## Loading required package: ggplot2
## Loading required package: ggpubr
library(survival)
##
## Attaching package: 'survival'
## The following object is masked from 'package:survminer':
##
## myeloma
score_df_adeno$Hot_Tumor_Sum <- score_df_adeno$B_Sum + score_df_adeno$T_Sum + score_df_adeno$M_Sum
threshold <- 0.35
score_df_adeno$Tumor_Type <- ifelse(score_df_adeno$Cold_Tumor_Sum > threshold, "Cold_Tumor", "Hot_Tumor")
luad_data <- cbind(adeno_data, score_df_adeno)
fit_stage <- survfit(Surv(overall_survival, deceased) ~ Tumor_Type, data = luad_data)
library(survminer)
ggsurvplot(fit_stage,
data = luad_data,
pval = TRUE,
risk.table = TRUE,
title = "LUAD Kaplan-Meier Survival Curves by Hot or Cold")
## Call:
## survdiff(formula = Surv(overall_survival, deceased) ~ Tumor_Type,
## data = luad_data)
##
## n=319, 8 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## Tumor_Type=Cold_Tumor 260 115 104.6 1.03 4.85
## Tumor_Type=Hot_Tumor 59 18 28.4 3.79 4.85
##
## Chisq= 4.8 on 1 degrees of freedom, p= 0.03
## Loading required package: grid
## ========================================
## ComplexHeatmap version 2.18.0
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
##
## If you use it in published research, please cite either one:
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
##
##
## The new InteractiveComplexHeatmap package can directly export static
## complex heatmaps into an interactive Shiny app with zero effort. Have a try!
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(ComplexHeatmap))
## ========================================
## ========================================
## circlize version 0.4.16
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
##
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(circlize))
## ========================================
## The automatically generated colors map from the 1^st and 99^th of the
## values in the matrix. There are outliers in the matrix whose patterns
## might be hidden by this color mapping. You can manually set the color
## to `col` argument.
##
## Use `suppressMessages()` to turn off this message.
## corrplot 0.94 loaded
score_df_scc$Hot_Tumor_Sum <- score_df_scc$B_Sum + score_df_scc$T_Sum + score_df_scc$M_Sum
threshold <- 0.46
score_df_scc$Tumor_Type <- ifelse(score_df_scc$Cold_Tumor_Sum > threshold, "Cold_Tumor", "Hot_Tumor")
lusc_data <- cbind(data, score_df_scc)
fit_stage <- survfit(Surv(overall_survival, deceased) ~ Tumor_Type, data = lusc_data)
library(survminer)
ggsurvplot(fit_stage,
data = lusc_data,
pval = TRUE,
risk.table = TRUE,
title = "LUSC Kaplan-Meier Survival Curves by Hot or Cold")
## Call:
## survdiff(formula = Surv(overall_survival, deceased) ~ Tumor_Type,
## data = lusc_data)
##
## n=457, 6 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## Tumor_Type=Cold_Tumor 139 62 53.7 1.280 1.79
## Tumor_Type=Hot_Tumor 318 130 138.3 0.497 1.79
##
## Chisq= 1.8 on 1 degrees of freedom, p= 0.2
## The automatically generated colors map from the 1^st and 99^th of the
## values in the matrix. There are outliers in the matrix whose patterns
## might be hidden by this color mapping. You can manually set the color
## to `col` argument.
##
## Use `suppressMessages()` to turn off this message.