Introduction to R with applications to bioinformatics (2KV)
| Course no.: | 365.054 |
| Lecturer: | Sepp Hochreiter |
| Times/locations: | Mon 13:45-15:15, room HS 14 Start: Mon 1.3.2010 |
| Mode: | KV, 2h, weekly |
| Registration: | KUSSS |
Lecture notes (Version Apr 30, 2010):
- PDF Part I (16.5 MB)
R chunks from the script for copy and paste into the R command window -
PDF Part II (5 MB)
R chunks from the script for copy and paste into the R command window
Test1 Data for the case study of a microarray experiment -
vorl_RI.Rnw
vorl_RII.Rnw
Files for final project:
Motivation:
This course should show how to approach and solve problems in bioinformatics and computational biology with tools supplied by R. The focus is on data analysis with machine learning methods and visualizing the results of this analysis. R is free and an implementation of the S language which has been used by statisticians and data analysts since two decades. R is probably the most widely used software tool for bioinformatics and became popular due to its data handling (e.g. importing microarray data), statistical algorithms, machine learning / data modelling implementations and integrated data visualization. One of the largest sources of R tools for bioinformatics is the Bioconductor Project (www. bioconductor.org) which will be utilized in this course. These days R is increasing popular in machine learning even outside bioinformatics e.g. for modelling the financial market or for forecasting.R has the advantages:
- it is free and open source with a large community
- it is highly extensible and flexible
- it has implementations of machine learning and statistical methods
- it has graphics for data visualization
- it is effective data handling tools
- it has matrix and vector calculation tools
Topics of the course:
- Basics of the R language
- Data Import and Export
- Object-oriented programming in R
- Case study: gene expression profiling for analyzing genes relevant in leukemia:
classification and feature selection - Case study: biclustering of gene expression profiles
- Case study: analysis of genetic variations (copy number variations) in the human population
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"An Introduction to R"
"The R Language Definitions"
"R Installation and Administration"
"Writing R Extensions"
"R Data Import and Export"