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Course Outline
I. Introduction and preliminaries
1. Overview
- Enhancing user-friendliness in R: R and available graphical user interfaces (GUIs)
- RStudio
- Related software and documentation
- R and statistics
- Interactive use of R
- Introductory session
- Obtaining help with functions and features
- R commands, case sensitivity, and related concepts
- Recalling and correcting previous commands
- Executing commands from or redirecting output to a file
- Data permanency and object removal
- Best programming practices: self-contained scripts, readability (e.g., structured scripts), documentation, and Markdown
- Installing packages: CRAN and Bioconductor
2. Reading data
- Text files (read.delim)
- CSV files
3. Simple manipulations: numbers, vectors, and arrays
- Vectors and assignment
- Vector arithmetic
- Generating regular sequences
- Logical vectors
- Missing values
- Character vectors
- Index vectors: selecting and modifying subsets of a dataset
- Arrays
- Array indexing: subsections of an array
- Index matrices
- The array() function and basic array operations (e.g., multiplication, transposition)
- Other object types
4. Lists and data frames
- Lists
- Constructing and modifying lists
- Concatenating lists
- Data frames
- Creating data frames
- Working with data frames
- Attaching arbitrary lists
- Managing the search path
5. Data manipulation
- Selecting and subsetting observations and variables
- Filtering and grouping
- Recoding and transformations
- Aggregation and combining datasets
- Forming partitioned matrices using cbind() and rbind()
- The concatenation function c() with arrays
- Character manipulation with the stringr package
- Brief introduction to grep and regexpr
6. Advanced data reading
- XLS and XLSX files
- readr and readxl packages
- SPSS, SAS, Stata, and other data formats
- Exporting data to TXT, CSV, and other formats
7. Grouping, loops, and conditional execution
- Grouped expressions
- Control statements
- Conditional execution: if statements
- Repetitive execution: for loops, repeat, and while
- Introduction to apply, lapply, sapply, and tapply
8. Functions
- Creating functions
- Optional arguments and default values
- Variable numbers of arguments
- Scope and its implications
9. 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 dataset
2. Hypothesis testing
- 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 hypothesis testing
- Type I error and false discovery rate (FDR)
- ROC curves and area under the curve (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 component analysis
- Clustering methods (k-means, hierarchical clustering, k-medoids)
5. Survival analysis (survival package)
- Survival objects in R
- Kaplan-Meier estimate, log-rank test, and parametric regression
- Confidence bands
- Censored (interval-censored) data analysis
- Cox proportional hazards (PH) models with constant covariates
- Cox PH models with 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 the limma package
- Microarray data analysis workflow
- Data download from GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1397
- Data processing (quality control, normalization, differential expression)
- Volcano plots
- Clustering examples and heatmaps
28 Hours
Testimonials (2)
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
The real life applications using Statcan and CER as examples.
