TY - JOUR
T1 - A model-based background adjustment for oligonucleotide expression arrays
AU - Wu, Zhijin
AU - Irizarry, Rafael A.
AU - Gentleman, Robert
AU - Martinez-Murillo, Francisco
AU - Spencer, Forrest
N1 - Funding Information:
Zhijin Wu is Graduate Student, Department of Biostatistics; Rafael A. Irizarry is Associate Professor, Department of Biostatistics (E-mail: rafa@jhu. edu); Francisco Martinez-Murillo is Manager, JHMI Microarray Core; and Forrest Spencer is Associate Professor, Department of Medicine and Molecular Biology and Genetics, and Director, JHMI Microarray Core, Johns Hopkins University, Baltimore, MD 21205. Robert Gentleman is Associate Professor, Department of Biostatistics, Dana Farber Cancer Institute, Boston, MA 02115. The authors thank Terry Speed, Ben Bolstad, and Earl Hubbell for insightful discussions that helped develop the ideas in this article. They also thank Wolfgang Huber and Tom Louis for some helpful comments. The work of Rafael Irizarry is partially funded by the Hopkins PGA (http://www.hopkins-genomics.org) Administrative/Bioinformatics Component (P01 HL 66583). The work of Zhijin Wu is partially funded by the Johnson & Johnson Research Foundation.
PY - 2004/12
Y1 - 2004/12
N2 - High-density oligonucleolide expression arrays are widely used in many areas of biomedical research. Affymetrix GeneChip arrays are the most popular. In the Affymetrix system, a fair amount of further preprocessing and data reduction occurs after the image-processing step. Statistical procedures developed by academic groups have been successful in improving the default algorithms provided by the Affymetrix system. In this article we present a solution to one of the preprocessing steps-background adjustment-based on a formal statistical framework. Our solution greatly improves the performance of the technology in various practical applications. These arrays use short oligonucleotides to probe for genes in an RNA sample. Typically, each gene is represented by 11-20 pairs of oligonucleotide probes. The first component of these pairs is referred to as a perfect match probe and is designed to hybridize only with transcripts from the intended gene (i.e., specific hybridization). However, hybridization by other sequences (i.e., nonspecific hybridization) is unavoidable. Furthermore, hybridization strengths are measured by a scanner that introduces optical noise. Therefore, the observed intensities need to be adjusted to give accurate measurements of specific hybridization. We have found that the default ad hoc adjustment, provided as part of the Affymetrix system, can be improved through the use of estimators derived from a statistical model that uses probe sequence information. A final step in preprocessing is to summarize the probe-level data for each gene to define a measure of expression that represents the amount of the corresponding mRNA species. In this article we illustrate the practical consequences of not adjusting appropriately for the presence of nonspecific hybridization and provide a solution based on our background adjustment procedure. Software that computes our adjustment is available as part of the Bioconductor Project (http://www.bioconductor.org).
AB - High-density oligonucleolide expression arrays are widely used in many areas of biomedical research. Affymetrix GeneChip arrays are the most popular. In the Affymetrix system, a fair amount of further preprocessing and data reduction occurs after the image-processing step. Statistical procedures developed by academic groups have been successful in improving the default algorithms provided by the Affymetrix system. In this article we present a solution to one of the preprocessing steps-background adjustment-based on a formal statistical framework. Our solution greatly improves the performance of the technology in various practical applications. These arrays use short oligonucleotides to probe for genes in an RNA sample. Typically, each gene is represented by 11-20 pairs of oligonucleotide probes. The first component of these pairs is referred to as a perfect match probe and is designed to hybridize only with transcripts from the intended gene (i.e., specific hybridization). However, hybridization by other sequences (i.e., nonspecific hybridization) is unavoidable. Furthermore, hybridization strengths are measured by a scanner that introduces optical noise. Therefore, the observed intensities need to be adjusted to give accurate measurements of specific hybridization. We have found that the default ad hoc adjustment, provided as part of the Affymetrix system, can be improved through the use of estimators derived from a statistical model that uses probe sequence information. A final step in preprocessing is to summarize the probe-level data for each gene to define a measure of expression that represents the amount of the corresponding mRNA species. In this article we illustrate the practical consequences of not adjusting appropriately for the presence of nonspecific hybridization and provide a solution based on our background adjustment procedure. Software that computes our adjustment is available as part of the Bioconductor Project (http://www.bioconductor.org).
KW - Asymetrix GeneChip
KW - Background adjustment
KW - Expression Measure
KW - High density oligonucleotide microarrays
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U2 - 10.1198/016214504000000683
DO - 10.1198/016214504000000683
M3 - Article
AN - SCOPUS:10844290766
SN - 0162-1459
VL - 99
SP - 909
EP - 917
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 468
ER -