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Experiment Specific Expression Patterns

  • Conference paper
Research in Computational Molecular Biology (RECOMB 2011)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6577))

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Abstract

The differential analysis of genes between microarrays from several experimental conditions or treatments routinely estimates which genes change significantly between groups. As genes are never regulated individually observed behavior may be a consequence of changes in other genes. Existing approaches like co-expression analysis aim to resolve such patterns from a wide range of experiments. The knowledge of such a background set of experiments can be used to compute expected gene behavior based on known links. It is particularly interesting to detect previously unseen specific effects in other experiments. Here, a new method to spot genes deviating from expected behavior (PAttern DEviation SCOring – Padesco ) is devised. It uses linear regression models learned from a background set to arrive at gene specific prediction accuracy distributions. For a given experiment it is then decided whether each gene is predicted better or worse than expected. This provides a novel way to estimate the experiment specificityof each gene. We propose a validation procedure to estimate the detection of such specific candidates and show that these can be identified with an average accuracy of about 85 percent.

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Author information

Authors and Affiliations

  1. Department of Informatics, LMU Munich, Amalienstr. 17, 80333, Munich, Germany

    Tobias Petri, Robert Küffner & Ralf Zimmer

Authors
  1. Tobias Petri
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  2. Robert Küffner
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  3. Ralf Zimmer
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Editors and Affiliations

  1. EBU3b, University of California San Diego, #4218, 9500 Gilman Drive, 92093-0404, La Jolla, CA, USA

    Vineet Bafna

  2. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    S. Cenk Sahinalp

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Petri, T., Küffner, R., Zimmer, R. (2011). Experiment Specific Expression Patterns. In: Bafna, V., Sahinalp, S.C. (eds) Research in Computational Molecular Biology. RECOMB 2011. Lecture Notes in Computer Science(), vol 6577. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20036-6_32

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  • DOI: https://doi.org/10.1007/978-3-642-20036-6_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20035-9

  • Online ISBN: 978-3-642-20036-6

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