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Robust Significance Analysis of Microarrays by Minimum β-Divergence Method

Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it...

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Autores principales: Shahjaman, Md., Kumar, Nishith, Mollah, Md. Manir Hossain, Ahmed, Md. Shakil, Ara Begum, Anjuman, Shahinul Islam, S. M., Mollah, Md. Nurul Haque
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5551475/
https://www.ncbi.nlm.nih.gov/pubmed/28819626
http://dx.doi.org/10.1155/2017/5310198
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author Shahjaman, Md.
Kumar, Nishith
Mollah, Md. Manir Hossain
Ahmed, Md. Shakil
Ara Begum, Anjuman
Shahinul Islam, S. M.
Mollah, Md. Nurul Haque
author_facet Shahjaman, Md.
Kumar, Nishith
Mollah, Md. Manir Hossain
Ahmed, Md. Shakil
Ara Begum, Anjuman
Shahinul Islam, S. M.
Mollah, Md. Nurul Haque
author_sort Shahjaman, Md.
collection PubMed
description Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum β-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition.
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spelling pubmed-55514752017-08-17 Robust Significance Analysis of Microarrays by Minimum β-Divergence Method Shahjaman, Md. Kumar, Nishith Mollah, Md. Manir Hossain Ahmed, Md. Shakil Ara Begum, Anjuman Shahinul Islam, S. M. Mollah, Md. Nurul Haque Biomed Res Int Research Article Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum β-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition. Hindawi 2017 2017-07-27 /pmc/articles/PMC5551475/ /pubmed/28819626 http://dx.doi.org/10.1155/2017/5310198 Text en Copyright © 2017 Md. Shahjaman et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Shahjaman, Md.
Kumar, Nishith
Mollah, Md. Manir Hossain
Ahmed, Md. Shakil
Ara Begum, Anjuman
Shahinul Islam, S. M.
Mollah, Md. Nurul Haque
Robust Significance Analysis of Microarrays by Minimum β-Divergence Method
title Robust Significance Analysis of Microarrays by Minimum β-Divergence Method
title_full Robust Significance Analysis of Microarrays by Minimum β-Divergence Method
title_fullStr Robust Significance Analysis of Microarrays by Minimum β-Divergence Method
title_full_unstemmed Robust Significance Analysis of Microarrays by Minimum β-Divergence Method
title_short Robust Significance Analysis of Microarrays by Minimum β-Divergence Method
title_sort robust significance analysis of microarrays by minimum β-divergence method
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5551475/
https://www.ncbi.nlm.nih.gov/pubmed/28819626
http://dx.doi.org/10.1155/2017/5310198
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