A proposed metric for assessing the measurement quality of individual microarrays

<p>Abstract</p> <p>Background</p> <p>High-density microarray technology is increasingly applied to study gene expression levels on a large scale. Microarray experiments rely on several critical steps that may introduce error and uncertainty in analyses. These steps incl...

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Main Authors: Scheirer Katherine E, Barnes Stephen, Beasley T Mark, Page Grier P, Kim Kyoungmi, Allison David B
Format: Article
Language:English
Published: BMC 2006-01-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/7/35
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spelling doaj-59c23dd1503e4e94a28d6335652ffdf72020-11-25T00:54:44ZengBMCBMC Bioinformatics1471-21052006-01-01713510.1186/1471-2105-7-35A proposed metric for assessing the measurement quality of individual microarraysScheirer Katherine EBarnes StephenBeasley T MarkPage Grier PKim KyoungmiAllison David B<p>Abstract</p> <p>Background</p> <p>High-density microarray technology is increasingly applied to study gene expression levels on a large scale. Microarray experiments rely on several critical steps that may introduce error and uncertainty in analyses. These steps include mRNA sample extraction, amplification and labeling, hybridization, and scanning. In some cases this may be manifested as systematic spatial variation on the surface of microarray in which expression measurements within an individual array may vary as a function of geographic position on the array surface.</p> <p>Results</p> <p>We hypothesized that an index of the degree of spatiality of gene expression measurements associated with their physical geographic locations on an array could indicate the summary of the physical reliability of the microarray. We introduced a novel way to formulate this index using a statistical analysis tool. Our approach regressed gene expression intensity measurements on a polynomial response surface of the microarray's Cartesian coordinates. We demonstrated this method using a fixed model and presented results from real and simulated datasets.</p> <p>Conclusion</p> <p>We demonstrated the potential of such a quantitative metric for assessing the reliability of individual arrays. Moreover, we showed that this procedure can be incorporated into laboratory practice as a means to set quality control specifications and as a tool to determine whether an array has sufficient quality to be retained in terms of spatial correlation of gene expression measurements.</p> http://www.biomedcentral.com/1471-2105/7/35
collection DOAJ
language English
format Article
sources DOAJ
author Scheirer Katherine E
Barnes Stephen
Beasley T Mark
Page Grier P
Kim Kyoungmi
Allison David B
spellingShingle Scheirer Katherine E
Barnes Stephen
Beasley T Mark
Page Grier P
Kim Kyoungmi
Allison David B
A proposed metric for assessing the measurement quality of individual microarrays
BMC Bioinformatics
author_facet Scheirer Katherine E
Barnes Stephen
Beasley T Mark
Page Grier P
Kim Kyoungmi
Allison David B
author_sort Scheirer Katherine E
title A proposed metric for assessing the measurement quality of individual microarrays
title_short A proposed metric for assessing the measurement quality of individual microarrays
title_full A proposed metric for assessing the measurement quality of individual microarrays
title_fullStr A proposed metric for assessing the measurement quality of individual microarrays
title_full_unstemmed A proposed metric for assessing the measurement quality of individual microarrays
title_sort proposed metric for assessing the measurement quality of individual microarrays
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2006-01-01
description <p>Abstract</p> <p>Background</p> <p>High-density microarray technology is increasingly applied to study gene expression levels on a large scale. Microarray experiments rely on several critical steps that may introduce error and uncertainty in analyses. These steps include mRNA sample extraction, amplification and labeling, hybridization, and scanning. In some cases this may be manifested as systematic spatial variation on the surface of microarray in which expression measurements within an individual array may vary as a function of geographic position on the array surface.</p> <p>Results</p> <p>We hypothesized that an index of the degree of spatiality of gene expression measurements associated with their physical geographic locations on an array could indicate the summary of the physical reliability of the microarray. We introduced a novel way to formulate this index using a statistical analysis tool. Our approach regressed gene expression intensity measurements on a polynomial response surface of the microarray's Cartesian coordinates. We demonstrated this method using a fixed model and presented results from real and simulated datasets.</p> <p>Conclusion</p> <p>We demonstrated the potential of such a quantitative metric for assessing the reliability of individual arrays. Moreover, we showed that this procedure can be incorporated into laboratory practice as a means to set quality control specifications and as a tool to determine whether an array has sufficient quality to be retained in terms of spatial correlation of gene expression measurements.</p>
url http://www.biomedcentral.com/1471-2105/7/35
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