Thanks it is interesting to add Survival plots
The phenoTest menu may has in part what do you expect (without curve). It is possible to cluster a list of genes significantly
regulated (gene expression) at a range of tumor size (continuous variable) or tumoral stage (ordinal variable, Figure 12) for recurred or DiseaseFree cases (survival and categorical).
The Figure 14 associate Overall Survival Status of patients depending on the fold change of gene expression and the Level of PSA in prostate cancer. The two last columns show opposite sign between the PSA fold change and the living status which is expect in medicine
YES, It is easy to add this plots. In the next release I will add Survival function in plot menu :)
here two examples from survival package:
##fit a Kaplan-Meier and plot it
fit <- survfit(Surv(OS_MONTHS,OS_
plot(fit, lty=2:3, xlab="OS_MONTHS", ylab="OS_STATUS: Survival", main="Lung Squamous Cell Carcinoma (TCGA)")
legend(60, 1.1, c("DiseaseFree", "Recurred/Progressed"), lty = 2:3)
# fit a Cox proportional hazards model and plot the
#predicted survival for a 60 year old
fit<- coxph(Surv(OS_MONTHS, OS_STATUS)~ AGE, data= ClinicData)
plot(survfit(fit, newdata=data.frame(AGE=60)), xlab = "OS_MONTHS", ylab="OS_STATUS: Survival", main="Lung Squamous Cell Carcinoma (TCGA)")
text(90,0.95,"Cox: Predicted survival for a 60 year old", cex=1)
Please tell me which others Survival models are useful for biologists.
On Fri, Mar 27, 2015 at 4:00 AM, Dario Strbenac <firstname.lastname@example.org> wrote:
An analysis that biologists would be interested in is a Kaplan-Meier analysis with a survival time plot. Can the package do this ? It's the most obvious way to integrate gene expression (allow the samples to be put into a low expression and high expression group, based on a threshold) and clinical data, but I don't see how it could be done with canceR.
University of Sydney
Camperdown NSW 2050