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)

alveolarcelltype1 <- median(data$`Alveolar cell type 1`)
data$acl1<-ifelse(data$`Alveolar cell type 1`> alveolarcelltype1, "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))

alveolarcelltype2 <- median(data$`Alveolar cell type 2`)
data$acl2 <- ifelse(data$`Alveolar cell type 2` > alveolarcelltype2, "HIGH", "LOW")
fitacl2 <- survfit(Surv(overall_survival, deceased) ~ acl2, data = data)
ggsurvplot(fitacl2,
           data,
           pval = TRUE,
           risk.table = TRUE)

print(ggsurvplot(fitacl2, data, pval = TRUE, risk.table = TRUE))

bcell_median <- median(data$`B cell`)
data$bcell_acl <- ifelse(data$`B cell` > bcell_median, "HIGH", "LOW")
fit_bcell_acl <- survfit(Surv(overall_survival, deceased) ~ bcell_acl, data = data)
ggsurvplot(fit_bcell_acl,
           data,
           pval = TRUE,
           risk.table = TRUE)

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

# B cell dividing için işlem
bcell_div_median <- median(data$`B cell dividing`)
data$bcell_div_acl <- ifelse(data$`B cell dividing` > bcell_div_median, "HIGH", "LOW")
fit_bcell_div_acl <- survfit(Surv(overall_survival, deceased) ~ bcell_div_acl, data = data)

# cDC1 için işlem
cdc1_median <- median(data$cDC1)
data$cdc1_acl <- ifelse(data$cDC1 > cdc1_median, "HIGH", "LOW")
fit_cdc1_acl <- survfit(Surv(overall_survival, deceased) ~ cdc1_acl, data = data)

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

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

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

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

# cDC2 için işlem
cdc2_median <- median(data$cDC2)
data$cdc2_acl <- ifelse(data$cDC2 > cdc2_median, "HIGH", "LOW")
fit_cdc2_acl <- survfit(Surv(overall_survival, deceased) ~ cdc2_acl, data = data)

# Ciliated için işlem
ciliated_median <- median(data$Ciliated)
data$ciliated_acl <- ifelse(data$Ciliated > ciliated_median, "HIGH", "LOW")
fit_ciliated_acl <- survfit(Surv(overall_survival, deceased) ~ ciliated_acl, data = data)

# Club için işlem
club_median <- median(data$Club)
data$club_acl <- ifelse(data$Club > club_median, "HIGH", "LOW")
fit_club_acl <- survfit(Surv(overall_survival, deceased) ~ club_acl, data = data)

# DC mature için işlem
dcmature_median <- median(data$`DC mature`)
data$dcmature_acl <- ifelse(data$`DC mature` > dcmature_median, "HIGH", "LOW")
fit_dcmature_acl <- survfit(Surv(overall_survival, deceased) ~ dcmature_acl, data = data)

# Endothelial cell arterial için işlem
endo_arterial_median <- median(data$`Endothelial cell arterial`)
data$endo_arterial_acl <- ifelse(data$`Endothelial cell arterial` > endo_arterial_median, "HIGH", "LOW")
fit_endo_arterial_acl <- survfit(Surv(overall_survival, deceased) ~ endo_arterial_acl, data = data)

# Grafiklerin çizdirilmesi
ggsurvplot(fit_cdc2_acl, data, pval = TRUE, risk.table = TRUE)

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

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

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

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

# Endothelial cell capillary için işlem
endo_capillary_median <- median(data$`Endothelial cell capillary`)
data$endo_capillary_acl <- ifelse(data$`Endothelial cell capillary` > endo_capillary_median, "HIGH", "LOW")
fit_endo_capillary_acl <- survfit(Surv(overall_survival, deceased) ~ endo_capillary_acl, data = data)

# Endothelial cell lymphatic için işlem
endo_lymphatic_median <- median(data$`Endothelial cell lymphatic`)
data$endo_lymphatic_acl <- ifelse(data$`Endothelial cell lymphatic` > endo_lymphatic_median, "HIGH", "LOW")
fit_endo_lymphatic_acl <- survfit(Surv(overall_survival, deceased) ~ endo_lymphatic_acl, data = data)

# Endothelial cell venous için işlem
endo_venous_median <- median(data$`Endothelial cell venous`)
data$endo_venous_acl <- ifelse(data$`Endothelial cell venous` > endo_venous_median, "HIGH", "LOW")
fit_endo_venous_acl <- survfit(Surv(overall_survival, deceased) ~ endo_venous_acl, data = data)

# Fibroblast adventitial için işlem
fibro_adventitial_median <- median(data$`Fibroblast adventitial`)
data$fibro_adventitial_acl <- ifelse(data$`Fibroblast adventitial` > fibro_adventitial_median, "HIGH", "LOW")
fit_fibro_adventitial_acl <- survfit(Surv(overall_survival, deceased) ~ fibro_adventitial_acl, data = data)

# Fibroblast alveolar için işlem
fibro_alveolar_median <- median(data$`Fibroblast alveolar`)
data$fibro_alveolar_acl <- ifelse(data$`Fibroblast alveolar` > fibro_alveolar_median, "HIGH", "LOW")
fit_fibro_alveolar_acl <- survfit(Surv(overall_survival, deceased) ~ fibro_alveolar_acl, data = data)

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

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_adventitial_acl, data, pval = TRUE, risk.table = TRUE)

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

# Fibroblast peribronchial için işlem
fibro_peribronchial_median <- median(data$`Fibroblast peribronchial`)
data$fibro_peribronchial_acl <- ifelse(data$`Fibroblast peribronchial` > fibro_peribronchial_median, "HIGH", "LOW")
fit_fibro_peribronchial_acl <- survfit(Surv(overall_survival, deceased) ~ fibro_peribronchial_acl, data = data)

# Macrophage için işlem
macrophage_median <- median(data$Macrophage)
data$macrophage_acl <- ifelse(data$Macrophage > macrophage_median, "HIGH", "LOW")
fit_macrophage_acl <- survfit(Surv(overall_survival, deceased) ~ macrophage_acl, data = data)

# Macrophage alveolar için işlem
macrophage_alveolar_median <- median(data$`Macrophage alveolar`)
data$macrophage_alveolar_acl <- ifelse(data$`Macrophage alveolar` > macrophage_alveolar_median, "HIGH", "LOW")
fit_macrophage_alveolar_acl <- survfit(Surv(overall_survival, deceased) ~ macrophage_alveolar_acl, data = data)

# Mast cell için işlem
mast_cell_median <- median(data$`Mast cell`)
data$mast_cell_acl <- ifelse(data$`Mast cell` > mast_cell_median, "HIGH", "LOW")
fit_mast_cell_acl <- survfit(Surv(overall_survival, deceased) ~ mast_cell_acl, data = data)

# Mesothelial için işlem
mesothelial_median <- median(data$Mesothelial)
data$mesothelial_acl <- ifelse(data$Mesothelial > mesothelial_median, "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_classical_median <- median(data$`Monocyte classical`)
data$monocyte_classical_acl <- ifelse(data$`Monocyte classical` > monocyte_classical_median, "HIGH", "LOW")
fit_monocyte_classical_acl <- survfit(Surv(overall_survival, deceased) ~ monocyte_classical_acl, data = data)

# Monocyte non-classical için işlem
monocyte_non_classical_median <- median(data$`Monocyte non-classical`)
data$monocyte_non_classical_acl <- ifelse(data$`Monocyte non-classical` > monocyte_non_classical_median, "HIGH", "LOW")
fit_monocyte_non_classical_acl <- survfit(Surv(overall_survival, deceased) ~ monocyte_non_classical_acl, data = data)

# Myeloid dividing için işlem
myeloid_dividing_median <- median(data$`Myeloid dividing`)
data$myeloid_dividing_acl <- ifelse(data$`Myeloid dividing` > myeloid_dividing_median, "HIGH", "LOW")
fit_myeloid_dividing_acl <- survfit(Surv(overall_survival, deceased) ~ myeloid_dividing_acl, data = data)

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

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

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

# Neutrophils için işlem
neutrophils_median <- median(data$Neutrophils)
data$neutrophils_acl <- ifelse(data$Neutrophils > neutrophils_median, "HIGH", "LOW")
fit_neutrophils_acl <- survfit(Surv(overall_survival, deceased) ~ neutrophils_acl, data = data)

# NK cell için işlem
nk_cell_median <- median(data$`NK cell`)
data$nk_cell_acl <- ifelse(data$`NK cell` > nk_cell_median, "HIGH", "LOW")
fit_nk_cell_acl <- survfit(Surv(overall_survival, deceased) ~ nk_cell_acl, data = data)

# NK cell dividing için işlem
nk_cell_dividing_median <- median(data$`NK cell dividing`)
data$nk_cell_dividing_acl <- ifelse(data$`NK cell dividing` > nk_cell_dividing_median, "HIGH", "LOW")
fit_nk_cell_dividing_acl <- survfit(Surv(overall_survival, deceased) ~ nk_cell_dividing_acl, data = data)

# pDC için işlem
pdc_median <- median(data$pDC)
data$pdc_acl <- ifelse(data$pDC > pdc_median, "HIGH", "LOW")
fit_pdc_acl <- survfit(Surv(overall_survival, deceased) ~ pdc_acl, data = data)

# Pericyte için işlem
pericyte_median <- median(data$Pericyte)
data$pericyte_acl <- ifelse(data$Pericyte > pericyte_median, "HIGH", "LOW")
fit_pericyte_acl <- survfit(Surv(overall_survival, deceased) ~ pericyte_acl, data = data)


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

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

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

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

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

# Plasma cell için işlem
plasma_cell_median <- median(data$`Plasma cell`)
data$plasma_cell_acl <- ifelse(data$`Plasma cell` > plasma_cell_median, "HIGH", "LOW")
fit_plasma_cell_acl <- survfit(Surv(overall_survival, deceased) ~ plasma_cell_acl, data = data)

# Plasma cell dividing için işlem
plasma_cell_dividing_median <- median(data$`Plasma cell dividing`)
data$plasma_cell_dividing_acl <- ifelse(data$`Plasma cell dividing` > plasma_cell_dividing_median, "HIGH", "LOW")
fit_plasma_cell_dividing_acl <- survfit(Surv(overall_survival, deceased) ~ plasma_cell_dividing_acl, data = data)

# ROS1+ healthy epithelial için işlem
ros1_epithelial_median <- median(data$`ROS1+ healthy epithelial`)
data$ros1_epithelial_acl <- ifelse(data$`ROS1+ healthy epithelial` > ros1_epithelial_median, "HIGH", "LOW")
fit_ros1_epithelial_acl <- survfit(Surv(overall_survival, deceased) ~ ros1_epithelial_acl, data = data)

# Smooth muscle cell için işlem
smooth_muscle_median <- median(data$`Smooth muscle cell`)
data$smooth_muscle_acl <- ifelse(data$`Smooth muscle cell` > smooth_muscle_median, "HIGH", "LOW")
fit_smooth_muscle_acl <- survfit(Surv(overall_survival, deceased) ~ smooth_muscle_acl, data = data)

# Stromal dividing için işlem
stromal_dividing_median <- median(data$`Stromal dividing`)
data$stromal_dividing_acl <- ifelse(data$`Stromal dividing` > stromal_dividing_median, "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_ros1_epithelial_acl, data, pval = TRUE, risk.table = TRUE)

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

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

# T cell CD4 için işlem
t_cell_cd4_median <- median(data$`T cell CD4`)
data$t_cell_cd4_acl <- ifelse(data$`T cell CD4` > t_cell_cd4_median, "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_cd4_dividing_median <- median(data$`T cell CD4 dividing`)
data$t_cell_cd4_dividing_acl <- ifelse(data$`T cell CD4 dividing` > t_cell_cd4_dividing_median, "HIGH", "LOW")
fit_t_cell_cd4_dividing_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_cd4_dividing_acl, data = data)

# T cell CD8 activated için işlem
t_cell_cd8_activated_median <- median(data$`T cell CD8 activated`)
data$t_cell_cd8_activated_acl <- ifelse(data$`T cell CD8 activated` > t_cell_cd8_activated_median, "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
t_cell_cd8_dividing_median <- median(data$`T cell CD8 dividing`)
data$t_cell_cd8_dividing_acl <- ifelse(data$`T cell CD8 dividing` > t_cell_cd8_dividing_median, "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_cd4_dividing_acl, data, pval = TRUE, risk.table = TRUE)

## Warning in .pvalue(fit, data = data, method = method, pval = pval, pval.coord = pval.coord, : There are no survival curves to be compared. 
##  This is a null model.

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_effector_memory_median <- median(data$`T cell CD8 effector memory`)
data$t_cell_cd8_effector_memory_acl <- ifelse(data$`T cell CD8 effector memory` > t_cell_cd8_effector_memory_median, "HIGH", "LOW")
fit_t_cell_cd8_effector_memory_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_cd8_effector_memory_acl, data = data)

# T cell CD8 naive için işlem
t_cell_cd8_naive_median <- median(data$`T cell CD8 naive`)
data$t_cell_cd8_naive_acl <- ifelse(data$`T cell CD8 naive` > t_cell_cd8_naive_median, "HIGH", "LOW")
fit_t_cell_cd8_naive_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_cd8_naive_acl, data = data)

# T cell CD8 terminally exhausted için işlem
t_cell_cd8_terminally_exhausted_median <- median(data$`T cell CD8 terminally exhausted`)
data$t_cell_cd8_terminally_exhausted_acl <- ifelse(data$`T cell CD8 terminally exhausted` > t_cell_cd8_terminally_exhausted_median, "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
t_cell_nk_like_median <- median(data$`T cell NK-like`)
data$t_cell_nk_like_acl <- ifelse(data$`T cell NK-like` > t_cell_nk_like_median, "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
t_cell_regulatory_median <- median(data$`T cell regulatory`)
data$t_cell_regulatory_acl <- ifelse(data$`T cell regulatory` > t_cell_regulatory_median, "HIGH", "LOW")
fit_t_cell_regulatory_acl <- survfit(Surv(overall_survival, deceased) ~ t_cell_regulatory_acl, data = data)

# Transitional Club/AT2 için işlem
transitional_club_at2_median <- median(data$`Transitional Club/AT2`)
data$transitional_club_at2_acl <- ifelse(data$`Transitional Club/AT2` > transitional_club_at2_median, "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_effector_memory_acl, data, pval = TRUE, risk.table = TRUE)

ggsurvplot(fit_t_cell_cd8_naive_acl, data, pval = TRUE, risk.table = TRUE)

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)