Detailed learning objectives

** Session 1 (Mon, Nov 01):


- 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 (Wed, Nov 03): Hypothesis testing fundamentals


- 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 (Fri, Nov 05): Genomic region analysis


- 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 (Mon, Nov 08): RNA-seq data analysis


- 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 5 (Wed, Nov 10): Diffferential expression analysis


- 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 6 (Fri, Nov 12): Gene set analysis


- 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