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International Symposium on Bioinformatics Research and Applications

ISBRA 2016: Bioinformatics Research and Applications pp 136–147Cite as

Progression Reconstruction from Unsynchronized Biological Data using Cluster Spanning Trees

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9683))

Abstract

Identifying the progression-order of an unsynchronized set of biological samples is crucial for comprehending the dynamics of the underlying molecular interactions. It is also valuable in many applied problems such as data denoising and synchronization, tumor classification and cell lineage identification. Current methods that attempt solving this problem are ultimately based either on polynomial and piece-wise approximation of the unknown generating function or its reconstruction through the use of spanning trees. Such approaches face difficulty when it is necessary to factor-in complex relationships within the data such as partial ordering or bifurcating or multifurcating progressions. We propose the notion of Cluster Spanning Trees (CST) that can model both linear as well as the aforementioned complex progression relationships in data. Through a number of experiments on synthetic data sets as well as datasets from the cell cycle, cellular differentiation, and phenotypic screening, we show that the proposed CST approach outperforms the previous approaches in reconstructing the temporal progression of the data.

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Acknowledgements

This research was funded in part by the National Science Foundation grant IIS-0644418 and the National Institutes of Health grant 1R01A1089896.

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Authors and Affiliations

  1. Department of Computer Science, San Francisco State University, San Francisco, CA, 94132, USA

    Ryan Eshleman & Rahul Singh

Authors
  1. Ryan Eshleman
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  2. Rahul Singh
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Corresponding author

Correspondence to Rahul Singh .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Georgia State University, Atlanta, Georgia, USA

    Anu Bourgeois

  2. Centre for Disease Control & Prevention, Atlanta, Georgia, USA

    Pavel Skums

  3. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Xiang Wan

  4. Georgia State University, Atlanta, Georgia, USA

    Alex Zelikovsky

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© 2016 Springer International Publishing Switzerland

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Eshleman, R., Singh, R. (2016). Progression Reconstruction from Unsynchronized Biological Data using Cluster Spanning Trees. In: Bourgeois, A., Skums, P., Wan, X., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2016. Lecture Notes in Computer Science(), vol 9683. Springer, Cham. https://doi.org/10.1007/978-3-319-38782-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-38782-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-38781-9

  • Online ISBN: 978-3-319-38782-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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