Session content

Session 1 – Introduction

 

Monday  - 2-8 pm Berlin time

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 -   2-8 pm Berlin time

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  – 2-8 pm Berlin time


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 -  2-8 pm Berlin time
 
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  -  2-8 pm Berlin time

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