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survival-analysis-check-and-update.R
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library(ezcox)
library(ggplot2)
df.seqz = readRDS(file = "output/df.seqz.RDS")
surv_df <- readRDS(file = "data/PRAD_Survival.rds")
range01 <- function(x, ...) {
(x - min(x, ...)) / (max(x, ...) - min(x, ...))
}
cols_to_sigs.seqz <- c(paste0("CN-Sig", 1:5), paste0("SBS-Sig", 1:3))
surv_dt <- dplyr::inner_join(
df.seqz %>%
dplyr::select(-c("Study", "subject_id",
"tumor_body_site",
"tumor_Run", "normal_Run",
"CNV_ID", "Fusion")),
surv_df, by = c("PatientID" = "sample"))
dup_ids = which(duplicated(surv_dt$PatientID))
# Remove duplicated records
surv_dt = surv_dt[-dup_ids, ]
# Scale the signature exposure to 0-20.
# To better understand the effect of signature exposure on clinical events,
# here we scale them into range 0-20. 1 increase means 5% increase of signature exposure.
#
# Scale the CNA burden to 0-20.
#
# keep only ti_fraction, tv result will be 1/ti_result
# also scale it to 0-20
# also scale purity to 0-20
surv_dt = surv_dt %>%
dplyr::select(-Tv_fraction) %>%
dplyr::mutate(cnaBurden = 20 * cnaBurden,
Stage = as.character(Stage) %>%
factor(levels = c("T2", "T3", "T4"))) %>%
dplyr::mutate_at(c(cols_to_sigs.seqz, "Ti_fraction", "purity"),
~ 20 * range01(., na.rm = TRUE))
# Unvariable analysis -----------------------------------------------------
p = show_forest(surv_dt, covariates = cols_to_sigs.seqz,
time = "OS.time", status = "OS",
merge_models = TRUE, add_caption = FALSE)
p
# split into primary and meta
table(surv_dt$sample_type)
p1 = show_forest(surv_dt %>%
subset(sample_type == "Primary"),
covariates = cols_to_sigs.seqz,
time = "OS.time", status = "OS",
merge_models = TRUE, add_caption = FALSE)
p1
ggsave(filename = "check/OS_sig_primary.pdf", plot = p1,
width = 7, height = 5)
p2 = show_forest(surv_dt %>%
subset(sample_type != "Primary"),
covariates = cols_to_sigs.seqz,
time = "OS.time", status = "OS",
merge_models = TRUE, add_caption = FALSE)
p2
ggsave(filename = "check/OS_sig_meta.pdf", plot = p2,
width = 7, height = 5)
# Multivariable analysis
p = show_forest(surv_dt,
covariates = cols_to_sigs.seqz[1],
controls = cols_to_sigs.seqz[-1],
time = "OS.time", status = "OS",
add_caption = FALSE, merge_models = TRUE)
p
ggsave(filename = "check/OS_sig_multivariable.pdf", plot = p,
width = 7, height = 5)
p1 = show_forest(surv_dt %>%
subset(sample_type == "Primary"),
covariates = cols_to_sigs.seqz[1],
controls = cols_to_sigs.seqz[-1],
time = "OS.time", status = "OS",
add_caption = FALSE, merge_models = TRUE)
p1
ggsave(filename = "check/OS_sig_multivariable_primary.pdf", plot = p1,
width = 7, height = 5)
p2 = show_forest(surv_dt %>%
subset(sample_type != "Primary"),
covariates = cols_to_sigs.seqz[1],
controls = cols_to_sigs.seqz[-1],
time = "OS.time", status = "OS",
add_caption = FALSE, merge_models = TRUE)
p2
ggsave(filename = "check/OS_sig_multivariable_meta.pdf", plot = p2,
width = 7, height = 5)
# PFS
p = show_forest(surv_dt, covariates = cols_to_sigs.seqz,
time = "PFI.time", status = "PFI",
merge_models = TRUE, add_caption = FALSE)
p
p1 = show_forest(surv_dt,
covariates = cols_to_sigs.seqz[1],
controls = cols_to_sigs.seqz[-1],
time = "PFI.time", status = "PFI",
merge_models = TRUE, add_caption = FALSE)
p1
ggsave(filename = "check/PFS_sig_multivariable.pdf", plot = p1,
width = 7, height = 5)
# Features ----------------------------------------------------------------
cols_to_features <- c(
"Age", "Stage", "GleasonScore",
"n_SBS",
"n_INDEL",
"n_CNV", "n_Amp", "n_Del",
"cnaBurden",
"Ti_fraction",
"TDP score",
"Chromoth_state",
"MATH",
"purity",
"ploidy"
)
p = show_forest(surv_dt,
covariates = cols_to_features,
controls = NULL,
time = "OS.time", status = "OS",
merge_models = TRUE, limits = log(c(0.01, 5)),
add_caption = FALSE)
p
ggsave(filename = "check/OS_features.pdf", plot = p,
width = 8, height = 6)
p1 = show_forest(surv_dt %>%
subset(sample_type == "Primary"),
covariates = cols_to_features,
controls = NULL,
time = "OS.time", status = "OS",
merge_models = TRUE, limits = log(c(0.01, 5)),
add_caption = FALSE)
p1
ggsave(filename = "check/OS_features_primary.pdf", plot = p1,
width = 8, height = 6)
p2 = show_forest(surv_dt %>%
subset(sample_type != "Primary"),
covariates = cols_to_features,
controls = NULL,
time = "OS.time", status = "OS",
merge_models = TRUE, limits = log(c(0.01, 5)),
add_caption = FALSE)
p2
ggsave(filename = "check/OS_features_meta.pdf", plot = p2,
width = 8, height = 6)
# multivariable
p3 = show_forest(surv_dt,
covariates = cols_to_features[1],
controls = cols_to_features[-1],
time = "OS.time", status = "OS",
merge_models = TRUE, limits = log(c(0.01, 5)),
add_caption = FALSE)
p3
ggsave(filename = "check/OS_features_multivariable.pdf", plot = p3,
width = 8, height = 6)
# PFI
p = show_forest(surv_dt,
covariates = cols_to_features,
controls = NULL,
time = "PFI.time", status = "PFI",
merge_models = TRUE, limits = log(c(0.5, 5)),
add_caption = FALSE)
p
ggsave(filename = "check/PFS_features.pdf", plot = p,
width = 8, height = 6)
p1 = show_forest(surv_dt %>%
subset(sample_type == "Primary"),
covariates = cols_to_features,
controls = NULL,
time = "PFI.time", status = "PFI",
merge_models = TRUE, limits = log(c(0.5, 5)),
add_caption = FALSE)
p1
ggsave(filename = "check/PFI_features_primary.pdf", plot = p1,
width = 8, height = 6)
p2 = show_forest(surv_dt %>%
subset(sample_type != "Primary"),
covariates = cols_to_features,
controls = NULL,
time = "OS.time", status = "OS",
merge_models = TRUE, limits = log(c(0.01, 5)),
add_caption = FALSE)
p2
ggsave(filename = "check/OS_features_meta.pdf", plot = p2,
width = 8, height = 6)
# multivariable
p3 = show_forest(surv_dt,
covariates = cols_to_features[1],
controls = cols_to_features[-1],
time = "PFI.time", status = "PFI",
merge_models = TRUE, limits = log(c(0.5, 5)),
add_caption = FALSE)
p3
ggsave(filename = "check/PFI_features_multivariable.pdf", plot = p3,
width = 8, height = 6)
# Check PFS HR for CNA burden ---------------------------------------------
library(ezcox)
library(ggplot2)
df.seqz = readRDS(file = "output/df.seqz.RDS")
surv_df <- readRDS(file = "data/PRAD_Survival.rds")
range01 <- function(x, ...) {
(x - min(x, ...)) / (max(x, ...) - min(x, ...))
}
cols_to_sigs.seqz <- c(paste0("CN-Sig", 1:5), paste0("SBS-Sig", 1:3))
surv_dt <- dplyr::inner_join(
df.seqz %>%
dplyr::select(-c("Study", "subject_id",
"tumor_body_site",
"tumor_Run", "normal_Run",
"CNV_ID", "Fusion")),
surv_df, by = c("PatientID" = "sample"))
dup_ids = which(duplicated(surv_dt$PatientID))
# Remove duplicated records
surv_dt = surv_dt[-dup_ids, ]
summary(surv_dt$cnaBurden)
p1 <- show_forest(surv_dt %>%
dplyr::mutate(cnaBurden = cnaBurden),
covariates = "cnaBurden",
controls = NULL,
time = "PFI.time", status = "PFI",
add_caption = FALSE, merge_models = TRUE)
p2 <- show_forest(surv_dt %>%
dplyr::mutate(cnaBurden = cnaBurden * 5),
covariates = "cnaBurden",
controls = NULL,
time = "PFI.time", status = "PFI",
add_caption = FALSE, merge_models = TRUE)
p3 <- show_forest(surv_dt %>%
dplyr::mutate(cnaBurden = cnaBurden * 20),
covariates = "cnaBurden",
controls = NULL,
time = "PFI.time", status = "PFI",
add_caption = FALSE, merge_models = TRUE)
p <- cowplot::plot_grid(p1, p2, p3, nrow = 3)
p
ggsave(filename = "check/PFI_CNA_burden.pdf", plot = p,
width = 8, height = 5)