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Bioinformatics Advance Access originally published online on October 23, 2006
Bioinformatics 2006 22(24):3054-3060; doi:10.1093/bioinformatics/btl542
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© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

High-resolution spatial normalization for microarrays containing embedded technical replicates

Daniel S. Yuan 1,2,3,* and Rafael A. Irizarry 3

1 Department of Molecular Biology and Genetics and Baltimore MD, 21205, USA
2 High-throughput Biology Center, School of Medicine Baltimore MD, 21205, USA
3 Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University Baltimore MD, 21205, USA

*To whom correspondence should be addressed.

Motivation: Microarray data are susceptible to a wide-range of artifacts, many of which occur on physical scales comparable to the spatial dimensions of the array. These artifacts introduce biases that are spatially correlated. The ability of current methodologies to detect and correct such biases is limited.

Results: We introduce a new approach for analyzing spatial artifacts, termed ‘conditional residual analysis for microarrays’ (CRAM). CRAM requires a microarray design that contains technical replicates of representative features and a limited number of negative controls, but is free of the assumptions that constrain existing analytical procedures. The key idea is to extract residuals from sets of matched replicates to generate residual images. The residual images reveal spatial artifacts with single-feature resolution. Surprisingly, spatial artifacts were found to coexist independently as additive and multiplicative errors. Efficient procedures for bias estimation were devised to correct the spatial artifacts on both intensity scales. In a survey of 484 published single-channel datasets, variance fell 4- to 12-fold in 5% of the datasets after bias correction. Thus, inclusion of technical replicates in a microarray design affords benefits far beyond what one might expect with a conventional ‘n = 5’ averaging, and should be considered when designing any microarray for which randomization is feasible.

Availability: CRAM is implemented as version 2 of the hoptag software package for R, which is included in the Supplementary information.

Contact: dyuan{at}jhmi.edu

Supplementary information: Supplementary Data are available at Bioinformatics online.

Received on April 28, 2006; revised on August 3, 2006; accepted on October 12, 2006

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