Course: Analysis of RNA sequencing data with R/Bioconductor

Where: Freie Universitat Berlin (Germany)

When: 22-26 June 2020

This course will provide biologists and bioinformaticians with practical statistical analysis skills to perform rigorous analysis of RNAseq data with R and Bioconductor. The course assumes basic familiarity with genomics, but does not assume prior statistical training. It covers the statistical concepts necessary to design experiments and analyze high-throughput data generated by next-generation sequencing, including: exploratory data analysis, principal components analysis, clustering, differential expression, and gene set analysis.

Session 1 – Introduction

Monday - 09:30 to 17:30

Lecture 1: Data distributions

• random variables
• distributions
• population and samples

Hands-On 1: Introduction to R

Lecture 2: Creating high-quality graphics in R

• Visualizing data in 1D, 2D & more than two dimensions
• Heatmaps
• Data transformations

Hands-On 2: Graphics with base R and ggplot2

Session 2 – Hypothesis testing

Tuesday - 09:30 to 17:30

Lecture 1: Hypothesis testing theory

• type I and II error and power
• multiple hypothesis testing: false discovery rate, familywise error rate
• exploratory data analysis (EDA)

Hands-On 1: Standard tests & EDA

Lecture 2: Hypothesis testing in practice

• hypothesis tests for categorical variables (chi-square, Fisher's exact)
• Monte Carlo simulation
• Permutation tests

Hands-On 2: Permutation tests

Session 3 - Bioconductor

Wednesday – Classes from 09:30 to 17:30

Lecture 1: Introduction to Bioconductor

• Incorporating Bioconductor in your data analysis
• ExpressionSet / SummarizedExperiment
• Annotation resources

Hands-On 1: Leveraging Bioconductor annotation resources

Lecture 2: Genomic intervals

• Introduction to genomic region algebra
• Basic operations: construction, intra- and inter-region operations
• Finding overlaps

Hands-On 2: Solving common bioinformatic challenges with GenomicRanges

Session 4 - Next-generation sequencing data

Thursday - 09:30 to 17:30

Lecture 1: High-throughput count data

• Characteristics of count data
• Exploring count data
• Modeling count data

Hands-On 1: Analyzing next-generation sequencing data

Lecture 2: Clustering and Principal Components Analysis

• Measures of similarity
• Hierarchical clustering
• Dimension reduction
• Principal components analysis (PCA)

Hands-On 2: Clustering & PCA

Session 5 - Differential expression and gene set analysis

Friday - 09:30 to 17:30

Lecture 1 - Differential expression analysis

• Normalization
• Experimental designs
• Generalized linear models

Lab 1: Performing differential expression analysis with DESeq2

Lecture 2 - Gene set analysis

• A primer on terminology, existing methods & statistical theory
• GO/KEGG overrepresentation analysis
• Functional class scoring & permutation testing
• Network-based enrichment analysis

Lab 2: Performing gene set enrichment analysis with the EnrichmentBrowser