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  Introducing knowledge into differential expression analysis

Szczurek, E., Biecek, P., Tiuryn, J., & Vingron, M. (2010). Introducing knowledge into differential expression analysis. Journal of Computational Biology, 17(8), 953-967. doi:10.1089/cmb.2010.0034.

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Genre: Zeitschriftenartikel
Alternativer Titel : J Comput Biol

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Szczurek, E.1, Autor           
Biecek, P., Autor
Tiuryn, J., Autor
Vingron, M.2, Autor           
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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 Zusammenfassung: Gene expression measurements allow determining sets of up- or down-regulated, or unchanged genes in a particular experimental condition. Additional biological knowledge can suggest examples of genes from one of these sets. For instance, known target genes of a transcriptional activator are expected, but are not certain to go down after this activator is knocked out. Available differential expression analysis tools do not take such imprecise examples into account. Here we put forward a novel partially supervised mixture modeling methodology for differential expression analysis. Our approach, guided by imprecise examples, clusters expression data into differentially expressed and unchanged genes. The partially supervised methodology is implemented by two methods: a newly introduced belief-based mixture modeling, and soft-label mixture modeling, a method proved efficient in other applications. We investigate on synthetic data the input example settings favorable for each method. In our tests, both belief-based and soft-label methods prove their advantage over semi-supervised mixture modeling in correcting for erroneous examples. We also compare them to alternative differential expression analysis approaches, showing that incorporation of knowledge yields better performance. We present a broad range of knowledge sources and data to which our partially supervised methodology can be applied. First, we determine targets of Ste12 based on yeast knockout data, guided by a Ste12 DNA-binding experiment. Second, we distinguish miR-1 from miR-124 targets in human by clustering expression data under transfection experiments of both microRNAs, using their computationally predicted targets as examples. Finally, we utilize literature knowledge to improve clustering of time-course expression profiles.

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Sprache(n): eng - English
 Datum: 2010-08
 Publikationsstatus: Erschienen
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Titel: Journal of Computational Biology
  Alternativer Titel : J Comput Biol
Genre der Quelle: Zeitschrift
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Seiten: - Band / Heft: 17 (8) Artikelnummer: - Start- / Endseite: 953 - 967 Identifikator: ISSN: 1557-8666 (Electronic) 1066-5277 (Linking) %R 10.1089/cmb.2010.0034