library(survival)
library(survminer)
## Loading required package: ggplot2
## Loading required package: ggpubr
##
## Attaching package: 'survminer'
## The following object is masked from 'package:survival':
##
## myeloma
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("Fibroblast_alveolar"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$Fibroblast_alveolar <- ifelse(data$Fibroblast_alveolar > cutpoint, "HIGH", "LOW")
fit_Fibroblast_alveolar <- survfit(Surv(overall_survival, deceased) ~ Fibroblast_alveolar, data = data)
ggsurvplot(fit_Fibroblast_alveolar,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ Fibroblast_alveolar, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ Fibroblast_alveolar,
## data = data)
##
## coef exp(coef) se(coef) z p
## Fibroblast_alveolar 0.07372 1.07651 0.06647 1.109 0.267
##
## Likelihood ratio test=1.11 on 1 df, p=0.2915
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`Fibroblast alveolar`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "Fibroblast_alveolar"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
Fibroblast_alveolar_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "Fibroblast_alveolar"
)
Fibroblast_alveolar_negative_gene_df$exp.level <- sig$`Fibroblast alveolar`
reactable(Fibroblast_alveolar_negative_gene_df)
library(survival)
library(survminer)
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("Macrophage"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$Macrophage <- ifelse(data$Macrophage > cutpoint, "HIGH", "LOW")
fit_Macrophage <- survfit(Surv(overall_survival, deceased) ~ Macrophage, data = data)
ggsurvplot(fit_Macrophage,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ Macrophage, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ Macrophage,
## data = data)
##
## coef exp(coef) se(coef) z p
## Macrophage -0.04942 0.95178 0.03187 -1.551 0.121
##
## Likelihood ratio test=2.54 on 1 df, p=0.1113
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$Macrophage
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "Macrophage"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", mirror = "www")
## Ensembl site unresponsive, trying asia mirror
## Ensembl site unresponsive, trying useast mirror
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
Macrophage_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "Macrophage"
)
Macrophage_negative_gene_df$exp.level <- sig$`Macrophage`
reactable(Macrophage_negative_gene_df)
library(survival)
library(survminer)
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("T_cell_CD8_naive"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$T_cell_CD8_naive <- ifelse(data$T_cell_CD8_naive > cutpoint, "HIGH", "LOW")
fit_T_cell_CD8_naive <- survfit(Surv(overall_survival, deceased) ~ T_cell_CD8_naive, data = data)
ggsurvplot(fit_T_cell_CD8_naive,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ T_cell_CD8_naive, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ T_cell_CD8_naive,
## data = data)
##
## coef exp(coef) se(coef) z p
## T_cell_CD8_naive -0.19359 0.82399 0.08076 -2.397 0.0165
##
## Likelihood ratio test=6.36 on 1 df, p=0.01167
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`T cell CD8 naive`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "T_cell_CD8_naive"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
T_cell_CD8_naive_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "T_cell_CD8_naive"
)
T_cell_CD8_naive_negative_gene_df$exp.level <- sig$`T cell CD8 naive`
reactable(T_cell_CD8_naive_negative_gene_df)
library(survival)
library(survminer)
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("B_cell"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$B_cell <- ifelse(data$B_cell > cutpoint, "HIGH", "LOW")
fit_B_cell <- survfit(Surv(overall_survival, deceased) ~ B_cell, data = data)
ggsurvplot(fit_B_cell,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ B_cell, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ B_cell, data = data)
##
## coef exp(coef) se(coef) z p
## B_cell -0.2520 0.7772 0.2897 -0.87 0.384
##
## Likelihood ratio test=0.84 on 1 df, p=0.3581
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`B cell`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "B_cell"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
B_cell_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "B_cell"
)
B_cell_negative_gene_df$exp.level <- sig$`B cell`
reactable(B_cell_negative_gene_df)
#ENSG00000102445: RUBCNL, ENSG00000175857: GAPT, ENSG00000122224: LY9, ENSG00000166405: RIC3
library(survival)
library(survminer)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("Fibroblast_alveolar"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$Fibroblast_alveolar <- ifelse(data$Fibroblast_alveolar > cutpoint, "HIGH", "LOW")
fit_Fibroblast_alveolar <- survfit(Surv(overall_survival, deceased) ~ Fibroblast_alveolar, data = data)
ggsurvplot(fit_Fibroblast_alveolar,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ Fibroblast_alveolar, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ Fibroblast_alveolar,
## data = data)
##
## coef exp(coef) se(coef) z p
## Fibroblast_alveolar 0.07372 1.07651 0.06647 1.109 0.267
##
## Likelihood ratio test=1.11 on 1 df, p=0.2915
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`Fibroblast alveolar`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "Fibroblast_alveolar"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
Fibroblast_alveolar_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "Fibroblast_alveolar"
)
Fibroblast_alveolar_negative_gene_df$exp.level <- sig$`Fibroblast alveolar`
reactable(Fibroblast_alveolar_negative_gene_df)
#ENSG00000157734: SNX22, ENSG00000035664: DAPK2, ENSG00000154065: ANKRD29, ENSG00000114790: ARHGEF26
library(survival)
library(survminer)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("Macrophage_alveolar"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$Macrophage_alveolar <- ifelse(data$Macrophage_alveolar > cutpoint, "HIGH", "LOW")
fit_Macrophage_alveolar <- survfit(Surv(overall_survival, deceased) ~ Macrophage_alveolar, data = data)
ggsurvplot(fit_Macrophage_alveolar,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ Macrophage_alveolar, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ Macrophage_alveolar,
## data = data)
##
## coef exp(coef) se(coef) z p
## Macrophage_alveolar -0.03434 0.96624 0.05707 -0.602 0.547
##
## Likelihood ratio test=0.37 on 1 df, p=0.542
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`Macrophage alveolar`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "Macrophage_alveolar"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
Macrophage_alveolar_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "Macrophage_alveolar"
)
Macrophage_alveolar_negative_gene_df$exp.level <- sig$`Macrophage alveolar`
reactable(Macrophage_alveolar_negative_gene_df)
#ENSG00000157734: SNX22, ENSG00000035664: DAPK2, ENSG00000154065: ANKRD29, ENSG00000114790: ARHGEF26
library(survival)
library(survminer)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("Neutrophils"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$Neutrophils <- ifelse(data$Neutrophils > cutpoint, "HIGH", "LOW")
fit_Neutrophils<- survfit(Surv(overall_survival, deceased) ~ Neutrophils, data = data)
ggsurvplot(fit_Neutrophils,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ Neutrophils, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ Neutrophils,
## data = data)
##
## coef exp(coef) se(coef) z p
## Neutrophils 0.1280 1.1365 0.1633 0.784 0.433
##
## Likelihood ratio test=0.55 on 1 df, p=0.4568
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`Neutrophils`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "Neutrophils"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
Neutrophils_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "Neutrophils"
)
Neutrophils_negative_gene_df$exp.level <- sig$`Neutrophils`
reactable(Neutrophils_negative_gene_df)
#ENSG00000157734: SNX22, ENSG00000035664: DAPK2, ENSG00000154065: ANKRD29, ENSG00000114790: ARHGEF26
library(survival)
library(survminer)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("Pericyte"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$Pericyte <- ifelse(data$Pericyte > cutpoint, "HIGH", "LOW")
fit_Pericyte<- survfit(Surv(overall_survival, deceased) ~ Pericyte, data = data)
ggsurvplot(fit_Pericyte,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ Pericyte, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ Pericyte,
## data = data)
##
## coef exp(coef) se(coef) z p
## Pericyte -0.2611 0.7702 0.1617 -1.615 0.106
##
## Likelihood ratio test=2.67 on 1 df, p=0.1023
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`Pericyte`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "Pericyte"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
Pericyte_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "Pericyte"
)
Pericyte_negative_gene_df$exp.level <- sig$`Pericyte`
reactable(Pericyte_negative_gene_df)
library(survival)
library(survminer)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("Mast_cell"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$Mast_cell <- ifelse(data$Mast_cell > cutpoint, "HIGH", "LOW")
fit_Mast_cell<- survfit(Surv(overall_survival, deceased) ~ Mast_cell, data = data)
ggsurvplot(fit_Mast_cell,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ Mast_cell, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ Mast_cell,
## data = data)
##
## coef exp(coef) se(coef) z p
## Mast_cell 0.6361 1.8891 0.6100 1.043 0.297
##
## Likelihood ratio test=1 on 1 df, p=0.3174
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`Mast cell`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "Mast_cell"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
Mast_cell_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "Mast_cell"
)
Mast_cell_negative_gene_df$exp.level <- sig$`Mast cell`
reactable(Mast_cell_negative_gene_df)
library(survival)
library(survminer)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("Mesothelial"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$Mesothelial <- ifelse(data$Mesothelial > cutpoint, "HIGH", "LOW")
fit_Mesothelial<- survfit(Surv(overall_survival, deceased) ~ Mesothelial, data = data)
ggsurvplot(fit_Mesothelial,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ Mesothelial, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ Mesothelial,
## data = data)
##
## coef exp(coef) se(coef) z p
## Mesothelial 0.01638 1.01652 0.01515 1.081 0.28
##
## Likelihood ratio test=0.91 on 1 df, p=0.3408
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`Mesothelial`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "Mesothelial"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
Mesothelial_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "Mesothelial"
)
Mesothelial_negative_gene_df$exp.level <- sig$`Mesothelial`
reactable(Mesothelial_negative_gene_df)
library(survival)
library(survminer)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("T_cell_CD8_activated"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$T_cell_CD8_activated <- ifelse(data$T_cell_CD8_activated > cutpoint, "HIGH", "LOW")
fit_T_cell_CD8_activated<- survfit(Surv(overall_survival, deceased) ~ T_cell_CD8_activated, data = data)
ggsurvplot(fit_T_cell_CD8_activated,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ T_cell_CD8_activated, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ T_cell_CD8_activated,
## data = data)
##
## coef exp(coef) se(coef) z p
## T_cell_CD8_activated 0.09551 1.10022 0.07094 1.346 0.178
##
## Likelihood ratio test=1.52 on 1 df, p=0.2175
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`T cell CD8 activated`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "T_cell_CD8_activated"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value < 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
T_cell_CD8_activated_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "T_cell_CD8_activated"
)
T_cell_CD8_activated_negative_gene_df$exp.level <- sig$`T cell CD8 activated`
reactable(T_cell_CD8_activated_negative_gene_df)
library(survival)
library(survminer)
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("T_cell_CD8_dividing"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$T_cell_CD8_dividing <- ifelse(data$T_cell_CD8_dividing > cutpoint, "HIGH", "LOW")
fit_T_cell_CD8_dividing <- survfit(Surv(overall_survival, deceased) ~ T_cell_CD8_dividing, data = data)
ggsurvplot(fit_T_cell_CD8_dividing,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ T_cell_CD8_dividing, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ T_cell_CD8_dividing,
## data = data)
##
## coef exp(coef) se(coef) z p
## T_cell_CD8_dividing 0.18378 1.20176 0.08876 2.07 0.0384
##
## Likelihood ratio test=3.98 on 1 df, p=0.04618
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`T cell CD8 dividing`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "T_cell_CD8_dividing"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value > 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
T_cell_CD8_dividing_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "T_cell_CD8_dividing"
)
T_cell_CD8_dividing_negative_gene_df$exp.level <- sig$`T cell CD8 dividing`
reactable(T_cell_CD8_dividing_negative_gene_df)
#ENSG00000228716: DHFR, ENSG00000148019: CEP78, ENSG00000127152: BCL11B, ENSG00000134545: KLRC1
library(survival)
library(survminer)
adeno <- readRDS("~/final_results/14_02_2025_deconvolution_adeno.rds")
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
deconvolution_adeno_scaled <- adeno / rowSums(adeno) * 100
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("T_cell_NK_like"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$T_cell_NK_like <- ifelse(data$T_cell_NK_like > cutpoint, "HIGH", "LOW")
fit_T_cell_NK_like <- survfit(Surv(overall_survival, deceased) ~ T_cell_NK_like, data = data)
ggsurvplot(fit_T_cell_NK_like,
data,
pval = TRUE,
risk.table = TRUE)
data <- cbind(luad_clinic_for_bulk, deconvolution_adeno_scaled)
colnames(data) <- gsub(" ", "_", colnames(data))
colnames(data) <- gsub("-", "_", colnames(data))
coxa <- coxph(Surv(overall_survival, deceased) ~ T_cell_NK_like, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ T_cell_NK_like,
## data = data)
##
## coef exp(coef) se(coef) z p
## T_cell_NK_like 0.006271 1.006290 0.085374 0.073 0.941
##
## Likelihood ratio test=0.01 on 1 df, p=0.9416
## n= 91, number of events= 91
## (152 observations deleted due to missingness)
library(survival)
library(reactable)
signature_adeno_matrix <- readRDS("~/final_results/14_02_2025_signature_adeno_matrix.rds")
signature_adeno_matrix <- as.data.frame(signature_adeno_matrix)
Alveolar_exp <- signature_adeno_matrix$`T cell NK-like`
other_cells_exp <- rowMeans(signature_adeno_matrix[, colnames(signature_adeno_matrix) != "T_cell_NK_like"])
Alveolar_luad_filtered_genes <- signature_adeno_matrix[(Alveolar_exp - other_cells_exp) / other_cells_exp > 0.90, ]
Adeno_bulk <- readRDS("~/final_results/luad_bulk.rds")
Alveolar <- intersect(rownames(Alveolar_luad_filtered_genes), rownames(Adeno_bulk))
Alveolar <- Adeno_bulk[Alveolar,]
Alveolar <- t(Alveolar)
Alveolar <- as.data.frame(Alveolar)
LUAD_BULK_CLINIC <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
LUAD_BULK_CLINIC <- as.data.frame(LUAD_BULK_CLINIC)
select_col <- c("overall_survival", "vital_status")
clinic <- LUAD_BULK_CLINIC[,select_col]
data <- cbind(clinic, Alveolar)
data$deceased <- ifelse(data$vital_status == "Alive", FALSE, TRUE)
library(survival)
exclude_cols <- c("overall_survival", "vital_status", "deceased")
gene_cols <- setdiff(colnames(data), exclude_cols)
cox_results <- list()
for (gene in gene_cols) {
formula <- as.formula(paste("Surv(overall_survival, deceased) ~", gene))
cox_model <- coxph(formula, data = data)
cox_results[[gene]] <- summary(cox_model)
}
negative_genes <- c()
negative_coeffs <- c()
for (gene in names(cox_results)) {
coef_value <- cox_results[[gene]]$coefficients[1, "coef"]
if (!is.na(coef_value) && coef_value > 0) {
negative_genes <- c(negative_genes, gene)
negative_coeffs <- c(negative_coeffs, coef_value)
}
}
library(biomaRt)
ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = negative_genes,
mart = ensembl)
ort <- intersect(negative_genes, rownames(signature_adeno_matrix))
sig <- signature_adeno_matrix[ort, ]
T_cell_NK_like_negative_gene_df <- data.frame(
ENSG_ID = negative_genes,
Gene_Name = gene_annotation$hgnc_symbol,
Coefficient = negative_coeffs,
Cell_Type = "T_cell_NK_like"
)
colnames(sig) <- gsub("-", "_", colnames(sig))
T_cell_NK_like_negative_gene_df$exp.level <- sig$`T cell NK_like`
reactable(T_cell_NK_like_negative_gene_df)
#ENSG00000180739: S1PR5, ENSG00000101695: RNF125, ENSG00000100351: GRAP2, ENSG00000169252: ADRB2
library(survival)
library(survminer)
library(ggplot2)
df_list <- list(
T_cell_NK_like_negative_gene_df,
T_cell_CD8_dividing_negative_gene_df,
T_cell_CD8_naive_negative_gene_df,
T_cell_NK_like_negative_gene_df,
Mesothelial_negative_gene_df,
Mast_cell_negative_gene_df,
Pericyte_negative_gene_df,
Neutrophils_negative_gene_df,
Macrophage_alveolar_negative_gene_df,
Macrophage_negative_gene_df,
Fibroblast_alveolar_negative_gene_df,
B_cell_negative_gene_df
)
df_names <- c(
"T_cell_NK_like",
"T_cell_CD8_dividing",
"T_cell_CD8_naive",
"T_cell_NK_like",
"Mesothelial",
"Mast_cell",
"Pericyte",
"Neutrophils",
"Macrophage_alveolar",
"Macrophage",
"Fibroblast_alveolar",
"B_cell"
)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:biomaRt':
##
## select
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
select <- unique(unlist(lapply(df_list, function(df) {
if (!is.null(df)) {
df %>%
arrange(desc(exp.level)) %>%
head(5) %>%
pull(ENSG_ID)
}
})))
adeno <- readRDS("~/final_results/luad_bulk.rds") |> as.data.frame() |> t() |> as.data.frame()
luad_clinic_for_bulk <- readRDS("~/final_results/luad_clinic_for_bulk.rds")
data <- cbind(luad_clinic_for_bulk, adeno)
data$deceased <- ifelse(data$vital_status == "Alive", 0, 1)
ensembl_ids <- select
mart <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl", version = 105)
gene_map <- getBM(
attributes = c("ensembl_gene_id", "external_gene_name"),
filters = "ensembl_gene_id",
values = ensembl_ids,
mart = mart
)
colnames(data)[colnames(data) %in% gene_map$ensembl_gene_id] <- gene_map$external_gene_name
data <- cbind(luad_clinic_for_bulk, data[, gene_map$external_gene_name])
data$overall_survival <- as.numeric(data$overall_survival)
univ_cox <- function(variable) {
formula <- reformulate(variable, response = "Surv(overall_survival, deceased)")
cox_model <- coxph(formula, data = data)
cox_summary <- summary(cox_model)
c(
coef = cox_summary$coefficients[1],
HR = exp(cox_summary$coefficients[1]),
p_value = cox_summary$coefficients[5],
CI_lower = exp(cox_summary$conf.int[ ,3]),
CI_upper = exp(cox_summary$conf.int[ ,4])
)
}
univ_results <- lapply(colnames(data[, 8:ncol(data)]), univ_cox)
univ_results_df <- do.call(rbind, univ_results) |> as.data.frame()
rownames(univ_results_df) <- colnames(data[, 8:ncol(data)])
gen_expression <- data[, 8:ncol(data)]
coef_values <- univ_results_df$coef
risk_scores <- apply(gen_expression, 1, function(x) sum(x * coef_values))
risk_scores <- as.data.frame(risk_scores)
data$risk_scores <- risk_scores$risk_scores
res.cut <- surv_cutpoint(data, time = "overall_survival", event = "deceased",
variables = c("risk_scores"))
cutpoint <- res.cut[["cutpoint"]][["cutpoint"]]
data$risk_scores <- ifelse(data$risk_scores > cutpoint, "HIGH", "LOW")
fit_risk_scores <- survfit(Surv(overall_survival, deceased) ~ risk_scores, data = data)
ggsurvplot(fit_risk_scores, data, pval = TRUE, risk.table = TRUE)
coxa <- coxph(Surv(overall_survival, deceased) ~ risk_scores, data = data)
coxa
## Call:
## coxph(formula = Surv(overall_survival, deceased) ~ risk_scores,
## data = data)
##
## coef exp(coef) se(coef) z p
## risk_scoresLOW -0.6988 0.4972 0.2279 -3.066 0.00217
##
## Likelihood ratio test=9.95 on 1 df, p=0.00161
## n= 91, number of events= 91
## (152 observations deleted due to missingness)