Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on\r\nthe fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate\r\nwith their target�s true concentration in the sample. However, systematic, nonbiological variation fromseveral sources undermines\r\nthis hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation\r\nof which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial\r\nautocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and\r\nundesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial\r\nautocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection\r\npower; and performs better than previously published methods. Conclusions.The proposed algorithm increases both precision and\r\naccuracy, while requiring virtually no changes to users� current analysis pipelines: the correction consists merely of a transformation\r\nof raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible\r\nwith standard Bioconductor tools.The approach may also be tailored to other platform types and other sources of bias.
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