Use of signal thresholds to determine significant changes in microarray data analyses
Autor(a) principal: | |
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Data de Publicação: | 2005 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Genetics and Molecular Biology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572005000200002 |
Resumo: | The use of a constant fold-change to determine significant changes in gene expression has been widely accepted for its intuition and ease of use in microarray data analysis, but this concept has been increasingly criticized because it does not reflect signal intensity and can result in a substantial number of false positives and false negatives. To resolve this dilemma, we have analyzed 65 replicate Affymetrix chip-chip comparisons and determined a series of user adjustable signal-dependent thresholds which do not require replicates and offer a 95% confidence interval. Quantitative RT-PCR shows that such thresholds significantly improve the power to discriminate biological changes in mRNA from noise and reduce false calls compared to the traditional two-fold threshold. The user-friendly nature of this approach means that it can be easily applied by any user of microarray analysis, even those without any specialized knowledge of computational techniques or statistics. Noise is a function of signal intensity not only for Affymetrix data but also for cDNA array data, analysis of which may also be benefited by our methodology. |
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Genetics and Molecular Biology |
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Use of signal thresholds to determine significant changes in microarray data analysesmicroarraysignal thresholdaffymetrixdata analysisThe use of a constant fold-change to determine significant changes in gene expression has been widely accepted for its intuition and ease of use in microarray data analysis, but this concept has been increasingly criticized because it does not reflect signal intensity and can result in a substantial number of false positives and false negatives. To resolve this dilemma, we have analyzed 65 replicate Affymetrix chip-chip comparisons and determined a series of user adjustable signal-dependent thresholds which do not require replicates and offer a 95% confidence interval. Quantitative RT-PCR shows that such thresholds significantly improve the power to discriminate biological changes in mRNA from noise and reduce false calls compared to the traditional two-fold threshold. The user-friendly nature of this approach means that it can be easily applied by any user of microarray analysis, even those without any specialized knowledge of computational techniques or statistics. Noise is a function of signal intensity not only for Affymetrix data but also for cDNA array data, analysis of which may also be benefited by our methodology.Sociedade Brasileira de Genética2005-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572005000200002Genetics and Molecular Biology v.28 n.2 2005reponame:Genetics and Molecular Biologyinstname:Sociedade Brasileira de Genética (SBG)instacron:SBG10.1590/S1415-47572005000200002info:eu-repo/semantics/openAccessXinmin,LiKim,JaejungZhou,JianGu,WeikuanQuigg,Richardeng2005-07-11T00:00:00Zoai:scielo:S1415-47572005000200002Revistahttp://www.gmb.org.br/ONGhttps://old.scielo.br/oai/scielo-oai.php||editor@gmb.org.br1678-46851415-4757opendoar:2005-07-11T00:00Genetics and Molecular Biology - Sociedade Brasileira de Genética (SBG)false |
dc.title.none.fl_str_mv |
Use of signal thresholds to determine significant changes in microarray data analyses |
title |
Use of signal thresholds to determine significant changes in microarray data analyses |
spellingShingle |
Use of signal thresholds to determine significant changes in microarray data analyses Xinmin,Li microarray signal threshold affymetrix data analysis |
title_short |
Use of signal thresholds to determine significant changes in microarray data analyses |
title_full |
Use of signal thresholds to determine significant changes in microarray data analyses |
title_fullStr |
Use of signal thresholds to determine significant changes in microarray data analyses |
title_full_unstemmed |
Use of signal thresholds to determine significant changes in microarray data analyses |
title_sort |
Use of signal thresholds to determine significant changes in microarray data analyses |
author |
Xinmin,Li |
author_facet |
Xinmin,Li Kim,Jaejung Zhou,Jian Gu,Weikuan Quigg,Richard |
author_role |
author |
author2 |
Kim,Jaejung Zhou,Jian Gu,Weikuan Quigg,Richard |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Xinmin,Li Kim,Jaejung Zhou,Jian Gu,Weikuan Quigg,Richard |
dc.subject.por.fl_str_mv |
microarray signal threshold affymetrix data analysis |
topic |
microarray signal threshold affymetrix data analysis |
description |
The use of a constant fold-change to determine significant changes in gene expression has been widely accepted for its intuition and ease of use in microarray data analysis, but this concept has been increasingly criticized because it does not reflect signal intensity and can result in a substantial number of false positives and false negatives. To resolve this dilemma, we have analyzed 65 replicate Affymetrix chip-chip comparisons and determined a series of user adjustable signal-dependent thresholds which do not require replicates and offer a 95% confidence interval. Quantitative RT-PCR shows that such thresholds significantly improve the power to discriminate biological changes in mRNA from noise and reduce false calls compared to the traditional two-fold threshold. The user-friendly nature of this approach means that it can be easily applied by any user of microarray analysis, even those without any specialized knowledge of computational techniques or statistics. Noise is a function of signal intensity not only for Affymetrix data but also for cDNA array data, analysis of which may also be benefited by our methodology. |
publishDate |
2005 |
dc.date.none.fl_str_mv |
2005-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572005000200002 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572005000200002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S1415-47572005000200002 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Genética |
publisher.none.fl_str_mv |
Sociedade Brasileira de Genética |
dc.source.none.fl_str_mv |
Genetics and Molecular Biology v.28 n.2 2005 reponame:Genetics and Molecular Biology instname:Sociedade Brasileira de Genética (SBG) instacron:SBG |
instname_str |
Sociedade Brasileira de Genética (SBG) |
instacron_str |
SBG |
institution |
SBG |
reponame_str |
Genetics and Molecular Biology |
collection |
Genetics and Molecular Biology |
repository.name.fl_str_mv |
Genetics and Molecular Biology - Sociedade Brasileira de Genética (SBG) |
repository.mail.fl_str_mv |
||editor@gmb.org.br |
_version_ |
1752122379472994304 |