Survival analysis with TCGA data in R | Create Kaplan-Meier Curves

In this video I talk about the concept of survival analysis, what questions does it help to answer and what data do we need to perform this analysis. I also discuss important concepts like censoring and how it is perform and explain how to interpret Kaplan-Meier curves. Lastly, I demonstrate how to perform survival analysis in R using survival and survminer packages.
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Chapters:
0:00 Intro
0:35 Intuition behind survival analysis
2:21 Why do we perform survival analysis?
3:57 What is Censoring and why is it important?
6:14 What is considered as an event?
6:35 Methods for survival analysis
8:03 How to read a Kaplan-Meier curve?
10:31 Question to answer using survival analysis
10:53 3 things required for survival analysis
12:08 Download clinical data from GDC portal
15:57 Getting status information and censoring data
17:31 Set up an “overall survival” (i.e. time) for each patient in the cohort
19:01 For event/strata information for each patient, fetch gene expression data from GDC portal
19:33 Build query using GDCquery()
22:45 Download data using GDCdownload()
23:14 Extract counts using GDCprepare()
25:07 Perform Variance Stabilization Transformation (vst) on counts before further analysis
27:38 Wrangle data to get the relevant data and data in the right shape
33:11 Approaches to divide cohort into 2 groups based on expression
34:41 Bifurcating patients into low and high TP53 expression groups
34:57 Define strata for each patient
38:41 Compute a survival curve using survfit() and creating a Kaplan-Meier curve using ggsruvplot()
41:30 survfit() vs survdiff()

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To get in touch:
Website: https://bioinformagician.org/
Github: https://github.com/kpatel427
Email: [email protected]


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