4-15 November 2024

To foster international participation, this course will be held online

This course will provide biologists and bioinformaticians with practical  statistical analysis skills to perform rigorous analysis of high-throughput  genomic data. The course assumes basic familiarity with genomics and with R  programming, but does not assume prior statistical training.  It covers the statistical concepts necessary to analyze genomic and transcriptomic  high-throughput data generated by next-generation sequencing, including:  hypothesis testing, data visualization, genomic region analysis, differential  expression analysis, and gene set analysis.

●             Modern Statistics for Modern Biology (by Holmes and Huber)

●             The Bioconductor 2018 Workshop collection

** Session 1 – Introduction (Nov 4, 12-3 PM, Berlin time)

- Introduction to R / RStudio
- Creating high-quality graphics in R

** Session 2 – Hypothesis testing (Nov 6, 12-3 PM, Berlin time)

- CDF, p-value, binomial test
- types of error, t-test, permutation test

** Session 3 - Introduction to Bioconductor (Nov 8, 12-3 PM, Berlin time)

- Introduction to Bioconductor
- Working with genomic region data in Bioconductor (GenomicRanges)

 
** Session 4 - RNA-seq data analysis (Nov 11, 12-3 PM, Berlin time)

- Characteristics of RNA-seq data
- Storing and analyzing RNA-seq data in Bioconductor (SummarizedExperiment)

** Session 5 - Differential expression analysis (Mon, Nov 13, 12-3 PM, Berlin time)

- Multiple hypothesis testing
- Performing differential expression analysis with DESeq2

** Session 6 - Gene set analysis (Wed, Nov 15, 12-3 PM, Berlin time)

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