R2 Training Courses: 2024-04-11

This page contains a collection of training courses for the R2 platform: a biologist friendly, web based genomics analysis and visualization application developed by Jan Koster at the department of Oncogenomics in the Academic Medical Center (AMC) Amsterdam, the Netherlands. For citations, please include the following website: ‘R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl http://r2platform.com)’

Copyright (c) 2006-2024 Jan Koster

Table of Contents

Butterfly

• 1. Finding causes in Neuroblastoma genomics data
  • 1.1. Introduction
  • 1.2. Finding prognostic factors in your data
  • 1.3. Different expression patterns between subgroups and the underlying biology
• 2. Investigating structural variants
  • 2.1. Exploring the dataset
  • 2.2. Pie Charts
  • 2.3. Somatic mutations in neuroblastoma
  • 2.4. Further use of WGS data; structural variants
  • 2.5. Chromothripsis
  • 2.6. Locations of structural variants, hotspots?
  • 2.7. Remarks

Amsterdam Movement Sciences

• 1. Gene expression in Rheumatoid Arthritis
  • 1.1. Immune response in blood and synovial fluid
  • 1.2. Effects of treatment
  • 1.3. Final remarks / future directions

Students Course

• 1. Investigating Intra-tumor Heterogeneity in Neuroblastoma
  • 1.1. Introduction
  • 1.2. Tumors and origins: a first impression of your data
  • 1.3. Urgency of research: patient material
  • 1.4. Which genes make a difference? Creating signatures
  • 1.5. Identifying groups: using signatures to classify other datasets
  • 1.6. Using scores for further characterization
  • 1.7. Finding causes: homing in on transcription factors
  • 1.8. Proving causes: manipulating cell lines
  • 1.9. Creating hypotheses: relating to chromatin modification data
  • 1.10. Suggesting therapy
  • 1.11. Final remarks / future directions
• 2. Molecular Oncology Course - Colorectal Cancer
  • 2.1. Introduction
  • 2.2. Normal colonic epithelium vs adenomatous tissue: a first impression of genomic data
  • 2.3. Identifying groups and their characteristics: CMS
  • 2.4. Effects of imatinib: shifts of signature profiles and molecular subtypes
  • 2.5. Identifying key drivers of CRC: superenhancers controlling gene expression
  • 2.6. Optional exercise: Experiments TP53 - Molecule of the year 1994
  • 2.7. Evaluation
  • 2.8. Final remarks / future directions

Graduate Course

• 1. Investigating structural variants
  • 1.1. Introduction
  • 1.2. Exploring the dataset
  • 1.3. Pie Charts
  • 1.4. Somatic mutations in neuroblastoma
  • 1.5. Further use of WGS data; structural variants
  • 1.6. Chromothripsis
  • 1.7. Locations of structural variants, hotspots?
  • 1.8. Remarks
• 2. Investigating Intra-tumor Heterogeneity
  • 2.1. Introduction
  • 2.2. Tumors and origins: a first impression of your data
  • 2.3. Urgency of research: patient material
  • 2.4. Which genes make a difference? Creating signatures
  • 2.5. Identifying groups: using signatures to classify other datasets
  • 2.6. Using scores for further characterization
  • 2.7. Finding causes: homing in on transcription factors
  • 2.8. Proving causes: manipulating cell lines
  • 2.9. Creating hypotheses: relating to chromatin modification data
  • 2.10. Suggesting therapy
  • 2.11. Final remarks / future directions

Additional Course Materials

• 1. Differential gene expression in micro-array colon cancer data
  • 1.1. Introduction
  • 1.2. First Part: Colon versus Normal tissue
  • 1.3. Second Part: MSI in Colon tumors.
• 2. BMS38: Computer Practicals
  • 2.1. Introduction
  • 2.2. Assignment 1 (no answers necessary):
  • 2.3. Assignment 2: K-means clustering:
  • 2.4. Assignment 3: Principal component analysis.
  • 2.5. Assignment 4: Identifying a biomarker signature in gene expression data
  • 2.6. Assignment 5: pathway analysis
  • 2.7. Assignment 6: Integrative -omics analysis (methylation and expression)