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Analyzing high‐density oligonucleotide gene expression array data
Author(s) -
Schadt Eric E.,
Li Cheng,
Su Cheng,
Wong Wing H.
Publication year - 2000
Publication title -
journal of cellular biochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.028
H-Index - 165
eISSN - 1097-4644
pISSN - 0730-2312
DOI - 10.1002/1097-4644(20010201)80:2<192::aid-jcb50>3.0.co;2-w
Subject(s) - computational biology , oligonucleotide , gene expression , dna microarray , biology , expression (computer science) , gene , data mining , computer science , gene expression profiling , gene chip analysis , genetics , programming language
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® 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. J. Cell. Biochem. 80:192–202, 2000. © 2000 Wiley‐Liss, Inc.