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Selecting Genes by Test Statistics

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic...

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Tác giả chính: Dechang Chen (Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA), Zhenqiu Liu (Bioinformatics Cell, TATRC, 110 North Market Street, Frederick, MD 21703, USA), Xiaobin Ma (Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USA), Dong Hua (Department of Computer Science, The George Washington University, 801 22nd St. NW, Washington, DC 20052, USA)
Định dạng: Other
Năm xuất bản: Hindawi Limited 2018
Chủ đề:
Technology: Chemical technology: Biotechnology
Truy cập Trực tuyến:http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/4086
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spelling oai:localhost:DHQB_123456789-40862018-10-22T08:44:34Z Selecting Genes by Test Statistics Journal of Biomedicine and Biotechnology Dechang Chen (Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA) Zhenqiu Liu (Bioinformatics Cell, TATRC, 110 North Market Street, Frederick, MD 21703, USA) Xiaobin Ma (Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USA) Dong Hua (Department of Computer Science, The George Washington University, 801 22nd St. NW, Washington, DC 20052, USA) Technology: Chemical technology: Biotechnology Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets. 2018-09-12T03:34:35Z 2018-09-12T03:34:35Z 2005 Other http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/4086 Hindawi Limited
institution Trung tâm Học liệu Đại học Quảng Bình (Dspace)
collection Trung tâm Học liệu Đại học Quảng Bình (Dspace)
topic Technology: Chemical technology: Biotechnology
spellingShingle Technology: Chemical technology: Biotechnology
Dechang Chen (Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA)
Zhenqiu Liu (Bioinformatics Cell, TATRC, 110 North Market Street, Frederick, MD 21703, USA)
Xiaobin Ma (Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USA)
Dong Hua (Department of Computer Science, The George Washington University, 801 22nd St. NW, Washington, DC 20052, USA)
Selecting Genes by Test Statistics
description Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets.
format Other
author Dechang Chen (Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA)
Zhenqiu Liu (Bioinformatics Cell, TATRC, 110 North Market Street, Frederick, MD 21703, USA)
Xiaobin Ma (Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USA)
Dong Hua (Department of Computer Science, The George Washington University, 801 22nd St. NW, Washington, DC 20052, USA)
author_facet Dechang Chen (Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA)
Zhenqiu Liu (Bioinformatics Cell, TATRC, 110 North Market Street, Frederick, MD 21703, USA)
Xiaobin Ma (Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USA)
Dong Hua (Department of Computer Science, The George Washington University, 801 22nd St. NW, Washington, DC 20052, USA)
author_sort Dechang Chen (Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Road, Bethesda, MD 20814, USA)
title Selecting Genes by Test Statistics
title_short Selecting Genes by Test Statistics
title_full Selecting Genes by Test Statistics
title_fullStr Selecting Genes by Test Statistics
title_full_unstemmed Selecting Genes by Test Statistics
title_sort selecting genes by test statistics
publisher Hindawi Limited
publishDate 2018
url http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/4086
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score 9,463379

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