- Syllabus
- 1 Introduction
- 2 Data in Biology
- 3 Preliminaries
- 4 R Programming
- 4.1 Before you begin
- 4.2 Introduction
- 4.3 R Syntax Basics
- 4.4 Basic Types of Values
- 4.5 Data Structures
- 4.6 Logical Tests and Comparators
- 4.7 Functions
- 4.8 Iteration
- 4.9 Installing Packages
- 4.10 Saving and Loading R Data
- 4.11 Troubleshooting and Debugging
- 4.12 Coding Style and Conventions
- 4.12.1 Is my code correct?
- 4.12.2 Does my code follow the DRY principle?
- 4.12.3 Did I choose concise but descriptive variable and function names?
- 4.12.4 Did I use indentation and naming conventions consistently throughout my code?
- 4.12.5 Did I write comments, especially when what the code does is not obvious?
- 4.12.6 How easy would it be for someone else to understand my code?
- 4.12.7 Is my code easy to maintain/change?
- 4.12.8 The
stylerpackage
- 5 Data Wrangling
- 6 Data Science
- 7 Data Visualization
- 8 Biology & Bioinformatics
- 8.1 R in Biology
- 8.2 Biological Data Overview
- 8.3 Bioconductor
- 8.4 Microarrays
- 8.5 High Throughput Sequencing
- 8.6 Gene Identifiers
- 8.7 Gene Expression
- 8.7.1 Gene Expression Data in Bioconductor
- 8.7.2 Differential Expression Analysis
- 8.7.3 Microarray Gene Expression Data
- 8.7.4 Differential Expression: Microarrays (limma)
- 8.7.5 RNASeq
- 8.7.6 RNASeq Gene Expression Data
- 8.7.7 Filtering Counts
- 8.7.8 Count Distributions
- 8.7.9 Differential Expression: RNASeq
- 8.8 Gene Set Enrichment Analysis
- 8.9 Biological Networks .
- 9 EngineeRing
- 10 RShiny
- 11 Communicating with R
- 12 Contribution Guide
- Assignments
- Assignment Format
- Starting an Assignment
- Assignment 1
- Assignment 2
- Assignment 3
- Problem Statement
- Learning Objectives
- Skill List
- Background on Microarrays
- Background on Principal Component Analysis
- Marisa et al. Gene Expression Classification of Colon Cancer into Molecular Subtypes: Characterization, Validation, and Prognostic Value. PLoS Medicine, May 2013. PMID: 23700391
- Scaling data using R
scale() - Proportion of variance explained
- Plotting and visualization of PCA
- Hierarchical Clustering and Heatmaps
- References
- Assignment 4
- Assignment 5
- Problem Statement
- Learning Objectives
- Skill List
- DESeq2 Background
- Generating a counts matrix
- Prefiltering Counts matrix
- Median-of-ratios normalization
- DESeq2 preparation
- O’Meara et al. Transcriptional Reversion of Cardiac Myocyte Fate During Mammalian Cardiac Regeneration. Circ Res. Feb 2015. PMID: 25477501l
- 1. Reading and subsetting the data from verse_counts.tsv and sample_metadata.csv
- 2. Running DESeq2
- 3. Annotating results to construct a labeled volcano plot
- 4. Diagnostic plot of the raw p-values for all genes
- 5. Plotting the LogFoldChanges for differentially expressed genes
- The choice of FDR cutoff depends on cost
- 6. Plotting the normalized counts of differentially expressed genes
- 7. Volcano Plot to visualize differential expression results
- 8. Running fgsea vignette
- 9. Plotting the top ten positive NES and top ten negative NES pathways
- References
- Assignment 6
- Assignment 7
- Appendix
- A Class Outlines
3.2 RStudio
This course assumes the learner is using the RStudio software platform for all analyses, unless otherwise noted. RStudio is a freely available and fully featured integrated development environment (IDE) for R, and has many convenient capabilities when learning R. RStudio may be downloaded and installed for free from the site above.
All the examples and instructions in this book assume you have installed R are using RStudio. Be sure to turn off automatic environment saving in RStudio! Because this is so important, here it is again:
By default, RStudio preserves your R environment when you shut it down and restores it when you start it again. This is very bad practice!
The state of your R environment, which includes the values stored in variables, the R packages loaded, etc. from previously executed code is transient and may not reflect the results your code produces when run alone.
Open the Tools > Global Options… menu and:
- Uncheck “Restore .RData into workspace at startup”
- Set “Save workspace to .RData on exit:” to “Never”
Never save workspace
The book R for Data Science has an excellent chapter on why this is a problem and how to change the RStudio setting to avoid it.
- Hands-On Programming with R - Installing R and RStudio
- RStudio Education - Beginners Guide
- ModernDrive R Series - Getting Started
- R for Data Science - What is real, and where does your analysis “live?”
- What They Forgot To Teach You About R - Always start R with a blank slate
- R for Data Science - RStudio