Normal vs Tumor - Kaplan Meier Survival Analysis

Question

[Gnana]

To perform survival analysis for normal vs tumor cancer sample what kind of rna seq data is to used?

• unstranded
• stranded_first
• stranded_second
• tpm_unstrand
• fpkm_unstrand
• fpkm_uq_unstrand

Which of these shoud be used and is there any normalisation to be done?

[ATpoint]

Survival analysis tumor vs normal? I can already tell you that normal survives longer than tumor. Please add some details what exactly you want to do because the question as by now makes no sense.

[Gnana]

I want to check the survival for a specific gene in normal vs tumor condition in Tcga data.

[Ram]

What do you mean by "survival of a gene"?

[Barry Digby]

Your first question - use GDCquery - it takes care of loading the expression data:

mrna_query <- GDCquery(project = "TCGA-PRAD",
                       data.category = "Transcriptome Profiling",
                       data.type = "Gene Expression Quantification",
                       workflow.type = "STAR - Counts",
                       experimental.strategy = "RNA-Seq")

GDCdownload(mrna_query, method = "api", files.per.chunk = 100,
            directory = "~/Desktop/TCGA/mRNA/")

mrna_df <- GDCprepare(mrna_query, directory = "~/Desktop/TCGA/mRNA/")

Yes normalisation is required:

y <- edgeR::DGEList(mrna_df)
y <- edgeR::calcNormFactors(y)
logcpm <- edgeR::cpm(y, normalized.lib.sizes = T, log=TRUE)

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Follow up on ATpoints comment because he is 100% correct:

Take a look at GEPIA: http://gepia.cancer-pku.cn/index.html to look at survival plots for a single gene in a TCGA dataset

In GEPIA, the two lines in the kaplan-meier plot represent patients stratified into two groups using median gene expression cutoff... each group containing normal and tumor samples...