}
mtb_t_to_mdr = as.data.frame(mtb_t_to_mdr)
mtb_t_to_mdr[,c("time")] = as.numeric(as.vector(unlist(mtb_t_to_mdr[,c("time")])))
mtb_t_to_mdr$background = factor(mtb_t_to_mdr$background,
levels = c('sensitive', 'isoniazid'))
sfit <- survfit(Surv(time, status)~background, data=mtb_t_to_mdr)
fit.coxph <- coxph(Surv(time, status)~background, data=mtb_t_to_mdr)
cols = brewer.pal(12, "Set3")[c(5,4)]
col.levels = c('sensitive', 'isoniazid')
pdf ('/plots/inh2mdr.samara.pdf', height = 6, width = 7, useDingbats = F)
prettySurvPlot(sfit = sfit,
fit.coxph = fit.coxph,
xlevels = c('sensitive', 'isoniazid'),
kap_meier = TRUE,
haz = T,
risk = T,
conf.int = TRUE,
km.ylim = c(0,1.01),
km.xlim = c(0,40),
km.xlab = 'Time (years)',
km.ylab = 'Probability of\nremaining monoresistant',
cols = brewer.pal(12, "Set3")[c(5,4)],
col.levels = c('sensitive', 'isoniazid')
)
dev.off()