There is no silver bullet: a guide to low-level data transforms and normalisation methods for microarray data
To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficul...
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Published in: | Briefings in bioinformatics Vol. 6; no. 1; pp. 86 - 97 |
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Main Authors: | , |
Format: | Journal Article |
Language: | English |
Published: |
Oxford
Oxford University Press
01-03-2005
Oxford Publishing Limited (England) |
Subjects: | |
Online Access: | Get full text |
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Summary: | To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 |
ISSN: | 1467-5463 1477-4054 |
DOI: | 10.1093/bib/6.1.86 |