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Bioinformatics Advance Access originally published online on December 5, 2006
Bioinformatics 2007 23(3):321-327; doi:10.1093/bioinformatics/btl609
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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.

A distribution free summarization method for Affymetrix GeneChip® arrays

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

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

*To whom correspondence should be addressed.


  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 datasets, 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 datasets, the area under the receiver operating characteristic 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.

Contact: mmcgee{at}smu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Martin Bishop

Received on July 18, 2006; revised on November 6, 2006; accepted on November 24, 2006

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