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Sources of variation in Affymetrix microarray experiments
BACKGROUND: A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were:...
Autores principales: | , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2005
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1232851/ https://www.ncbi.nlm.nih.gov/pubmed/16124883 http://dx.doi.org/10.1186/1471-2105-6-214 |
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author | Zakharkin, Stanislav O Kim, Kyoungmi Mehta, Tapan Chen, Lang Barnes, Stephen Scheirer, Katherine E Parrish, Rudolph S Allison, David B Page, Grier P |
author_facet | Zakharkin, Stanislav O Kim, Kyoungmi Mehta, Tapan Chen, Lang Barnes, Stephen Scheirer, Katherine E Parrish, Rudolph S Allison, David B Page, Grier P |
author_sort | Zakharkin, Stanislav O |
collection | PubMed |
description | BACKGROUND: A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates. RESULTS: We performed a microarray experiment using a total of 24 Affymetrix GeneChip(® )arrays. The study included 4(th )mammary gland samples from eight 21-day-old Sprague Dawley CD female rats exposed to genistein (soy isoflavone). RNA samples from each rat were split to assess variation arising at labeling and hybridization steps. A general linear model was used to estimate variance components. Pearson correlations were computed to evaluate agreement between technical and biological replicates. CONCLUSION: The greatest source of variation was biological variation, followed by residual error, and finally variation due to labeling when *.cel files were processed with dChip and RMA image processing algorithms. When MAS 5.0 or GCRMA-EB were used, the greatest source of variation was residual error, followed by biology and labeling. Correlations between technical replicates were consistently higher than between biological replicates. |
format | Text |
id | pubmed-1232851 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2005 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-12328512005-09-24 Sources of variation in Affymetrix microarray experiments Zakharkin, Stanislav O Kim, Kyoungmi Mehta, Tapan Chen, Lang Barnes, Stephen Scheirer, Katherine E Parrish, Rudolph S Allison, David B Page, Grier P BMC Bioinformatics Research Article BACKGROUND: A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates. RESULTS: We performed a microarray experiment using a total of 24 Affymetrix GeneChip(® )arrays. The study included 4(th )mammary gland samples from eight 21-day-old Sprague Dawley CD female rats exposed to genistein (soy isoflavone). RNA samples from each rat were split to assess variation arising at labeling and hybridization steps. A general linear model was used to estimate variance components. Pearson correlations were computed to evaluate agreement between technical and biological replicates. CONCLUSION: The greatest source of variation was biological variation, followed by residual error, and finally variation due to labeling when *.cel files were processed with dChip and RMA image processing algorithms. When MAS 5.0 or GCRMA-EB were used, the greatest source of variation was residual error, followed by biology and labeling. Correlations between technical replicates were consistently higher than between biological replicates. BioMed Central 2005-08-29 /pmc/articles/PMC1232851/ /pubmed/16124883 http://dx.doi.org/10.1186/1471-2105-6-214 Text en Copyright © 2005 Zakharkin et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zakharkin, Stanislav O Kim, Kyoungmi Mehta, Tapan Chen, Lang Barnes, Stephen Scheirer, Katherine E Parrish, Rudolph S Allison, David B Page, Grier P Sources of variation in Affymetrix microarray experiments |
title | Sources of variation in Affymetrix microarray experiments |
title_full | Sources of variation in Affymetrix microarray experiments |
title_fullStr | Sources of variation in Affymetrix microarray experiments |
title_full_unstemmed | Sources of variation in Affymetrix microarray experiments |
title_short | Sources of variation in Affymetrix microarray experiments |
title_sort | sources of variation in affymetrix microarray experiments |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1232851/ https://www.ncbi.nlm.nih.gov/pubmed/16124883 http://dx.doi.org/10.1186/1471-2105-6-214 |
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