Linked Life Data

11074587

Source:http://linkedlifedata.com/resource/pubmed/id/11074587

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2001-1-25
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0730-2312
pubmed:author
pubmed:copyrightInfo
Copyright 2000 Wiley-Liss, Inc.
pubmed:issnType
Print
pubmed:day
20
pubmed:volume
80
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
192-202
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Analyzing high-density oligonucleotide gene expression array data.
pubmed:affiliation
Department of Biomathematics, University of California, Los Angeles, California 90095, USA. eric.schadt@roche.com
pubmed:publicationType
Journal Article