The survival plot based on
Best separation of high and low expression samples of
Expression cutoff 23.6 FPKM looks like below (This plot is from Human Protein Atlas database)
I took the GPAM FPKM data given in the above database and merged with Clinical data. Everything is stored in a dataframe
times bcr_patient_barcode patient.vital_status FPKM 1 724 TCGA-2Y-A9GS 1 30.3 2 1624 TCGA-2Y-A9GT 1 5.6 3 1569 TCGA-2Y-A9GU 0 26.6 4 2532 TCGA-2Y-A9GV 1 18.4 5 1271 TCGA-2Y-A9GW 1 4.7 6 2442 TCGA-2Y-A9GX 0 19.4
survminer package for the cutpoint to divide low and high expression samples.
library(survminer) surv_rnaseq.cut <- surv_cutpoint( df, time = "times", event = "patient.vital_status", variables = c("FPKM") ) summary(surv_rnaseq.cut) cutpoint statistic GPAM_FPKM 23.6 2.834408
Then catogarization is done.
surv_rnaseq.cat <- surv_categorize(surv_rnaseq.cut)
Then to plot the data I did like below:
library(survival) library(RTCGA) fit <- survfit(Surv(times, patient.vital_status) ~ FPKM, data = surv_rnaseq.cat) pdf("Survival_high_vs_low.pdf", width = 10, height = 10) ggsurvplot( fit, # survfit object with calculated statistics. risk.table = TRUE, # show risk table. pval = TRUE, # show p-value of log-rank test. conf.int = TRUE, # show confidence intervals for # point estimaes of survival curves. xlim = c(0,3000), # present narrower X axis, but not affect # survival estimates. break.x.by = 1000, # break X axis in time intervals by 500. break.y.by = 0.1, ggtheme = theme_RTCGA(), # customize plot and risk table with a theme. risk.table.y.text.col = T, # colour risk table text annotations. risk.table.y.text = FALSE # show bars instead of names in text annotations # in legend of risk table ) dev.off()
The Survival plot I got looks like this Suvival plot with my analysis. Basically I used the same data which they used in Human Protein Atlas database. But the plot with my analysis look different compared to the plot in the database.
What could be the reason for this? Kaplan Meier statistics?
Any help is appreciated.