The lectures in this day-long short course on microarray
data analysis address various commonly encountered problems in gene expression
studies.
Scientists, researchers and data analysts who are planning
and/or currently performing or analyzing microarray experiments.
Familiarity with microarray technology and associated data
structures.
| 9:00 - 9:15 am |
Welcome and introduction
Mark Segal
|
| 9:15 - 10:30 am |
Lecture 1: Introduction to
microarray technology and preprocessing of microarray data.
(Jean)
Yee Hwa Yang |
| |
-
Brief introduction to microarray technology.
-
Discussion on sources of variability.
-
Preprocessing: image analysis and normalization.
-
Assessing microarray data quality.
|
| 10:30 - 11:00 am |
Coffee Break
|
| 11:00 - 12:15 pm |
Lecture 2: Clustering microarray data.
Katie Pollard
|
| |
- Motivation and illustration.
- Overview of various clustering methods.
|
| 12:15 - 1:30 pm |
Catered Lunch at Genentech Hall
|
| 1:30 - 2:45 pm |
Lecture 3: Data analysis for identifying differentially expressed genes.
Sandrine Dudoit
|
| |
- Experimental design for comparative microarray experiments.
- Issues relating to summary statistics and multiplicity.
- Case studies: 1) Two sample comparisons
2) Factorial experiment.
|
| 2:45 - 3:15 pm |
Coffee Break
|
| 3:15 - 4:30 pm |
Lecture 4: Classification in microarray experiments.
Jane Fridlyand
|
| |
- Motivation and illustration.
- Overview of various classifiers.
- Statistical issues pertinent to classifying microarray data.
- Assessing performance.
|
