%0 Journal Article
%T Correction of Spatial Bias in Oligonucleotide Array Data
%A Philippe Serhal
%A S¨¦bastien Lemieux
%J Advances in Bioinformatics
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/167915
%X Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target¡¯s true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users¡¯ current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias. 1. Background Microarray technology, a fairly recent yet already well-established and extensively dissected method, allows for the simultaneous quantification of expression levels of entire genomes or subsets thereof [1]. In situ oligonucleotide arrays are by far the most popular type, representing at the time of writing 70% of all arrays deposited in the Gene Expression Omnibus (GEO), a public microarray database, in the last year; of these, 58% are Affymetrix GeneChips [2]. These are designed such that each gene is targeted by multiple perfectly complementary oligonucleotide probes at various locations along its sequence (forming a probe set); copies of each of these probes are covalently linked to a solid surface at a predetermined location on a grid; a labelled RNA sample is allowed to hybridize to each of these probes; and finally a hybridization signal intensity (HSI) is obtained for each probe [3]. The technology relies on the assumption that, on average, HSIs observed in a given probe set correlate with the true concentration of the given mRNA
%U http://www.hindawi.com/journals/abi/2013/167915/