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