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

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

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Intro

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1 of 23

Intro

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

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  1. 1 Intro
  2. 2 Intuition behind survival analysis
  3. 3 Why do we perform survival analysis?
  4. 4 What is Censoring and why is it important?
  5. 5 What is considered as an event?
  6. 6 Methods for survival analysis
  7. 7 How to read a Kaplan-Meier curve?
  8. 8 Question to answer using survival analysis
  9. 9 3 things required for survival analysis
  10. 10 Download clinical data from GDC portal
  11. 11 Getting status information and censoring data
  12. 12 Set up an “overall survival” i.e. time for each patient in the cohort
  13. 13 For event/strata information for each patient, fetch gene expression data from GDC portal
  14. 14 Build query using GDCquery
  15. 15 Download data using GDCdownload
  16. 16 Extract counts using GDCprepare
  17. 17 Perform Variance Stabilization Transformation vst on counts before further analysis
  18. 18 Wrangle data to get the relevant data and data in the right shape
  19. 19 Approaches to divide cohort into 2 groups based on expression
  20. 20 Bifurcating patients into low and high TP53 expression groups
  21. 21 Define strata for each patient
  22. 22 Compute a survival curve using survfit and creating a Kaplan-Meier curve using ggsruvplot
  23. 23 survfit vs survdiff

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