STAT 992: STATISTICAL METHODS FOR ANALYSIS OF MICROARRAY DATA

Instructor:

Christina Kendziorski
6729 Medical Sciences Center
1300 University Avenue
Email: kendzior@biostat.wisc.edu
Phone: 262-3146

Lecture:

Tuesdays and Thursdays 2:30 - 3:45 p.m.; 1207 CSSC, 1210 West Dayton St.

Text:

The analysis of gene expression data: methods and software
by G Parmigiani, ES Garrett, R Irizarry and SL Zeger, Springer-Verlag, 2003.
A final version of this book is expected to be ready in March, 2003.
The pre-printed version will be made available for this course.
In addition to this text, a course packet of references will be available.

Course Overview:

This course will provide an introduction to statistical methods and associated freeware tools developed to address questions in gene expression array studies. The course will begin with an overview of image analysis including issues related to intensity estimation and background correction. Experimental design will then be discussed. Oftentimes in microarray experiments, due to high costs, there are few replicates for any one given experiment. Methods to maximize the amount of information obtained in a set of comparison experiments with few replicates will be reviewed along with other considerations in experimental design such as normalization, labelling, pooling, and sample size estimation. We will then focus on exploratory tools such as hierarchical clustering methods and principal components analysis. Finally, we will consider a number of methods to estimate differential expression and identify significant differential expression across multiple conditions. The intended audience consists of graduate students, post-doctoral students, and researchers in statistics or molecular genetics with an interest in statistical methods used in expression array studies. Although there are no formal prerequisites, it is recommended that students at least be familiar with topics covered in an introductory statistics course (e.g. STAT 310-311, STAT 541, STAT 571-572).

Instructor:	Christina Kendziorski 6729 Medical Sciences Center 1300 University Avenue Email: kendzior@biostat.wisc.edu Phone: 262-3146
Lecture:	Tuesdays and Thursdays 2:30 - 3:45 p.m.; 1207 CSSC, 1210 West Dayton St.
Text:	The analysis of gene expression data: methods and software by G Parmigiani, ES Garrett, R Irizarry and SL Zeger, Springer-Verlag, 2003. A final version of this book is expected to be ready in March, 2003. The pre-printed version will be made available for this course. In addition to this text, a course packet of references will be available.
Course Overview:
This course will provide an introduction to statistical methods and associated freeware tools developed to address questions in gene expression array studies. The course will begin with an overview of image analysis including issues related to intensity estimation and background correction. Experimental design will then be discussed. Oftentimes in microarray experiments, due to high costs, there are few replicates for any one given experiment. Methods to maximize the amount of information obtained in a set of comparison experiments with few replicates will be reviewed along with other considerations in experimental design such as normalization, labelling, pooling, and sample size estimation. We will then focus on exploratory tools such as hierarchical clustering methods and principal components analysis. Finally, we will consider a number of methods to estimate differential expression and identify significant differential expression across multiple conditions. The intended audience consists of graduate students, post-doctoral students, and researchers in statistics or molecular genetics with an interest in statistical methods used in expression array studies. Although there are no formal prerequisites, it is recommended that students at least be familiar with topics covered in an introductory statistics course (e.g. STAT 310-311, STAT 541, STAT 571-572).