Kaplan Meier Adeno

library(survival)
library(survminer)

## Loading required package: ggplot2

## Loading required package: ggpubr

## 
## Attaching package: 'survminer'

## The following object is masked from 'package:survival':
## 
##     myeloma

ADENO_SURVIVAL <- readRDS("~/clinic_deconv_adeno.rds")
data <- ADENO_SURVIVAL
data$overall_survival <- as.numeric(data$overall_survival)

res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Alveolar cell type 1"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$acl1 <- ifelse(data$`Alveolar cell type 1` > cutpoint, "HIGH", "LOW")
fitacl1 <- survfit(Surv(overall_survival, deceased) ~ acl1, data = data)
ggsurvplot(fitacl1,
           data,
           pval=T,
           risk.table = T)

print(ggsurvplot(fitacl1, data, pval = TRUE, risk.table = TRUE))

# cDC1 için işlem

res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("cDC1"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$cdc1_acl <- ifelse(data$`cDC1` > cutpoint, "HIGH", "LOW")
fit_cdc1_acl <- survfit(Surv(overall_survival, deceased) ~ cdc1_acl, data = data)

# Grafiklerin çizdirilmesi


ggsurvplot(fit_cdc1_acl,
           data,
           pval = TRUE,
           risk.table = TRUE)

print(ggsurvplot(fit_cdc1_acl, data, pval = TRUE, risk.table = TRUE))

# cDC2 için işlem


# Ciliated için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Ciliated"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$ciliated_acl <- ifelse(data$Ciliated > cutpoint, "HIGH", "LOW")
fit_ciliated_acl <- survfit(Surv(overall_survival, deceased) ~ ciliated_acl, data = data)

# Club için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Club"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$club_acl <- ifelse(data$Club > cutpoint, "HIGH", "LOW")
fit_club_acl <- survfit(Surv(overall_survival, deceased) ~ club_acl, data = data)



# Endothelial cell arterial için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Endothelial cell arterial"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$endo_arterial_acl <- ifelse(data$`Endothelial cell arterial` > cutpoint, "HIGH", "LOW")
fit_endo_arterial_acl <- survfit(Surv(overall_survival, deceased) ~ endo_arterial_acl, data = data)

# Grafiklerin çizdirilmesi
ggsurvplot(fit_ciliated_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_club_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_endo_arterial_acl, data, pval = TRUE, risk.table = TRUE)

# Endothelial cell capillary için işlem


# Endothelial cell lymphatic için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Endothelial cell lymphatic"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$endo_lymphatic_acl <- ifelse(data$`Endothelial cell lymphatic` > cutpoint, "HIGH", "LOW")
fit_endo_lymphatic_acl <- survfit(Surv(overall_survival, deceased) ~ endo_lymphatic_acl, data = data)

# Endothelial cell venous için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Endothelial cell venous"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$endo_venous_acl <- ifelse(data$`Endothelial cell venous` > cutpoint, "HIGH", "LOW")
fit_endo_venous_acl <- survfit(Surv(overall_survival, deceased) ~ endo_venous_acl, data = data)

# Fibroblast adventitial için işlem

# Fibroblast alveolar için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Fibroblast alveolar"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$fibro_alveolar_acl <- ifelse(data$`Fibroblast alveolar` > cutpoint, "HIGH", "LOW")
fit_fibro_alveolar_acl <- survfit(Surv(overall_survival, deceased) ~ fibro_alveolar_acl, data = data)

# Grafiklerin çizdirilmesi
ggsurvplot(fit_endo_lymphatic_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_endo_venous_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_fibro_alveolar_acl, data, pval = TRUE, risk.table = TRUE)

# Fibroblast peribronchial için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Fibroblast peribronchial"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$fibro_peribronchial_acl <- ifelse(data$`Fibroblast peribronchial` > cutpoint, "HIGH", "LOW")
fit_fibro_peribronchial_acl <- survfit(Surv(overall_survival, deceased) ~ fibro_peribronchial_acl, data = data)

# Macrophage için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Macrophage"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$macrophage_acl <- ifelse(data$Macrophage > cutpoint, "HIGH", "LOW")
fit_macrophage_acl <- survfit(Surv(overall_survival, deceased) ~ macrophage_acl, data = data)

# Macrophage alveolar için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Macrophage alveolar"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$macrophage_alveolar_acl <- ifelse(data$`Macrophage alveolar` > cutpoint, "HIGH", "LOW")
fit_macrophage_alveolar_acl <- survfit(Surv(overall_survival, deceased) ~ macrophage_alveolar_acl, data = data)

# Mast cell için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Mast cell"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$mast_cell_acl <- ifelse(data$`Mast cell` > cutpoint, "HIGH", "LOW")
fit_mast_cell_acl <- survfit(Surv(overall_survival, deceased) ~ mast_cell_acl, data = data)

# Mesothelial için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Mesothelial"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$mesothelial_acl <- ifelse(data$Mesothelial > cutpoint, "HIGH", "LOW")
fit_mesothelial_acl <- survfit(Surv(overall_survival, deceased) ~ mesothelial_acl, data = data)

# Grafiklerin çizdirilmesi
ggsurvplot(fit_fibro_peribronchial_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_macrophage_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_macrophage_alveolar_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_mast_cell_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_mesothelial_acl, data, pval = TRUE, risk.table = TRUE)

# Monocyte classical için işlem

# Monocyte non-classical için işlem


# Myeloid dividing için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Myeloid dividing"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$myeloid_dividing_acl <- ifelse(data$`Myeloid dividing` > cutpoint, "HIGH", "LOW")
fit_myeloid_dividing_acl <- survfit(Surv(overall_survival, deceased) ~ myeloid_dividing_acl, data = data)

# Grafiklerin çizdirilmesi
ggsurvplot(fit_myeloid_dividing_acl, data, pval = TRUE, risk.table = TRUE)

# Neutrophils için işlem


# NK cell için işlem

# NK cell dividing için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("NK cell dividing"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$nk_cell_dividing_acl <- ifelse(data$`NK cell dividing` > cutpoint, "HIGH", "LOW")
fit_nk_cell_dividing_acl <- survfit(Surv(overall_survival, deceased) ~ nk_cell_dividing_acl, data = data)

# pDC için işlem


# Pericyte için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Pericyte"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$pericyte_acl <- ifelse(data$Pericyte > cutpoint, "HIGH", "LOW")
fit_pericyte_acl <- survfit(Surv(overall_survival, deceased) ~ pericyte_acl, data = data)


# Grafiklerin çizdirilmesi
ggsurvplot(fit_nk_cell_dividing_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_pericyte_acl, data, pval = TRUE, risk.table = TRUE)

# Plasma cell için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Plasma cell"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$plasma_cell_acl <- ifelse(data$`Plasma cell` > cutpoint, "HIGH", "LOW")
fit_plasma_cell_acl <- survfit(Surv(overall_survival, deceased) ~ plasma_cell_acl, data = data)

# Plasma cell dividing için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Plasma cell dividing"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$plasma_cell_dividing_acl <- ifelse(data$`Plasma cell dividing` > cutpoint, "HIGH", "LOW")
fit_plasma_cell_dividing_acl <- survfit(Surv(overall_survival, deceased) ~ plasma_cell_dividing_acl, data = data)

# ROS1+ healthy epithelial için işlem


# Smooth muscle cell için işlem


# Stromal dividing için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Stromal dividing"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$stromal_dividing_acl <- ifelse(data$`Stromal dividing` > cutpoint, "HIGH", "LOW")
fit_stromal_dividing_acl <- survfit(Surv(overall_survival, deceased) ~ stromal_dividing_acl, data = data)

# Grafiklerin çizdirilmesi
ggsurvplot(fit_plasma_cell_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_plasma_cell_dividing_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_stromal_dividing_acl, data, pval = TRUE, risk.table = TRUE)

# T cell CD4 için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("T cell CD4"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$t_cell_cd4_acl <- ifelse(data$`T cell CD4` > cutpoint, "HIGH", "LOW")
fit_t_cell_cd4_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_cd4_acl, data = data)

# T cell CD4 dividing için işlem

# T cell CD8 activated için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("T cell CD8 activated"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$t_cell_cd8_activated_acl <- ifelse(data$`T cell CD8 activated` > cutpoint, "HIGH", "LOW")
fit_t_cell_cd8_activated_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_cd8_activated_acl, data = data)

# T cell CD8 dividing için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("T cell CD8 dividing"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$t_cell_cd8_dividing_acl <- ifelse(data$`T cell CD8 dividing` > cutpoint, "HIGH", "LOW")
fit_t_cell_cd8_dividing_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_cd8_dividing_acl, data = data)


# Grafiklerin çizdirilmesi
ggsurvplot(fit_t_cell_cd4_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_t_cell_cd8_activated_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_t_cell_cd8_dividing_acl, data, pval = TRUE, risk.table = TRUE)

# T cell CD8 effector memory için işlem

# T cell CD8 naive için işlem


# T cell CD8 terminally exhausted için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("T cell CD8 terminally exhausted"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$t_cell_cd8_terminally_exhausted_acl <- ifelse(data$`T cell CD8 terminally exhausted` > cutpoint, "HIGH", "LOW")
fit_t_cell_cd8_terminally_exhausted_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_cd8_terminally_exhausted_acl, data = data)

# T cell NK-like için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("T cell NK-like"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$t_cell_nk_like_acl <- ifelse(data$`T cell NK-like` > cutpoint, "HIGH", "LOW")
fit_t_cell_nk_like_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_nk_like_acl, data = data)

# T cell regulatory için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("T cell regulatory"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$t_cell_regulatory_acl <- ifelse(data$`T cell regulatory` > cutpoint, "HIGH", "LOW")
fit_t_cell_regulatory_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_regulatory_acl, data = data)

# Transitional Club/AT2 için işlem
#res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
#                         variables = c("Transitional Club/AT2"))
#cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
#data$transitional_club_at2_acl <- ifelse(data$`Transitional Club/AT2` > cutpoint, "HIGH", "LOW")
#fit_transitional_club_at2_acl <- survfit(Surv(overall_survival, deceased) ~ transitional_club_at2_acl, data = data)

# Grafiklerin çizdirilmesi
ggsurvplot(fit_t_cell_cd8_terminally_exhausted_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_t_cell_nk_like_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_t_cell_regulatory_acl, data, pval = TRUE, risk.table = TRUE)

#ggsurvplot(fit_transitional_club_at2_acl, data, pval = TRUE, risk.table = TRUE)


# DC mature için işlem
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("DC mature"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$dcmature_acl <- ifelse(data$`DC mature` > cutpoint, "HIGH", "LOW")
fit_dcmature_acl <- survfit(Surv(overall_survival, deceased) ~ dcmature_acl, data = data)
ggsurvplot(fit_dcmature_acl, data, pval = TRUE, risk.table = TRUE)

res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Endothelial cell capillary"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$endo_capillary_acl <- ifelse(data$`Endothelial cell capillary` > cutpoint, "HIGH", "LOW")
fit_endo_capillary_acl <- survfit(Surv(overall_survival, deceased) ~ endo_capillary_acl, data = data)

ggsurvplot(fit_endo_capillary_acl, data, pval = TRUE, risk.table = TRUE)

#res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
#                         variables = c("Monocyte classical"))
#cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
#data$monocyte_classical_acl <- ifelse(data$`Monocyte classical` > cutpoint, "HIGH", "LOW")
#fit_monocyte_classical_acl <- survfit(Surv(overall_survival, deceased) ~ monocyte_classical_acl, data = data)
#ggsurvplot(fit_monocyte_classical_acl, data, pval = TRUE, risk.table = TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Monocyte non-classical"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$monocyte_non_classical_acl <- ifelse(data$`Monocyte non-classical` > cutpoint, "HIGH", "LOW")
fit_monocyte_non_classical_acl <- survfit(Surv(overall_survival, deceased) ~ monocyte_non_classical_acl, data = data)
ggsurvplot(fit_monocyte_non_classical_acl, data, pval = TRUE, risk.table = TRUE)

#res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
#                         variables = c("NK cell"))
#cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
#data$nk_cell_acl <- ifelse(data$`NK cell` > cutpoint, "HIGH", "LOW")
#fit_nk_cell_acl <- survfit(Surv(overall_survival, deceased) ~ nk_cell_acl, data = data)
#ggsurvplot(fit_nk_cell_acl, data, pval = TRUE, risk.table = TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("pDC"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$pdc_acl <- ifelse(data$pDC > cutpoint, "HIGH", "LOW")
fit_pdc_acl <- survfit(Surv(overall_survival, deceased) ~ pdc_acl, data = data)
ggsurvplot(fit_pdc_acl, data, pval = TRUE, risk.table = TRUE)

res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("ROS1+ healthy epithelial"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$ros1_epithelial_acl <- ifelse(data$`ROS1+ healthy epithelial` > cutpoint, "HIGH", "LOW")
fit_ros1_epithelial_acl <- survfit(Surv(overall_survival, deceased) ~ ros1_epithelial_acl, data = data)
ggsurvplot(fit_ros1_epithelial_acl, data, pval = TRUE, risk.table = TRUE)

res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("Smooth muscle cell"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$smooth_muscle_acl <- ifelse(data$`Smooth muscle cell` > cutpoint, "HIGH", "LOW")
fit_smooth_muscle_acl <- survfit(Surv(overall_survival, deceased) ~ smooth_muscle_acl, data = data)
ggsurvplot(fit_smooth_muscle_acl, data, pval = TRUE, risk.table = TRUE)

#res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
#                         variables = c("T cell CD4 dividing"))
#cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
#data$t_cell_cd4_dividing_acl <- ifelse(data$`T cell CD4 dividing` > cutpoint, "HIGH", "LOW")
#fit_t_cell_cd4_dividing_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_cd4_dividing_acl, data = data)
#ggsurvplot(fit_t_cell_cd4_dividing_acl, data, pval = TRUE, risk.table = TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
                         variables = c("T cell CD8 effector memory"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$t_cell_cd8_effector_memory_acl <- ifelse(data$`T cell CD8 effector memory` > cutpoint, "HIGH", "LOW")
fit_t_cell_cd8_effector_memory_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_cd8_effector_memory_acl, data = data)

ggsurvplot(fit_t_cell_cd8_effector_memory_acl, data, pval = TRUE, risk.table = TRUE)