Download Planning Microarray Experiments

Title: Planning Microarray Experiments
Abstract:
Microarray technology is a powerful tool that allows the study of tens of thousands of genes at
once. In the complex microarray experiments, many sources of potential slight disturbances are
possible. Statistical design of microarray studies aims at reducing effect of the unwanted
variations to increase the precision of the quantatities of interest. A carefully designed
experiment often suggests a suitable method of analysis and lends itself to simple and powerful
interpretation.
In this talk, I will first provide an overview of microarray studies and then focus on issues
involved in the design of microarray experiments such as sources of variation, replication,
randomization and pooling.
Topics:
1. Objectives and applications of microarray experiments
2. Spotted vs. Affymetrix Arrays
3. Steps of microarray study
4. Issues in the design of microarray study: sources of variation, replication, randomization,
pooling
5. Some pitfalls and recommendations
Goals:
1. Provide an overview of microarray experiments
2. Discuss issues involved in the design of microarray study.
3. Discuss recommendation and pitfalls for conducting microarray experiments.
Intended Audience:
Anyone who is interested in applying the microarray technology to their research.
Speaker Description:
Lily Wang, PhD, Assistant professor in Biostatistics, is a statistical advisor of the Quantitative
group. She has a doctorate degree in Biostatistics from the University of North Carolina at
Chapel Hill. Her major research interests are statistical methodologies related to bioinformatics
research including prediction methods using protein sequences and the analysis of gene
expression data. In addition, Wang maintains a broad interest in biostatistics and, in particular
the analysis of longitudinal and correlated data using Generalized Estimating Equations and
Mixed Models. She provides consultation on the use of publicly available genomic resources and
the design and analysis of microarray and other biological experimental data.