R2 Tutorials: 2024-04-08¶
Dear reader. Welcome to the tutorials for ‘R2: Genomics Analysis and Visualization Platform’. The R2 platform is a (molecular) biologist friendly, web based genomics analysis and visualization application developed by Jan Koster and his team at the department of CEMM in the Amsterdam University Medical Centers (AUMC), location Academic Medical Center (AMC) in Amsterdam, the Netherlands. The idea behind the application is to enable researchers that are not experts in bioinformatics to mine and explore omics data sources, and thereby gain insights for their research. This can be done on publicly avaialble data sets, but also on your own data in a restricted environment. The tutorials have been assembled as guided short stories, that will instruct you how to get things done in the platform by an example. We hope that, by following our examples, you will get familiar with the concepts of R2, and thereby find your way in the platform. Even though many of our examples are illustrated by a neuroblastoma pediatric cancer dataset, R2 has many more (2100+) public datasets to work with, covering nearly any cancer but also other diseases as well as normal reference series (See also Selecting datasets).
If you make use of our platform in manuscripts, then please add a citation that includes the following webcite: ‘R2: Genomics Analysis and Visualization Platform (https://r2.amc.nl http://r2platform.com)’. This will help us get the necessary funds to keep on going and in addition allows us to ‘scan’ the literature to keep track of manuscripts that cite our resource.
Copyright (c) 2006-2023 R2 Support Team
- 1. Preface
- 2. Using Datasets
- 3. One Gene View
- 3.1. Scope
- 3.2. Step 1: Select the View a Gene module
- 3.3. Step 2: Select the gene or reporter
- 3.4. Step 3: Plotting Gene expression
- 3.5. Step 4: Selecting analysis types: View a gene in groups
- 3.6. Step 5: Marking / highlighting samples within a plot
- 3.7. Step 6: Sources for additional information on the selected gene
- 3.8. Step 7: Adapting a plot
- 3.9. Step 8: Selecting subsets
- 3.10. Step 9: Find best track separation with CliniSnitch
- 3.11. Step 10: Finding sample extremes.
- 3.12. Step 11: Probeset verification
- 3.13. Final remarks / future directions
- 4. Multiple Genes View
- 5. Annotation analyses
- 6. Differential expression of genes in your dataset
- 6.1. Scope
- 6.2. Step 1: Selecting data and the type of analysis
- 6.3. Step 2: Choose the gene and the annotation track as grouping variable
- 6.4. Step 3: Anova results / adapting plots
- 6.5. Step 4: Finding differentially expressed genes in two groups
- 6.6. Step 5 Setting parameters
- 6.7. Step 5: Correct for paired analysis
- 6.8. Step 6: Find differential expression in multiple groups
- 6.9. Step 7: Inspecting single genes
- 6.10. Step 8: Plot all genes and adapt visualization: Volcano plot etc
- 6.11. Step 9: Using the Enrichr
- 6.12. Final remarks / future directions
- 7. Find genes correlating with your gene of interest
- 7.1. Scope
- 7.2. Step 1: Selecting data
- 7.3. Step 2: Inspecting correlating genes
- 7.4. Step 3: Inspecting correlation between specific genes
- 7.5. Step 4: Relation with Chromosome position
- 7.6. Step 5: Establishing overrepresentation in other domains
- 7.7. Step 7: Gene list in pathway context
- 7.8. Step 8: Further pathways analysis
- 7.9. Step 9: Gene set analysis
- 7.10. Final remarks / future directions
- 8. Working with Kaplan Meier
- 8.1. Scope
- 8.2. Step 1: Selecting the Kaplan Meier module
- 8.3. Step 2: Kaplan Meier by gene expression; the Kaplan Scan
- 8.4. Step 3: Kaplan scan for a group of genes
- 8.5. Step 4: Kaplan scan on your own cohort
- 8.6. Step 4: Cox Regression analysis and hazard ratio
- 8.7. Final remarks / future directions
- 9. Pathway Finder
- 9.1. Scope
- 9.2. Step 1: Selecting data
- 9.3. Step 2: Correlating pathways with a gene
- 9.4. Step 3: Finding pathways relevant to subgroups
- 9.5. Step 4: Determining differentially expressed pathways
- 9.6. Step 5: Verifying a pathway
- 9.7. Step 6: Correlating with the expression of a gene
- 9.8. Final remarks / future directions
- 10. Multiple datasets overview with Megasampler
- 11. K-means clustering in R2
- 12. Using signatures
- 12.1. Scope
- 12.2. Step 1: Creating a geneset signature, a Track within R2
- 12.3. Step 2: Determine the activity of a signature
- 12.4. Step 3: Using signature scores
- 12.5. Step 4: Plot signature scores using the relate 2-tracks module.
- 12.6. Step 5: Drawing lines between samples in a XY plot
- 12.7. Step 6: Signature Gene correlations
- 12.8. Final remarks / future directions
- 13. Analysing Time Series
- 13.1. Scope
- 13.2. Step 1: Choosing the time series module and data
- 13.3. Step 2: Finding regulated genes in a time series experiment
- 13.4. Step 3: Using the regulated genes in further analyses
- 13.5. Step 4: Correlate with other datasets
- 13.6. Step 5: In a K-means analysis
- 13.7. Final remarks / future directions
- 14. Using genesets and creating heatmaps in R2
- 15. Principle Components Analysis in R2
- 16. Sample maps: t-SNE / UMAP, high dimensionality reduction in R2
- 17. Using the R2-Genome browser
- 18. DataScopes
- 19. Integrative analysis: ChIP-seq data
- 20. Integrative Analysis : Across Platforms
- 21. Integrative Analysis : WGS/NGS data
- 22. Target Actionability Literature Reviews : TAR
- 23. Adapting R2 to your needs
- 23.1. Scope
- 23.2. Step 1: Adapt your settings
- 23.3. Step 2: How to add data to R2.
- 23.4. Step 3: Create your custom genesets
- 23.5. Step 4: Tracks in R2: create your own data annotation
- 23.6. Step 5: Upload your own tracks
- 23.7. Step 6: Cooperate through R2: sharing tracks, creating communities
- 23.8. Final remarks / future directions
- 24. Exporting data
- 25. R2 Dataset Addition
- 25.1. Scope
- 25.2. What to prepare when you would like to have a dataset added
- 25.3. Who can add datasets to R2
- 25.4. Addition of a public dataset from the GEO database
- 25.5. Addition of personal datasets
- 25.6. Access levels
- 25.7. Preparing the expression data
- 25.8. Preparing the gene annotation
- 25.9. Preparing the sample annotation
- 25.10. Describing your dataset
- 26. Graphs: Adjustable Settings menu versus Repsonsive Settings
- 27. Concepts of R2: did you know..?