A short course on Computational and Statistical Aspects of Microarray Analysis

Bressanone, Italy
June 7th-11th, 2004

Robert Gentleman
Wolfgang Huber

Schedule of Topics

Monday, June 7 Tuesday, June 8 Wednesday, June 9 Thursday, June 10 Friday, June 11
Lecture 1 Programming in R, S Programming Techniques, Recent Developments in R and S Graphics Quality Control and Further Topics on Preprocessing and Solving the Riddle of Bright Mismatches Differential Expression, Univariable Screening by ROC Curve Analysis, Differential Gene Expression, Testing for Differential Expression and Differential Expression with the Bioconductor Project Unsupervised Learning Methods For Analysis of Microarray Data and Exploratory Data Analysis for Microarray Data Networks in Molecular Biology and Graph, RBGL and Rgraphviz
Lecture 2 Error Models and Normalization Annotation in Bioconductor and Using GO Combining Experiments Machine Learning and Classification in DNA Microarray Experiments Graphs, EDA, and Computational Biology and High Throughput Protein-Protein Interaction Data
Lab Using R and Bioconductor Preprocessing and Quality Control Annotation and meta-data Machine Learning Introductory Graph Lab
Packages Used arrayMagic, estrogen and lymphoma ALL, GOstats, graph, RBGL and Rgraphviz

Lab materials

Lab1 Using R and Bioconductor
Lab2 Preprocessing and Quality Control
Lab3 Annotation and meta-data
Lab4 Machine Learning
Lab5 Introductor Graph Lab
Fred Hutchinson Cancer Research Center