Analysis of RNA sequencing data with R/Bioconductor

Dates

30 October -17 November 2023

 

To foster international participation, this course will be held online

 

 

overview

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.

 

 

Preparation

Come to the first class with the following installed:

 

 

             R and Bioconductor: www.bioconductor.org/install

 

             R Studio: https://www.rstudio.com/products/rstudio/download3/

Textbook

 

             Modern Statistics for Modern Biology (by Holmes and Huber)

 

             The Bioconductor 2018 Workshop collection

 

program


** 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


Instructors

 

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.