Methodology article

Empirical array quality weights in the analysis of microarray data

Matthew E Ritchie¹ , Dileepa Diyagama² , Jody Neilson³ , Ryan van Laar² , Alexander Dobrovic³ , Andrew Holloway² and Gordon K Smyth¹

¹Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia

²lan Potter Foundation Centre for Cancer Genomics and Predictive Medicine, The Peter MacCallum Cancer Centre, St Andrews Place, East Melbourne, Victoria 3002, Australia

³Molecular Pathology Research, Department of Pathology, The Peter MacCallum Cancer Centre, St Andrews Place, East Melbourne, Victoria 3002, Australia

author email corresponding author email

BMC Bioinformatics 2006, 7:261doi:10.1186/1471-2105-7-261

Published:	19 May 2006

Abstract

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 the usual method of filtering the data. The method is available in the limma software package which is implemented in the R software environment.

Conclusion

This method complements existing normalisation and spot quality procedures, and allows poorer quality arrays, which would otherwise be discarded, to be included in an analysis. It is applicable to microarray data from experiments with some level of replication.