A short course on
Computational and Statistical Aspects of Microarray Analysis

University of Milan
May 26-30, 2003

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

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