Analysis of Gene Expression Microarray Data

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School of Medicine

School of Medicine

Analysis of Gene Expression Microarray Data

The lectures in this day-long short course on microarray data analysis address various commonly encountered problems in gene expression studies.

Target Audience

Scientists, researchers and data analysts who are planning and/or currently performing or analyzing microarray experiments.

Prerequisite:

Familiarity with microarray technology and associated data structures.

Materials:

Presentations and summaries of each topic with detailed references will be available on this web site.

Demo or software related discussion will be included in each lecture.

Course Outline:

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.