Detailed learning objectives

** Session 1 – Introduction (Mon, Oct 30, 11 AM -2 PM, Berlin time)


- learn how to import text (e.g., 'comma-separated values') files into R,
- learn how to perform manipulations of R data frames,
- learn how to apply R functions for statistical analysis and visualization,
- learn how to use R packages to extend basic functionality
- learn to create high-quality graphics with base R plot & ggplot2

 

 

** Session 2 – Hypothesis testing (Wed, Nov 01, 11 AM -2 PM, Berlin time)


- get familiar with the statistical machinery of hypothesis testing, its vocabulary, its purpose, and its strengths and limitations
- learn how to conduct frequently used tests in R and how to interpret the results

 

 

** Session 3 - Introduction to Bioconductor (Fri, Nov 3, 3-6 PM, Berlin time)

 

- learn how to use rtracklayer::import() to read genomic files
(e.g. BED, GTF, VCF, FASTA) into Bioconductor objects
- learn how to work with genomic region data (exons, genes, ChIP peaks, copy number variants, …) in Bioconductor
- learn how to find regions of overlap between experimentally-derived genomic regions (eg. CNV coordinates provided as BED) and functional genomic regions (eg. gene coordinates provided as GTF)

 


** Session 4 - Tidyverse (Mon, Nov 6, 3-6 PM, Berlin time)


 - Motivation and introduction to tidy analysis
- Useful packages and paradigms, integration with ggplot2
- Why tidy analysis works for genomics

 


** Session 5 - RNA-seq data analysis (Wed, Nov 8, 12-3 PM, Berlin time)


- get familiar with core concepts and key terminology of RNA-seq analysis
- understand the essential concepts of read mapping, counts computation, normalization and differential expression analysis and get to know selected R/Bioconductor packages for that purpose (Rsubread, EDASeq, edgeR, DESeq2)

 

 

** Session 6 - Genomic Data Visualisation (Fri, Nov 10, 3-6 PM, Berlin time)


- Visualization of genomic region data using the Gviz package

- Visualization of gene expression data using the ComplexHeatmap package



** Session 7  - Diffferential expression analysis (Mon, Nov 13, 12-3 PM, Berlin time)


- understand what multiple testing means
- understand the false discovery rate and how to apply it to a vector of p-values in R
- learn how to conduct a differential expression analysis with DESeq2 and how  to interpret the results
- understand similarities and differences to differential expression analysis with edgeR or limma/voom



** Session 8 - Gene set analysis (Wed, Nov 15, 12-3 PM, Berlin time)


- understand what are gene sets and pathways,  and what are major resources for obtaining them (GO / KEGG)
- understand the basic statistical concepts underlying GO/KEGG overrepresentation analysis
- learn how to perform permutation-based gene set enrichment analysis using the EnrichmentBrowser package

 

 

** Session 9 - Bioconductor tidy workflows (Fri, Nov 17, 3-6 PM, Berlin time)


- Tidy analysis of GenomicRanges datasets
- Genomic overlaps as "joins"
- Performing genomic enrichment analysis with bootstrapping and matching (nullranges package)
- Tidy analysis for transcriptomics: tidybulk, tidySingleCellExperiment, tidyseurat