>Analysis of RNA sequencing data with R/Bioconductor
ONLINE - November 01-12, 2021
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.
Program
** Session 1 – Introduction (Mon, Nov 01, 3-6 PM, Berlin time)
- Introduction to R / RStudio
- Creating high-quality graphics in R
** Session 2 – Hypothesis testing (Wed, Nov 03, 3-6 PM, Berlin time)
- CDF, p-value, binomial test
- types of error, t-test, permutation test
** Session 3 - Bioconductor (Fri, Nov 05, 3-6 PM, Berlin time)
- Introduction to Bioconductor
- Working with genomic region data in Bioconductor (GenomicRanges)
** Session 4 - RNA-seq data analysis (Mon, Nov 08, 3-6 PM, Berlin time)
- Characteristics of RNA-seq data
- Storing and analyzing RNA-seq data in Bioconductor (SummarizedExperiment)
** Session 5 - Differential expression analysis (Wed, Nov 03, 3-6 PM, Berlin time)
- Multiple hypothesis testing
- Performing differential expression analysis with DESeq2
** Session 6 - Gene set analysis (Fri, Nov 12, 3-6 PM, Berlin time)
- A primer on terminology, existing methods & statistical theory
- GO/KEGG overrepresentation analysis
- Functional class scoring & permutation testing
