problem for gene survival analysis through “Survival” package in R
My data belong to subtype of TCGA breast cancer. I have to do survival analysis for this data set. I know “survival” package but I don’t know how can I involve genes profiles to survival analysis. Surly, for OS analysis, I can use “survfit” as a function for survival analysis and by “Time” and “OS status” I can get my target. but really, I don’t know how to input my transcriptom profile as input in my survival analysis. In other words, I want to know how can I gene survival analysis through “survival” package or another nice package. due to this research, I found “RTCGA” as a bioconductor package but it’s not possible to customize arguments of “survivalTCGA” for for survival analysis about subtype of breasat cancer.
I deeply appreciate if you share your comment with me.
I do survival analysis with (patient) consensus cluster as a independent variable explaining survival time. This is an example.
coxph(Surv(time = Time, event = Death) ~ as.factor(myresults$consensuscluster), data = myresults, ties = "exact")
Perhaps this helps. I’d cluster your data first which assigns clusters to every patient, then go from there. Then the genes associating with a cluster can be analysed separately. Good class discovery/ clustering packages in my experiments on a single platform have been CLEST and M3C.
The main strategy here is to first use the information of the gene to stratify the patients into different groups (for example: High gene expression group vs. Low gene expression group, or Mutated gene group vs. Non-mutated gene group, etc)and only then perform the survival analysis. Then call the following survfit() function
OS_time: overall survival time;
Vital Status (Dead=1, Alive=0),
Patient_group: Grouping of patient using information of the gene of interest.
df=your data frame containing the data.
Hi! now you can do this task very easily with my recently developed tool named ‘geneSA’ (github.com/huynguyen250896/geneSA). Its output will automatically report genes statistically significant with survival outcome. Give it a try 😉