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  A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series

Stegle, O., Denby, K., Wild, D., Ghahramani, Z., & Borgwardt, K. (2009). A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series. In S. Batzoglou (Ed.), Research in Computational Molecular Biology: 13th Annual International Conference, RECOMB 2009, Tucson, AZ, USA, May 18-21, 2009 (pp. 201-216). Berlin, Germany: Springer.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C4E5-5 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-F925-C
Genre: Konferenzbeitrag

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externe Referenz:
https://link.springer.com/content/pdf/10.1007%2F978-3-642-02008-7_14.pdf (Verlagsversion)
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 Urheber:
Stegle, O, Autor           
Denby , K, Autor
Wild, Dl, Autor
Ghahramani, Z, Autor
Borgwardt, KM1, 2, Autor           
Affiliations:
1Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528696              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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Schlagwörter: -
 Zusammenfassung: Understanding the regulatory mechanisms that are responsible for an organism’s response to environmental changes is an important question in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 time points. In classification experiments our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.

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 Datum: 2009-05
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1007/978-3-642-02008-7_14
BibTex Citekey: 5665
 Art des Abschluß: -

Veranstaltung

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Titel: 13th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2009)
Veranstaltungsort: Tucson, AZ, USA
Start-/Enddatum: 2009-05-18 - 2009-05-21

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Quelle 1

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Titel: Research in Computational Molecular Biology: 13th Annual International Conference, RECOMB 2009, Tucson, AZ, USA, May 18-21, 2009
Genre der Quelle: Konferenzband
 Urheber:
Batzoglou, S, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Berlin, Germany : Springer
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 201 - 216 Identifikator: ISBN: 978-3-642-02008-7

Quelle 2

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Titel: Lecture Notes in Computer Science
Genre der Quelle: Reihe
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 5541 Artikelnummer: - Start- / Endseite: - Identifikator: -