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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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 |
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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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