News:RNA sequencing data with R/Bioconductor
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>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
R Bioconductor RNAseq • 133 views
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