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Bioinformatics Advance Access published online on January 12, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti258
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Bioinformatics © Oxford University Press 2005; all rights reserved.
Received June 24, 2004
Revised December 21, 2004
Accepted December 29, 2004

Article

Robust estimation of protein expression ratios with lysate microarray technology

Cristian Mircean 1, Ilya Shmulevich 2*, David Cogdell 2, Woonyoung Choi 2, Yu Jia 2, Ioan Tabus 3, Stanley R. Hamilton 2, and Wei Zhang 2

1 Department of Pathology, University of Texas M. D. Anderson Cancer Center, Houston, TX, Finland; Institute of Signal Processing, Tampere University of Technology, Tampere, Finland
2 Department of Pathology, University of Texas M. D. Anderson Cancer Center, Houston, TX, Finland
3 Institute of Signal Processing, Tampere University of Technology, Tampere, Finland

* To whom correspondence should be addressed.
Ilya Shmulevich, E-mail: is{at}ieee.org


  Abstract

Motivation: The protein lysate microarray is a developing proteomic technology for simultaneously measuring protein expression levels in a large number of biological samples. A challenge for accurate quantification is the relatively narrow dynamic range associated with the commonly used chromogenic signal detection system. To facilitate accurate measurement of the relative expression levels, each sample is serially diluted and each diluted version is spotted on a nitrocellulose-coated slide in triplicate. Thus, each sample yields multiple measurements in different dynamic ranges of the detection system. This study aims to develop suitable algorithms that yield accurate representations of the relative expression levels in different samples from multiple data points.

Results: We evaluated two algorithms for estimating relative protein expression in different samples on the lysate microarray by means of a cross-validation procedure. For this purpose as well as for quality control, we designed a 1440-spot lysate microarray containing 80 identical samples of purified bovine serum albumin, printed in triplicate with six two-fold dilutions. Our analysis showed that the algorithm based on a robust least squares estimator provided the most accurate quantification of the protein lysate microarray data. We also demonstrate our methods by estimating relative expression levels of p53 and p21 in either p53+/+ or p53-/- HCT116 colon cancer cells after two drug treatments and their combinations on another lysate microarray.


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