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Unsupervised assessment of microarray data quality using a Gaussian mixture model
BACKGROUND: Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generall...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2717951/ https://www.ncbi.nlm.nih.gov/pubmed/19545436 http://dx.doi.org/10.1186/1471-2105-10-191 |
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author | Howard, Brian E Sick, Beate Heber, Steffen |
author_facet | Howard, Brian E Sick, Beate Heber, Steffen |
author_sort | Howard, Brian E |
collection | PubMed |
description | BACKGROUND: Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny. RESULTS: We show how an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and the naïve Bayes model can be used to automate microarray quality assessment. The method is flexible and can be easily adapted to accommodate alternate quality statistics and platforms. We evaluate our approach using Affymetrix 3' gene expression and exon arrays and compare the performance of this method to a similar supervised approach. CONCLUSION: This research illustrates the efficacy of an unsupervised classification approach for the purpose of automated microarray data quality assessment. Since our approach requires only unannotated training data, it is easy to customize and to keep up-to-date as technology evolves. In contrast to other "black box" classification systems, this method also allows for intuitive explanations. |
format | Text |
id | pubmed-2717951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-27179512009-07-30 Unsupervised assessment of microarray data quality using a Gaussian mixture model Howard, Brian E Sick, Beate Heber, Steffen BMC Bioinformatics Research Article BACKGROUND: Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny. RESULTS: We show how an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and the naïve Bayes model can be used to automate microarray quality assessment. The method is flexible and can be easily adapted to accommodate alternate quality statistics and platforms. We evaluate our approach using Affymetrix 3' gene expression and exon arrays and compare the performance of this method to a similar supervised approach. CONCLUSION: This research illustrates the efficacy of an unsupervised classification approach for the purpose of automated microarray data quality assessment. Since our approach requires only unannotated training data, it is easy to customize and to keep up-to-date as technology evolves. In contrast to other "black box" classification systems, this method also allows for intuitive explanations. BioMed Central 2009-06-22 /pmc/articles/PMC2717951/ /pubmed/19545436 http://dx.doi.org/10.1186/1471-2105-10-191 Text en Copyright © 2009 Howard 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 Howard, Brian E Sick, Beate Heber, Steffen Unsupervised assessment of microarray data quality using a Gaussian mixture model |
title | Unsupervised assessment of microarray data quality using a Gaussian mixture model |
title_full | Unsupervised assessment of microarray data quality using a Gaussian mixture model |
title_fullStr | Unsupervised assessment of microarray data quality using a Gaussian mixture model |
title_full_unstemmed | Unsupervised assessment of microarray data quality using a Gaussian mixture model |
title_short | Unsupervised assessment of microarray data quality using a Gaussian mixture model |
title_sort | unsupervised assessment of microarray data quality using a gaussian mixture model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2717951/ https://www.ncbi.nlm.nih.gov/pubmed/19545436 http://dx.doi.org/10.1186/1471-2105-10-191 |
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