News: RNAseq data analysis with R and Bioconductor
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10 weeks ago by
carlopecoraro21.5k wrote:

Course: Analysis of RNA sequencing data with R/Bioconductor

Where: Freie Universitat Berlin (Germany)

When: 22-26 June 2020

This course will provide biologists and bioinformaticians with practical statistical analysis skills to perform rigorous analysis of RNAseq data with R and Bioconductor. The course assumes basic familiarity with genomics, but does not assume prior statistical training. It covers the statistical concepts necessary to design experiments and analyze high-throughput data generated by next-generation sequencing, including: exploratory data analysis, principal components analysis, clustering, differential expression, and gene set analysis.

Session 1 – Introduction

Monday - 09:30 to 17:30

Lecture 1: Data distributions

  • random variables
  • distributions
  • population and samples

Hands-On 1: Introduction to R

Lecture 2: Creating high-quality graphics in R

  • Visualizing data in 1D, 2D & more than two dimensions
  • Heatmaps
  • Data transformations

Hands-On 2: Graphics with base R and ggplot2

Session 2 – Hypothesis testing

Tuesday - 09:30 to 17:30

Lecture 1: Hypothesis testing theory

  • type I and II error and power
  • multiple hypothesis testing: false discovery rate, familywise error rate
  • exploratory data analysis (EDA)

Hands-On 1: Standard tests & EDA

Lecture 2: Hypothesis testing in practice

  • hypothesis tests for categorical variables (chi-square, Fisher's exact)
  • Monte Carlo simulation
  • Permutation tests

Hands-On 2: Permutation tests

Session 3 - Bioconductor

Wednesday – Classes from 09:30 to 17:30

Lecture 1: Introduction to Bioconductor

  • Incorporating Bioconductor in your data analysis
  • ExpressionSet / SummarizedExperiment
  • Annotation resources

Hands-On 1: Leveraging Bioconductor annotation resources

Lecture 2: Genomic intervals

  • Introduction to genomic region algebra
  • Basic operations: construction, intra- and inter-region operations
  • Finding overlaps

Hands-On 2: Solving common bioinformatic challenges with GenomicRanges

Session 4 - Next-generation sequencing data

Thursday - 09:30 to 17:30

Lecture 1: High-throughput count data

  • Characteristics of count data
  • Exploring count data
  • Modeling count data

Hands-On 1: Analyzing next-generation sequencing data

Lecture 2: Clustering and Principal Components Analysis

  • Measures of similarity
  • Hierarchical clustering
  • Dimension reduction
  • Principal components analysis (PCA)

Hands-On 2: Clustering & PCA

Session 5 - Differential expression and gene set analysis

Friday - 09:30 to 17:30

Lecture 1 - Differential expression analysis

  • Normalization
  • Experimental designs
  • Generalized linear models

Lab 1: Performing differential expression analysis with DESeq2

Lecture 2 - Gene set analysis

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

Lab 2: Performing gene set enrichment analysis with the EnrichmentBrowser

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