Introductory R for Biologists Training Course

I. Introduction and preliminaries

1. Overview

• Making R more friendly, R and available GUIs
• Rstudio
• Related software and documentation
• R and statistics
• Using R interactively
• An introductory session
• Getting help with functions and features
• R commands, case sensitivity, etc.
• Recall and correction of previous commands
• Executing commands from or diverting output to a file
• Data permanency and removing objects
• Good programming practice:  Self-contained scripts, good    readability e.g. structured scripts, documentation, markdown
• installing packages; CRAN and Bioconductor

2. Reading data

• Txt files  (read.delim)
• CSV files

3. Simple manipulations; numbers and vectors  + arrays

• Vectors and assignment
• Vector arithmetic
• Generating regular sequences
• Logical vectors
• Missing values
• Character vectors
• Index vectors; selecting and modifying subsets of a data set
  • Arrays
• Array indexing. Subsections of an array
• Index matrices
• The array() function + simple operations on arrays e.g. multiplication, transposition  
• Other types of objects

4. Lists and data frames

• Lists
• Constructing and modifying lists
  • Concatenating lists
• Data frames
  • Making data frames
  • Working with data frames
  • Attaching arbitrary lists
  • Managing the search path

5. Data manipulation

• Selecting, subsetting observations and variables         
• Filtering, grouping
• Recoding, transformations
• Aggregation, combining data sets
• Forming partitioned matrices, cbind() and rbind()
• The concatenation function, (), with arrays
• Character manipulation, stringr package
• short intro into grep and regexpr

6. More on Reading data                                            

• XLS, XLSX files
• readr  and readxl packages
• SPSS, SAS, Stata,… and other formats data
• Exporting data to txt, csv and other formats

6. Grouping, loops and conditional execution

• Grouped expressions
• Control statements
• Conditional execution: if statements
• Repetitive execution: for loops, repeat and while
• intro into apply, lapply, sapply, tapply

7. Functions

• Creating functions
• Optional arguments and default values
• Variable number of arguments
• Scope and its consequences

8. Simple graphics in R

• Creating a Graph
• Density Plots
• Dot Plots
• Bar Plots
• Line Charts
• Pie Charts
• Boxplots
• Scatter Plots
• Combining Plots

II. Statistical analysis in R 

1.    Probability distributions

• R as a set of statistical tables
• Examining the distribution of a set of data

2.   Testing of Hypotheses

• Tests about a Population Mean
• Likelihood Ratio Test
• One- and two-sample tests
• Chi-Square Goodness-of-Fit Test
• Kolmogorov-Smirnov One-Sample Statistic 
• Wilcoxon Signed-Rank Test
• Two-Sample Test
• Wilcoxon Rank Sum Test
• Mann-Whitney Test
• Kolmogorov-Smirnov Test

3. Multiple Testing of Hypotheses

• Type I Error and FDR
• ROC curves and AUC
• Multiple Testing Procedures (BH, Bonferroni etc.)

4. Linear regression models

• Generic functions for extracting model information
• Updating fitted models
• Generalized linear models
  • Families
  • The glm() function
• Classification
  • Logistic Regression
  • Linear Discriminant Analysis
• Unsupervised learning
  • Principal Components Analysis
  • Clustering Methods(k-means, hierarchical clustering, k-medoids)

5.  Survival analysis (survival package)

• Survival objects in r
• Kaplan-Meier estimate, log-rank test, parametric regression
• Confidence bands
• Censored (interval censored) data analysis
• Cox PH models, constant covariates
• Cox PH models, time-dependent covariates
• Simulation: Model comparison (Comparing regression models)

 6.   Analysis of Variance

• One-Way ANOVA
• Two-Way Classification of ANOVA
• MANOVA

III. Worked problems in bioinformatics           

• Short introduction to limma package
• Microarray data analysis workflow
• Data download from GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397
• Data processing (QC, normalisation, differential expression)
• Volcano plot             
• Custering examples + heatmaps