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Bioinformatics Advance Access published online on December 5, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl609
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© 2006 The Author(s)
Received July 18, 2006
Revised November 6, 2006
Accepted November 24, 2006

Article

A distribution free summarization method for Affymetrix GeneChip® arrays

Zhongxue Chen 1, Monnie McGee 2 *, Qingzhong Liu 3, and Richard H. Scheuermann 4

1 Department of Statistical Science, Southern Methodist University, Dallas, TX 75275 USA; Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390 USA
2 Department of Statistical Science, Southern Methodist University, Dallas, TX 75275 USA
3 Department of Computer Science, New Mexico Institute of Mining and Technology, Socorro NM 87801 USA
4 Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390 USA

* To whom correspondence should be addressed.
Monnie McGee, E-mail: mmcgee{at}smu.edu


  Abstract

Motivation: Affymetrix GeneChip arrays require summarization in order to combine the probe-level intensities into one value representing the expression level of a gene. However, probe intensity measurements are expected to be affected by different levels of non-specific- and cross-hybridization to non-specific transcripts. Here we present a new summarization technique, the Distribution Free Weighted method (DFW), which uses information about the variability in probe behavior to estimate the extent of non-specific and cross-hybridization for each probe. The contribution of the probe is weighted accordingly during summarization, without making any distributional assumptions for the probe-level data.

Results: We compare DFW with several popular summarization methods on spike-in data sets, via both our own calculations and the ‘Affycomp II’ competition. The results show that DFW outperforms other methods when sensitivity and specificity are considered simultaneously. With the Affycomp spike-in data sets, the area under the Receiver Operating Characteristic (ROC) curve for DFW is nearly 1.0 (a perfect value), indicating that DFW can identify all differentially expressed genes with a few false positives. The approach used is also computationally faster than most other methods in current use.

Availability: The R code for DFW is available upon request.

Associate Editor: Martin Bishop
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