A short course on Computational and Statistical Aspects of
Microarray Analysis
Bressanone, Italy
June 7th-11th, 2004
Lecturers:
Robert Gentleman
and
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 |
|
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