A Case Study on Normalizing Batch Effects on Microarrays

• Activity Number: 412
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Korean International Statistical Society
• Author(s): Dongseok Choi*+ and William O. Cepurna and John C. Morrison and Elaine C. Johnson and Stephen R. Planck and James T. Rosenbaum
• Companies: Oregon Health & Science University and Oregon Health & Science University and Oregon Health & Science University and Oregon Health & Science University and Oregon Health & Science University and Oregon Health & Science University
• Keywords: microarrays ; normalization ; batch effects

Abstract

Normalization is considered a key step of data preprocessing before statistical analyses in microarray studies. Many methods have been developed over the years, for examples, global normalization, local regression based methods and quantile normalization. Normalization methods typically remove systemic biases across arrays and enable us to carry out further analyses. These methods have been quite effective in normalizing arrays processed simultaneously in a batch. However, it is reported that they sometimes fail to remove batch effects when microarrays are split into several batches. In this presentation, we will present a case study of normalizing batch effects by using traditional normalization methods and a method that is developed as a secondary normalization to adjust batch effects.