Analysis of RNA sequencing data with R/Bioconductor


30 October -17 November 2023


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.




Come to the first class with the following installed:



             R and Bioconductor:


             R Studio:



             Modern Statistics for Modern Biology (by Holmes and Huber)


             The Bioconductor 2018 Workshop collection



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

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


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

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

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

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



** 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)

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



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

- Multiple hypothesis testing
- Performing differential expression analysis with DESeq2

** Session 8 - 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


** 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



Dr. Ludwig Geistlinger

(Center for Computational Biomedicine, Harvard Medical School, USA)



Dr. Michael Love

(UNC-Chapel Hill, USA)


Cost overview

Package 1


530 €



Cancellation Policy:


> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.


Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.