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Bioconductor short course

 

A short course on
Computational and Statistical Aspects of Microarray Analysis

Kitasato University
July 14-25, 2003

Lecturer:
Robert Gentleman


Schedule of Topics

Monday, July 14 Tuesday, July 15 Wednesday, July 16 Thursday, July 17 Friday, July 18
Lecture Introduction to Genome Biology R/S Programming Techniques DNA Microarray Data, Oligonucleotide Arrays Introduction to Bioconductor Experimental Design and Experimental Design Paper
Labs Lecture Con't Lab1, Lab2a Lab2b Lab3b Estrogen
Monday, July 21 Tuesday, July 22 Wednesday, July 23 Thursday, July 24 Friday, July 25
Lecture Holiday, No Lecture Microarray Experiments and Distances and Expression Measures Some Things Every Biologist Should Know About Machine Learning Classification in Microarray Experiments Clustering in Microarray Experiments
Labs Holiday Lab4 Lab5 Lab6, Lab7 Lab8

Lab materials
 

Lab1  Bioconductor Basics
Lab2a Bioinformatics (anotation package)
Lab2b An Introduction to Some Graphics in Bioconductor
Lab3a  Introduction to Bioconductor's marray Packages
Lab3b  Introduction to the affy package
Lab4  Differential Gene Expression
Lab5  Cluster Analysis Using R and Bioconductor
Lab6  Classification Using R and Bioconductor
Lab7  Analyzing Microarray Data: From Images to List of Candidate Genes
Lab8  Application of Machine Learning to Microarray Data, SVM and friends

Milan course package (Linux/Unix) (Windows).

You can step through the labs interactively using the vExplorer function and GUI from the tkWidgets package. At the R prompt type
             library(tkWidgets)
             library(Milan)
              vExplorer()
and select the package Milan and the vignette for a given lab, e.g., lab1.Rnw.

Installing software

The packages used for this course require the use of R version 1.7.1 or newer.

A download script has been provided. To use, type 'source("/workshops/Milan/getMilan.R")' in your R session and then 'getMilan()'. There are a few arguments to getMilan(), tho most users will not find them necessary:

  • getAllDeps: If this is TRUE, will automatically attempt to download any package dependencies, otherwise the user will be prompted. Default is TRUE.
  • destdir: This defines where to install the packages. Defaults to .libPaths()[1]
  • force: If TRUE, will install a package even if not all of its dependencies were available. Default is TRUE.
  • versForce: If TRUE, will install a binary package even if it was built on a different version of R. Default is TRUE.
  • method: The download method to use, same as in download.packages(). Default is 'auto'.

Alternatively, you can download the individual packages here:

Documentation

Main R website: R manuals, R News.
Main Bioconductor website: Bioconductor short course, vignettes, talks, publications.
 
 
 

News
2008-05-01

BioC 2.2, consisting of 260 packages and designed to work with R 2.7.0, was released today.

2008-03-04

BioConductor release scheduled for 30 April 2008.