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Activity Number:
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471
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2003 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #302259 |
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Title:
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Statistical Methods for Chip Calibration and Saturation Effects in Antibody-Spiked Gene Expression Data
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Author(s):
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Jingjin Li*+ and Sunil Rao
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Companies:
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Indiana University-Purdue University Indianapolis and Case Western Reserve University
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Address:
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1050 Wishard Blvd., RG4101, Indianapolis, IN, 46202,
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Keywords:
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linear mixed effects model ; parametric and nonparametric methods ; microarray ; gene expression
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Abstract:
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Oligonucleotide microarrays are amongst a set of technologies that allow for high throughput assessment of vast numbers of gene expressions. In order to evaluate gene expressions given detection limits, antibody spiking is often used providing one with an expression curve relating antibody-treated expression and nonantibody-treated expression. These curves can exhibit different functional shapes across chips and hence need to be standardized. In addition, each curve is subject to saturation effects which are typically dealt with by extrapolating a linear fit to the subset of the data not visually subject to saturation. We introduce methods for the nonparametric standardization of expression curves using univariate smoothers. We also explore parametric methods for more efficient analysis of the standardized curves. We demonstrate an alternate method of parametric analysis using a weighted linear mixed effects model that does not arbitrarily delete data beyond an observed saturation point, which allows for natural grouping of genes and provides significantly more accurate predictions than naïve linear extrapolation. Both methodologies are studied through sets of simulations.
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