Survival Analysis using Omics data
Survival analysis is a widely used statistical method for studying time to event data. It has large scale applications in cancer.
In this course, we explain about various concepts of survival analysis such as time, events, censoring. This is followed by in depth illustration of Kaplan-Meier survival estimate and Cox Proportional Hazards Models. We will show how to leverage all omics data such as mutation, gene expression, copy number alterations data for survival analysis. We will take TCGA cancer atlas data sets for demonstration purpose. We will also show some of the advanced concepts of stratification of samples into risk groups and how to study risk assessment based on gene expression data set. Furthermore, Survival Principal Component Analysis, Penalized regression concepts will also be explored. During this course we will showcase various R packages for survival analysis along with some online portals such as cBioportal, OncoLNC etc, which utilizes multi omics data for survival analysis. This is an excellent course which is an amalgamation of statistics, multi omics and cancer biology. Course Contents
Introduction
Statistics behind Survival Analysis
Visualize survival curves Cox proportional hazards methods
Explore R packages for survival analysis
Survival multivariate analysis and risk assessment
Advanced survival analysis for prognostic biomarker identification
Survival Analysis on TCGA datasets
TCGA resources for survival analysis
Case study Duration
Extra Benefits
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