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Dealing with Missing Data in R

LiquidBrain Bioinformatics via YouTube

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Overview

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This course explains how to handle missing data in R, covering missingness types, simple and advanced imputation methods, and ways to compare their effectiveness. It includes a practical example using TCGA gene-expression data and the mice package.

Syllabus

Introduction
What's imputation
Types of missing data
Measuring success
A number of different imputation techniques
R Script: introduction of the rmd format
Mean Imputation
locf and nocb
kNN and kNN imputation
Advance imputation with mice
How does pmm and rf performed?
TCGA data Imputation
Effectiveness of Imputation

Taught by

LiquidBrain Bioinformatics

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