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Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression
BACKGROUND: The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2006
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1579233/ https://www.ncbi.nlm.nih.gov/pubmed/16934150 http://dx.doi.org/10.1186/1471-2105-7-391 |
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author | Lemieux, Sébastien |
author_facet | Lemieux, Sébastien |
author_sort | Lemieux, Sébastien |
collection | PubMed |
description | BACKGROUND: The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model) method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication. RESULTS: On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition. CONCLUSION: The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition. |
format | Text |
id | pubmed-1579233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-15792332006-09-28 Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression Lemieux, Sébastien BMC Bioinformatics Research Article BACKGROUND: The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model) method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication. RESULTS: On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition. CONCLUSION: The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition. BioMed Central 2006-08-25 /pmc/articles/PMC1579233/ /pubmed/16934150 http://dx.doi.org/10.1186/1471-2105-7-391 Text en Copyright © 2006 Lemieux; 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 Lemieux, Sébastien Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression |
title | Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression |
title_full | Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression |
title_fullStr | Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression |
title_full_unstemmed | Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression |
title_short | Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression |
title_sort | probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1579233/ https://www.ncbi.nlm.nih.gov/pubmed/16934150 http://dx.doi.org/10.1186/1471-2105-7-391 |
work_keys_str_mv | AT lemieuxsebastien probelevellinearmodelfittingandmixturemodelingresultsinhighaccuracydetectionofdifferentialgeneexpression |