Survival Analysis with TCGA Data in R - Create Kaplan-Meier Curves

Learn how to perform survival analysis using TCGA data in R, focusing on creating Kaplan-Meier curves. Explore the concept of survival analysis, its applications, and essential components like censoring. Discover methods for survival analysis and how to interpret Kaplan-Meier curves. Follow step-by-step instructions to download clinical data from the GDC portal, prepare gene expression data, and perform variance stabilization transformation. Master techniques for dividing cohorts based on expression levels, defining strata, and computing survival curves using survfit() and ggsruvplot(). Gain practical skills in utilizing the survival and survminer packages to conduct comprehensive survival analysis in R.

Intro
Intuition behind survival analysis
Why do we perform survival analysis?
What is Censoring and why is it important?
What is considered as an event?
Methods for survival analysis
How to read a Kaplan-Meier curve?
Question to answer using survival analysis
3 things required for survival analysis
Download clinical data from GDC portal
Getting status information and censoring data
Set up an “overall survival” i.e. time for each patient in the cohort
For event/strata information for each patient, fetch gene expression data from GDC portal
Build query using GDCquery
Download data using GDCdownload
Extract counts using GDCprepare
Perform Variance Stabilization Transformation vst on counts before further analysis
Wrangle data to get the relevant data and data in the right shape
Approaches to divide cohort into 2 groups based on expression
Bifurcating patients into low and high TP53 expression groups
Define strata for each patient
Compute a survival curve using survfit and creating a Kaplan-Meier curve using ggsruvplot
survfit vs survdiff