Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis
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- 作者：Arran K Turnbull (1)
Robert R Kitchen (1) (2)
Alexey A Larionov (1)
Lorna Renshaw (1)
J Michael Dixon (2)
Andrew H Sims (1) - 刊名：BMC Medical Genomics
- 出版年：2012
- 出版时间：December 2012
- 年：2012
- 卷：5
- 期：1
- 全文大小：1437KB
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53. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/5/35/prepub - 作者单位：Arran K Turnbull (1)
Robert R Kitchen (1) (2)
Alexey A Larionov (1)
Lorna Renshaw (1)
J Michael Dixon (2)
Andrew H Sims (1)
1. Breakthrough Research Unit, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XR, UK
2. Department of Molecular Biophysics & Biochemistry and Department of Psychiatry, Yale University School of Medicine, 266 Whitney Ave, New Haven, CT, 06511, USA - ISSN：1755-8794
文摘
Background 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. Results 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. Conclusion 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.