Sparse linear discriminant analysis for simultaneous testing for the significance of a gene set/pathway and gene selection
Motivation: Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all t...
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| Published in | Bioinformatics Vol. 25; no. 9; pp. 1145 - 1151 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Oxford University Press
01.05.2009
Oxford Publishing Limited (England) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1460-2059 1367-4811 |
| DOI | 10.1093/bioinformatics/btp019 |
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| Abstract | Motivation: Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway. Methods: We develop a unified framework to jointly test the significance of a pathway and to select a subset of genes that drive the significant pathway effect. To achieve dimension reduction and gene selection, we decompose each gene pathway into a single score by using a regularized form of linear discriminant analysis, called sparse linear discriminant analysis (sLDA). Testing for the significance of the pathway effect proceeds via permutation of the sLDA score. The sLDA-based test is compared with competing approaches with simulations and two applications: a study on the effect of metal fume exposure on immune response and a study of gene expression profiles among Type II Diabetes patients. Results: Our results show that sLDA-based testing provides a powerful approach to test for the significance of a differentially expressed pathway and gene selection. Availability: An implementation of the proposed sLDA-based pathway test in the R statistical computing environment is available at http://www.hsph.harvard.edu/∼mwu/software/ Contact: xlin@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
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| AbstractList | Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway.MOTIVATIONPathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway.We develop a unified framework to jointly test the significance of a pathway and to select a subset of genes that drive the significant pathway effect. To achieve dimension reduction and gene selection, we decompose each gene pathway into a single score by using a regularized form of linear discriminant analysis, called sparse linear discriminant analysis (sLDA). Testing for the significance of the pathway effect proceeds via permutation of the sLDA score. The sLDA-based test is compared with competing approaches with simulations and two applications: a study on the effect of metal fume exposure on immune response and a study of gene expression profiles among Type II Diabetes patients.METHODSWe develop a unified framework to jointly test the significance of a pathway and to select a subset of genes that drive the significant pathway effect. To achieve dimension reduction and gene selection, we decompose each gene pathway into a single score by using a regularized form of linear discriminant analysis, called sparse linear discriminant analysis (sLDA). Testing for the significance of the pathway effect proceeds via permutation of the sLDA score. The sLDA-based test is compared with competing approaches with simulations and two applications: a study on the effect of metal fume exposure on immune response and a study of gene expression profiles among Type II Diabetes patients.Our results show that sLDA-based testing provides a powerful approach to test for the significance of a differentially expressed pathway and gene selection.RESULTSOur results show that sLDA-based testing provides a powerful approach to test for the significance of a differentially expressed pathway and gene selection.An implementation of the proposed sLDA-based pathway test in the R statistical computing environment is available at http://www.hsph.harvard.edu/~mwu/software/.AVAILABILITYAn implementation of the proposed sLDA-based pathway test in the R statistical computing environment is available at http://www.hsph.harvard.edu/~mwu/software/.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. Motivation: Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway.Methods: We develop a unified framework to jointly test the significance of a pathway and to select a subset of genes that drive the significant pathway effect. To achieve dimension reduction and gene selection, we decompose each gene pathway into a single score by using a regularized form of linear discriminant analysis, called sparse linear discriminant analysis (sLDA). Testing for the significance of the pathway effect proceeds via permutation of the sLDA score. The sLDA-based test is compared with competing approaches with simulations and two applications: a study on the effect of metal fume exposure on immune response and a study of gene expression profiles among Type II Diabetes patients.Results: Our results show that sLDA-based testing provides a powerful approach to test for the significance of a differentially expressed pathway and gene selection.Availability: An implementation of the proposed sLDA-based pathway test in the R statistical computing environment is available at http://www.hsph.harvard.edu/ similar to mwu/software/ Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway. Methods: We develop a unified framework to jointly test the significance of a pathway and to select a subset of genes that drive the significant pathway effect. To achieve dimension reduction and gene selection, we decompose each gene pathway into a single score by using a regularized form of linear discriminant analysis, called sparse linear discriminant analysis (sLDA). Testing for the significance of the pathway effect proceeds via permutation of the sLDA score. The sLDA-based test is compared with competing approaches with simulations and two applications: a study on the effect of metal fume exposure on immune response and a study of gene expression profiles among Type II Diabetes patients. Results: Our results show that sLDA-based testing provides a powerful approach to test for the significance of a differentially expressed pathway and gene selection. Availability: An implementation of the proposed sLDA-based pathway test in the R statistical computing environment is available at http://www.hsph.harvard.edu/∼mwu/software/ Contact: xlin@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online. Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway. We develop a unified framework to jointly test the significance of a pathway and to select a subset of genes that drive the significant pathway effect. To achieve dimension reduction and gene selection, we decompose each gene pathway into a single score by using a regularized form of linear discriminant analysis, called sparse linear discriminant analysis (sLDA). Testing for the significance of the pathway effect proceeds via permutation of the sLDA score. The sLDA-based test is compared with competing approaches with simulations and two applications: a study on the effect of metal fume exposure on immune response and a study of gene expression profiles among Type II Diabetes patients. Our results show that sLDA-based testing provides a powerful approach to test for the significance of a differentially expressed pathway and gene selection. An implementation of the proposed sLDA-based pathway test in the R statistical computing environment is available at http://www.hsph.harvard.edu/~mwu/software/. Supplementary data are available at Bioinformatics online. Motivation: Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway. Methods: We develop a unified framework to jointly test the significance of a pathway and to select a subset of genes that drive the significant pathway effect. To achieve dimension reduction and gene selection, we decompose each gene pathway into a single score by using a regularized form of linear discriminant analysis, called sparse linear discriminant analysis (sLDA). Testing for the significance of the pathway effect proceeds via permutation of the sLDA score. The sLDA-based test is compared with competing approaches with simulations and two applications: a study on the effect of metal fume exposure on immune response and a study of gene expression profiles among Type II Diabetes patients. Results: Our results show that sLDA-based testing provides a powerful approach to test for the significance of a differentially expressed pathway and gene selection. Availability: An implementation of the proposed sLDA-based pathway test in the R statistical computing environment is available at http://www.hsph.harvard.edu/∼mwu/software/ Contact: xlin@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway. Methods: We develop a unified framework to jointly test the significance of a pathway and to select a subset of genes that drive the significant pathway effect. To achieve dimension reduction and gene selection, we decompose each gene pathway into a single score by using a regularized form of linear discriminant analysis, called sparse linear discriminant analysis (sLDA). Testing for the significance of the pathway effect proceeds via permutation of the sLDA score. The sLDA-based test is compared with competing approaches with simulations and two applications: a study on the effect of metal fume exposure on immune response and a study of gene expression profiles among Type II Diabetes patients. Results: Our results show that sLDA-based testing provides a powerful approach to test for the significance of a differentially expressed pathway and gene selection. Availability: An implementation of the proposed sLDA-based pathway test in the R statistical computing environment is available at http://www.hsph.harvard.edu/∼mwu/software/ Contact: xlin@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems associated with individual gene analysis. Since most genes are not differently expressed, existing gene set tests, which consider all the genes within a gene set, are subject to considerable noise and power loss, a concern exacerbated in studies in which the degree of differential expression is moderate for truly differentially expressed genes. For a significantly differentially expressed pathway, it is also of substantial interest to select important genes that drive the differential expression of the pathway. Methods: We develop a unified framework to jointly test the significance of a pathway and to select a subset of genes that drive the significant pathway effect. To achieve dimension reduction and gene selection, we decompose each gene pathway into a single score by using a regularized form of linear discriminant analysis, called sparse linear discriminant analysis (sLDA). Testing for the significance of the pathway effect proceeds via permutation of the sLDA score. The sLDA-based test is compared with competing approaches with simulations and two applications: a study on the effect of metal fume exposure on immune response and a study of gene expression profiles among Type II Diabetes patients. Results: Our results show that sLDA-based testing provides a powerful approach to test for the significance of a differentially expressed pathway and gene selection. Availability: An implementation of the proposed sLDA-based pathway test in the R statistical computing environment is available at http://www.hsph.harvard.edu/∼mwu/software/ Contact: xlin@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
| Author | Wang, Zhaoxi Christiani, David C. Wu, Michael C. Zhang, Lingsong Lin, Xihong |
| AuthorAffiliation | 1 Department of Biostatistics and 2 Department of Environmental Health, Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115, USA |
| AuthorAffiliation_xml | – name: 1 Department of Biostatistics and 2 Department of Environmental Health, Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115, USA |
| Author_xml | – sequence: 1 givenname: Michael C. surname: Wu fullname: Wu, Michael C. organization: Department of Biostatistics and Department of Environmental Health, Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115, USA – sequence: 2 givenname: Lingsong surname: Zhang fullname: Zhang, Lingsong organization: Department of Biostatistics and Department of Environmental Health, Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115, USA – sequence: 3 givenname: Zhaoxi surname: Wang fullname: Wang, Zhaoxi organization: Department of Biostatistics and Department of Environmental Health, Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115, USA – sequence: 4 givenname: David C. surname: Christiani fullname: Christiani, David C. organization: Department of Biostatistics and Department of Environmental Health, Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115, USA – sequence: 5 givenname: Xihong surname: Lin fullname: Lin, Xihong organization: Department of Biostatistics and Department of Environmental Health, Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115, USA |
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| Snippet | Motivation: Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing... Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing problems... Motivation: Pathway and gene set-based approaches for the analysis of gene expression profiling experiments have become increasingly popular for addressing... |
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| SubjectTerms | Algorithms Bioinformatics Biological and medical sciences Databases, Protein Diabetes Mellitus - genetics Discriminant Analysis Fundamental and applied biological sciences. Psychology Gene Expression Profiling - methods General aspects Humans Immune response Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Original Papers |
| Title | Sparse linear discriminant analysis for simultaneous testing for the significance of a gene set/pathway and gene selection |
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