Milan 2003

A short course on
Computational and Statistical Aspects of Microarray Analysis

University of Milan
May 26-30, 2003

Lecturers:
Anestis Antoniadis
Robert Gentleman

Lecture notes

Monday	Tuesday	Wednesday	Thursday	Friday
Introduction to Genome Biology	DNA Microarray Data, Oligonucleotide Arrays	Microarray Experiments	Some Things Every Biologist Should Know About Machine Learning	Classification in Microarray Experiments
R/S Programming Techniques	Introduction to Bioconductor	Distances and Expression Measures	Penalized logistic regression and classification of microarray data	Dimension Reduction Techniques For Classification
		Wavelets and Gene Selection by Multiple Testing	Penalized Logistic Regression and Classification of Microarray Data	Dimension Reduction Techniques For Classification
				Some Statistical Methods for the Identification of Differentially Expressed Genes

Lab materials

Lab1	Bioconductor Basics	.pdf	.Rnw	.R
Lab2a	Bioinformatics (anotation package)	.pdf	.Rnw	.R
Lab2b	An Introduction to Some Graphics in Bioconductor	.pdf	.Rnw	.R
Lab3a	Introduction to Bioconductor's marray Packages	.pdf	.Rnw	.R
Lab3b	Introduction to the affy package	.pdf	.Rnw	.R
Lab4	Differential Gene Expression	.pdf	.Rnw	.R
Lab5	Cluster Analysis Using R and Bioconductor	.pdf	.Rnw	.R
Lab6	Classification Using R and Bioconductor	.pdf	.Rnw	.R
Lab7	Analyzing Microarray Data: From Images to List of Candidate Genes	.pdf	.Rnw	.R
Lab8	Application of Machine Learning to Microarray Data, SVM and friends	.pdf	.Rnw	.R
Lab9	Lab 9: An Introduction to Wavelets	.pdf	.Rnw	.R
Lab10	EFDR: Some Statistical Methods for the Identification of Differentially Expressed Genes	.pdf	.Rnw	.R
Lab11	Lab 11: Penalized Logistic Regression	.pdf	.Rnw	.R
Lab12	Lab 12: Dimension reduction in R	.pdf	.Rnw	.R

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 1.7.0-patched or newer.

A download script has been provided. To use, type 'source("http://www.bioconductor.org/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'.

Documentation

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

bioconductor.org

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