Discovering gene expression patterns in time course microarray experiments by ANOVA–SCA
Motivation: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwant...
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| Veröffentlicht in: | Bioinformatics Jg. 23; H. 14; S. 1792 - 1800 |
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| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Oxford
Oxford University Press
15.07.2007
Oxford Publishing Limited (England) |
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| ISSN: | 1367-4803, 1367-4811, 1460-2059, 1367-4811 |
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| Abstract | Motivation: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. Results: In this work, we develop the application of the Analysis of variance–simultaneous component analysis (ANOVA–SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. Availability: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. Contact: mj.nueda@ua.es and aconesa@cipf.es Supplementary information: Supplementary data are available at Bioinformatics online. |
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| AbstractList | MOTIVATION: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. RESULTS: In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. AVAILABILITY: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. Supplementary information: Supplementary data are available at Bioinformatics online. Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account.MOTIVATIONDesigned microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account.In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets.RESULTSIn this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets.ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm.AVAILABILITYASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. Motivation: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. Results: In this work, we develop the application of the Analysis of variance–simultaneous component analysis (ANOVA–SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. Availability: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. Contact: mj.nueda@ua.es and aconesa@cipf.es Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. Results: In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics , (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes . We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. Availability: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. Contact: mj.nueda@ua.es and aconesa@cipf.es Supplementary information: Supplementary data are available at Bioinformatics online. Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. Supplementary data are available at Bioinformatics online. Motivation: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. Results: In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. Availability: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. Contact: mj.nueda@ua.es and aconesa@cipf.es Supplementary information: Supplementary data are available at Bioinformatics online. |
| Author | Conesa, Ana Hoefsloot, Huub C. J. Talón, Manuel Nueda, María José Smilde, Age K. Westerhuis, Johan A. Ferrer, Alberto |
| Author_xml | – sequence: 1 givenname: María José surname: Nueda fullname: Nueda, María José organization: Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113, Moncada, Spain, Biosystems Data Analysis, University of Amsterdam, Nieuwe Achtergracht 166, 1018 W V, Amsterdam, TNO Quality of life, PO Box 360 AJ Zeist, The Netherlands, Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia, Cno. Vera s/n, Edificio I-3, Apartado 46022 and Bioinformatics Department, Centro de Investigación Príncipe Felipe, Autopista del Saler, 16, E46013, Valencia, Spain – sequence: 2 givenname: Ana surname: Conesa fullname: Conesa, Ana organization: Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113, Moncada, Spain, Biosystems Data Analysis, University of Amsterdam, Nieuwe Achtergracht 166, 1018 W V, Amsterdam, TNO Quality of life, PO Box 360 AJ Zeist, The Netherlands, Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia, Cno. Vera s/n, Edificio I-3, Apartado 46022 and Bioinformatics Department, Centro de Investigación Príncipe Felipe, Autopista del Saler, 16, E46013, Valencia, Spain – sequence: 3 givenname: Johan A. surname: Westerhuis fullname: Westerhuis, Johan A. organization: Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113, Moncada, Spain, Biosystems Data Analysis, University of Amsterdam, Nieuwe Achtergracht 166, 1018 W V, Amsterdam, TNO Quality of life, PO Box 360 AJ Zeist, The Netherlands, Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia, Cno. Vera s/n, Edificio I-3, Apartado 46022 and Bioinformatics Department, Centro de Investigación Príncipe Felipe, Autopista del Saler, 16, E46013, Valencia, Spain – sequence: 4 givenname: Huub C. J. surname: Hoefsloot fullname: Hoefsloot, Huub C. J. organization: Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113, Moncada, Spain, Biosystems Data Analysis, University of Amsterdam, Nieuwe Achtergracht 166, 1018 W V, Amsterdam, TNO Quality of life, PO Box 360 AJ Zeist, The Netherlands, Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia, Cno. Vera s/n, Edificio I-3, Apartado 46022 and Bioinformatics Department, Centro de Investigación Príncipe Felipe, Autopista del Saler, 16, E46013, Valencia, Spain – sequence: 5 givenname: Age K. surname: Smilde fullname: Smilde, Age K. organization: Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113, Moncada, Spain, Biosystems Data Analysis, University of Amsterdam, Nieuwe Achtergracht 166, 1018 W V, Amsterdam, TNO Quality of life, PO Box 360 AJ Zeist, The Netherlands, Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia, Cno. Vera s/n, Edificio I-3, Apartado 46022 and Bioinformatics Department, Centro de Investigación Príncipe Felipe, Autopista del Saler, 16, E46013, Valencia, Spain – sequence: 6 givenname: Manuel surname: Talón fullname: Talón, Manuel organization: Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113, Moncada, Spain, Biosystems Data Analysis, University of Amsterdam, Nieuwe Achtergracht 166, 1018 W V, Amsterdam, TNO Quality of life, PO Box 360 AJ Zeist, The Netherlands, Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia, Cno. Vera s/n, Edificio I-3, Apartado 46022 and Bioinformatics Department, Centro de Investigación Príncipe Felipe, Autopista del Saler, 16, E46013, Valencia, Spain – sequence: 7 givenname: Alberto surname: Ferrer fullname: Ferrer, Alberto organization: Departamento de Estadística e Investigación Operativa, Universidad de Alicante, Apartado 03080, Alicante, Centro de Genómica, Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113, Moncada, Spain, Biosystems Data Analysis, University of Amsterdam, Nieuwe Achtergracht 166, 1018 W V, Amsterdam, TNO Quality of life, PO Box 360 AJ Zeist, The Netherlands, Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia, Cno. Vera s/n, Edificio I-3, Apartado 46022 and Bioinformatics Department, Centro de Investigación Príncipe Felipe, Autopista del Saler, 16, E46013, Valencia, Spain |
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| Keywords | Transcription Noise DNA chip Synthetic test Gene expression Microarray Variance analysis Response Original document Signal Computer program Environment Extraction Bioinformatics |
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| Snippet | Motivation: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about... Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the... MOTIVATION: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about... |
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| SubjectTerms | Algorithms Analysis of Variance Bioinformatics Biological and medical sciences Computational Biology - methods Computer Simulation Data Interpretation, Statistical Fundamental and applied biological sciences. Psychology Gene Expression Profiling - methods General aspects Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis - methods Principal Component Analysis Time Factors Transcription, Genetic Variance analysis |
| Title | Discovering gene expression patterns in time course microarray experiments by ANOVA–SCA |
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