Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis
1 Breakthrough Research Unit, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XR, UK
2 Current address: Yale University School of Medicine, Department of Molecular Biophysics & Biochemistry and Department of Psychiatry, 266 Whitney Ave, New Haven, CT, 06511, USA
BMC Medical Genomics 2012, 5:35 doi:10.1186/1755-8794-5-35Published: 21 August 2012
Affymetrix GeneChips and Illumina BeadArrays are the most widely used commercial single channel gene expression microarrays. Public data repositories are an extremely valuable resource, providing array-derived gene expression measurements from many thousands of experiments. Unfortunately many of these studies are underpowered and it is desirable to improve power by combining data from more than one study; we sought to determine whether platform-specific bias precludes direct integration of probe intensity signals for combined reanalysis.
Using Affymetrix and Illumina data from the microarray quality control project, from our own clinical samples, and from additional publicly available datasets we evaluated several approaches to directly integrate intensity level expression data from the two platforms. After mapping probe sequences to Ensembl genes we demonstrate that, ComBat and cross platform normalisation (XPN), significantly outperform mean-centering and distance-weighted discrimination (DWD) in terms of minimising inter-platform variance. In particular we observed that DWD, a popular method used in a number of previous studies, removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets.
Normalised and batch-corrected intensity-level data from Affymetrix and Illumina microarrays can be directly combined to generate biologically meaningful results with improved statistical power for robust, integrated reanalysis.