logo_1.png mlg_web.png

Mining and analysis of genomic and epigenomic data (TCGA) using R

Where: Biopark Charleroi , Gosselies

When: December, 2016

 

 

Experts: Catharina Olsen and Antonio Colaprico

Academic supervisor: Gianluca Bontempi
Machine Learning Group,

Interuniversity Institute of Bioinformatics in Brussels, Universitˇ Libre de Bruxelles, Belgium


 

 

Planning & Materials

¤  Day 1

o   Introduction to R (1.5h): slides code script1.R data

o   Analyses (3.5h) slides code expression data GO annotation

o   Introduction

o   Differential expression analysis

o   Enrichment analysis

o   Clustering, dendrograms and heatmaps

o   Principal component analysis

o   Survival analysis

o   NGS data (1h): slides code

o   Introduction

o   TCGA

o   TCGAbiolinks slides code

¤  Day 2

o   TCGAbiolinks functionalities in R (3h): slides code data

o   Different cancer datatypes

o   Data retrieval code session 2

o   Functionality overview

o   Case studies (focus on Case Study 1: BRCA GE and Survival)

o   Analyses with graphical interface: TCGAbiolinksGUI (3h): slides data

o   Integrative analysis: epigenomic and transcriptomics


¤  R libraries: day 1 day 2 TCGAbiolinksGUI

¤  Package vignette and vignette source

¤  Package workflow

 

R software

Cran
Rstudio
R Reference Card
Swirl - Learn R, in R
Datacamp.com
Data Mining Algorithms In R
R-bloggers.com
One Page R: A Survival Guide to Data Science with R
Introduction to Machine Learning
rdatamining.com
Google's R Style Guide
Advanced R programming
A Guide to Speeding Up R Code for Busy People
The R inferno
Resources to help you learn and use R
Tools for Reproducible Research