Quality Assessment for Short Oligonucleotide Microarray Data

• Activity Number: 290
• Type: Invited
• Date/Time: Tuesday, August 5, 2008 : 2:00 PM to 3:50 PM
• Sponsor: Technometrics
• Author(s): Julia Brettschneider and Terence P. Speed*+ and Francois Collin and Benjamin Bolstad
• Companies: University of California, Berkeley and University of Warwick and University of California, Berkeley and University of California, Berkeley
• Keywords: quality control ; relative log expression ; microarrays ; normalized unscaled standard errors ; Affymetrix chips ; residual scale factors

Abstract

Quality of microarray gene expression data has emerged as a new research topic. Microarray quality is assessed by comparing suitable numerical summaries across microarrays, so that outliers and trends can be visualized, and poor quality arrays or variable quality sets of arrays can be identified. Since each single array comprises hundreds of thousands of measurements, the challenge is to find numerical summaries which can be used to make accurate quality calls. Several new quality measures are introduced based on probe level and probeset level information, all obtained as a byproduct of RMA/fitPLM. Quality landscapes spatially localize quality problems. Numerical chip quality measures are derived from the distributions of normalized unscaled standard errors and of relative log expressions. They are demonstrated on a variety of data sets and compared with Affymetrix' quality report.