Analyzing high-density oligonucleotide gene expression array data.
We have developed methods and identified problems associated with the analysis of data generated by high-density, oligonuceotide gene expression arrays. Our methods are aimed at accounting for many of the sources of variation that make it difficult, at times, to realize consistent results. We present here descriptions of some of these methods and how they impact the analysis of oligonucleotide gene expression array data. We will discuss the process of recognizing the "spots" (or features) on the Affymetrix GeneChip(R) probe arrays, correcting for background and intensity gradients in the resulting images, scaling/normalizing an array to allow array-to-array comparisons, monitoring probe performance with respect to hybridization efficiency, and assessing whether a gene is present or differentially expressed. Examples from the analyses of gene expression validation data are presented to contrast the different methods applied to these types of data.
Schadt
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Department of Biomathematics, University of California, Los Angeles, California 90095, USA. eric.schadt@roche.com
Li
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Wong
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D001483:Base Sequence; D004247:DNA; D020224:Expressed Sequence Tags; D020869:Gene Expression Profiling; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis
D004247:DNA