Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations

Persistent Link:
http://hdl.handle.net/10150/610395
Title:
Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations
Author:
Shippy, Richard; Sendera, Timothy; Lockner, Randall; Palaniappan, Chockalingam; Kaysser-Kranich, Tamma; Watts, George; Alsobrook, John
Affiliation:
GE Heathcare (formerly Amersham Biosciences) Chandler, Arizona 85248, USA; Microarray Shared Service, Arizona Cancer Center, Tucson, Arizona 85724, USA; Child Study Center, Yale University School of Medicine, New Haven, Connecticut 06510, USA
Issue Date:
2004
Publisher:
BioMed Central
Citation:
BMC Genomics 2004, 5:61 doi:10.1186/1471-2164-5-61
Journal:
BMC Genomics
Rights:
© 2004 Shippy et al; licensee BioMed Central Ltd. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)
Collection Information:
This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.
Abstract:
BACKGROUND:Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations.RESULTS:In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips(R) and Amersham CodeLinkTM UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of inter-platform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively.CONCLUSIONS:As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.
EISSN:
1471-2164
DOI:
10.1186/1471-2164-5-61
Version:
Final published version
Additional Links:
http://www.biomedcentral.com/1471-2164/5/61

Full metadata record

DC FieldValue Language
dc.contributor.authorShippy, Richarden
dc.contributor.authorSendera, Timothyen
dc.contributor.authorLockner, Randallen
dc.contributor.authorPalaniappan, Chockalingamen
dc.contributor.authorKaysser-Kranich, Tammaen
dc.contributor.authorWatts, Georgeen
dc.contributor.authorAlsobrook, Johnen
dc.date.accessioned2016-05-20T09:05:52Z-
dc.date.available2016-05-20T09:05:52Z-
dc.date.issued2004en
dc.identifier.citationBMC Genomics 2004, 5:61 doi:10.1186/1471-2164-5-61en
dc.identifier.doi10.1186/1471-2164-5-61en
dc.identifier.urihttp://hdl.handle.net/10150/610395-
dc.description.abstractBACKGROUND:Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations.RESULTS:In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips(R) and Amersham CodeLinkTM UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of inter-platform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively.CONCLUSIONS:As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttp://www.biomedcentral.com/1471-2164/5/61en
dc.rights© 2004 Shippy et al; licensee BioMed Central Ltd. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)en
dc.titlePerformance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlationsen
dc.typeArticleen
dc.identifier.eissn1471-2164en
dc.contributor.departmentGE Heathcare (formerly Amersham Biosciences) Chandler, Arizona 85248, USAen
dc.contributor.departmentMicroarray Shared Service, Arizona Cancer Center, Tucson, Arizona 85724, USAen
dc.contributor.departmentChild Study Center, Yale University School of Medicine, New Haven, Connecticut 06510, USAen
dc.identifier.journalBMC Genomicsen
dc.description.collectioninformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
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