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The impact of sample imbalance on identifying differentially expressed genes

BACKGROUND: Recently several statistical methods have been proposed to identify genes with differential expression between two conditions. However, very few studies consider the problem of sample imbalance and there is no study to investigate the impact of sample imbalance on identifying differentia...

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Detalles Bibliográficos
Autores principales: Yang, Kun, Li, Jianzhong, Gao, Hong
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1780111/
https://www.ncbi.nlm.nih.gov/pubmed/17217526
http://dx.doi.org/10.1186/1471-2105-7-S4-S8
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author Yang, Kun
Li, Jianzhong
Gao, Hong
author_facet Yang, Kun
Li, Jianzhong
Gao, Hong
author_sort Yang, Kun
collection PubMed
description BACKGROUND: Recently several statistical methods have been proposed to identify genes with differential expression between two conditions. However, very few studies consider the problem of sample imbalance and there is no study to investigate the impact of sample imbalance on identifying differential expression genes. In addition, it is not clear which method is more suitable for the unbalanced data. RESULTS: Based on random sampling, two evaluation models are proposed to investigate the impact of sample imbalance on identifying differential expression genes. Using the proposed evaluation models, the performances of six famous methods are compared on the unbalanced data. The experimental results indicate that the sample imbalance has a great influence on selecting differential expression genes. Furthermore, different methods have very different performances on the unbalanced data. Among the six methods, the welch t-test appears to perform best when the size of samples in the large variance group is larger than that in the small one, while the Regularized t-test and SAM outperform others on the unbalanced data in other cases. CONCLUSION: Two proposed evaluation models are effective and sample imbalance should be taken into account in microarray experiment design and gene expression data analysis. The results and two proposed evaluation models can provide some help in selecting suitable method to process the unbalanced data.
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spelling pubmed-17801112007-01-24 The impact of sample imbalance on identifying differentially expressed genes Yang, Kun Li, Jianzhong Gao, Hong BMC Bioinformatics Research BACKGROUND: Recently several statistical methods have been proposed to identify genes with differential expression between two conditions. However, very few studies consider the problem of sample imbalance and there is no study to investigate the impact of sample imbalance on identifying differential expression genes. In addition, it is not clear which method is more suitable for the unbalanced data. RESULTS: Based on random sampling, two evaluation models are proposed to investigate the impact of sample imbalance on identifying differential expression genes. Using the proposed evaluation models, the performances of six famous methods are compared on the unbalanced data. The experimental results indicate that the sample imbalance has a great influence on selecting differential expression genes. Furthermore, different methods have very different performances on the unbalanced data. Among the six methods, the welch t-test appears to perform best when the size of samples in the large variance group is larger than that in the small one, while the Regularized t-test and SAM outperform others on the unbalanced data in other cases. CONCLUSION: Two proposed evaluation models are effective and sample imbalance should be taken into account in microarray experiment design and gene expression data analysis. The results and two proposed evaluation models can provide some help in selecting suitable method to process the unbalanced data. BioMed Central 2006-12-12 /pmc/articles/PMC1780111/ /pubmed/17217526 http://dx.doi.org/10.1186/1471-2105-7-S4-S8 Text en Copyright © 2006 Yang 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
Yang, Kun
Li, Jianzhong
Gao, Hong
The impact of sample imbalance on identifying differentially expressed genes
title The impact of sample imbalance on identifying differentially expressed genes
title_full The impact of sample imbalance on identifying differentially expressed genes
title_fullStr The impact of sample imbalance on identifying differentially expressed genes
title_full_unstemmed The impact of sample imbalance on identifying differentially expressed genes
title_short The impact of sample imbalance on identifying differentially expressed genes
title_sort impact of sample imbalance on identifying differentially expressed genes
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1780111/
https://www.ncbi.nlm.nih.gov/pubmed/17217526
http://dx.doi.org/10.1186/1471-2105-7-S4-S8
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