Linked Life Data

17059591

Source:http://linkedlifedata.com/resource/pubmed/id/17059591

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2006-10-26
pubmed:abstractText
Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA.
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-11532216, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-11936955, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-12538238, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-12582260, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-12925520, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-14960456, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-14960458, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-15461798, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-15608262, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-15693945, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-15705192, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-15846361, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-16498083, http://linkedlifedata.com/resource/pubmed/commentcorrection/17059591-16877752
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1471-2105
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
464
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database.
pubmed:affiliation
Gene Logic Inc., 610 Professional Dr, Gaithersburg, MD, 20876, USA. skatz@genelogic.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies