Empirical array quality weights in the analysis of microarray data

Background: Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results. Results: In this article, a graduated approach to array quality is considered based on empirical reproducibility of the gene expression measures from replicate arrays. Weights are assigned to each microarray by fitting a heteroscedastic linear model with shared array variance terms. A novel gene-by-gene update algorithm is used to efficiently estimate the array variances. The inverse variances are used as weights in the linear model analysis to identify differentially expressed genes. The method successfully assigns lower weights to less reproducible arrays from different experiments. Down-weighting the observations from suspect arrays increases the power to detect differential expression. In smaller experiments, this approach outperforms th...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Matthew E. Ritchie, Dileepa S. Diyagama, Jody Neilson, Ryan van Laar, Alexander Dobrovic, Andrew J. Holloway, Gordon K. Smyth
• Keywords: Analysis, BMCBI 2006, Data Quality, Linear Model