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