Advertisement

Progression Reconstruction from Unsynchronized Biological Data using Cluster Spanning Trees

  • Ryan Eshleman
  • Rahul SinghEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Span Tree Minimum Span Tree Reconstruction Error Natural Cluster Phenotypic Screening 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

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

References

  1. 1.
    Amenta, N., Bern, M., Eppstein, D.: The crust and the Β-skeleton: combinatorial curve reconstruction. Graph. Models Image Process. 60(2), 125–135 (1998)CrossRefGoogle Scholar
  2. 2.
    Hastie, T., Stuetzle, W.: Principal curves. J. Am. Stat. Assoc. 84(406), 502–516 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Aigner, M., Ziegler, G.M., Erdos, P.: Proofs from THE BOOK, vol. 274. Springer, Berlin (2010)CrossRefzbMATHGoogle Scholar
  4. 4.
    Kruskal, B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proc. Am. Math. Soc. 7(1), 48–50 (1956)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Boruvka, O.: Contribution to the solution of a problem of economical construction of electrical networks. Elektronický Obzor 15, 153–154 (1926)Google Scholar
  6. 6.
    Sokal, R.R.: A statistical method for evaluating systematic relationships. Univ Kans Sci Bull. 38, 1409–1438 (1958)Google Scholar
  7. 7.
    Székely, G.J., Rizzo, M.L.: Hierarchical clustering via joint between-within distances: extending ward’s minimum variance method. J. Classif. 22, 151–183 (2005)CrossRefGoogle Scholar
  8. 8.
    Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Magwene, P.M., Lizardi, P., Kim, J.: Reconstructing the temporal ordering of biological samples using microarray data. Bioinformatics 19(7), 842–850 (2003)CrossRefGoogle Scholar
  10. 10.
    Qiu, P., Gentles, A.J., Plevritis, S.K.: Discovering biological progression underlying microarray samples. PLoS Comput. Biol. 7, 4 (2011)CrossRefGoogle Scholar
  11. 11.
    Bochner, B.R.: Global phenotypic characterization of bacteria. FEMS microbiology Rev. 33(1), 191–205 (2009)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Whitfield, M.L., et al.: Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13(6), 1977–2000 (2002)CrossRefGoogle Scholar
  13. 13.
    Park, Y., Shackney, S., Schwartz, R.: Network-based inference of cancer progression from microarray data. IEEE/ACM Trans. Comput. Biol. Bioinform. 6(2), 200–212 (2009)CrossRefGoogle Scholar
  14. 14.
    Arreola, L.R., Long, T., Asarnow, D., Suzuki, B.M., Singh, R., Caffrey, C.: Chemical and genetic validation of the Statin drug target for the potential treatment of the Helminth disease. Schistosomiasis PLoS One 9, 1 (2014)Google Scholar
  15. 15.
    Fitch, W.M.: Toward defining the course of evolution: minimum change for a specific tree topology. Syst. Zool. 20, 406–416 (1971)CrossRefGoogle Scholar
  16. 16.
    1000 Genomes Project Consortium.: A map of human genome variation from population-scale sequencing. Nature 467(7319), 1061–1073 (2010)Google Scholar
  17. 17.
    Behrends, S., Vehse, K., Scholz, H., Bullerdiek, J., Kazmierczak, B.: Assignment of GUCY1A3, a candidate gene for hypertension, to human chromosome bands 4q31. 1 → q31. 2 by in situ hybridization. Cytogenet. Genome Res. 88(3–4), 204–205 (2000)CrossRefGoogle Scholar
  18. 18.
    Yasuda, K., et al.: Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat. Genet. 40(9), 1092–1097 (2008)CrossRefGoogle Scholar
  19. 19.
    Platt, O.S., et al.: Pain in sickle cell disease: rates and risk factors. N. Engl. J. Med. 325(1), 11–16 (1991)CrossRefGoogle Scholar
  20. 20.
    Allison, A.C.: Protection afforded by sickle-cell trait against subtertian malarial infection. Br. Med. J. 1(4857), 290–294 (1954)CrossRefGoogle Scholar
  21. 21.
    Ehret, G.B., et al.: Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478(7367), 103–109 (2011)CrossRefGoogle Scholar
  22. 22.
    Merrill, G.F.: Cell synchronization. Methods Cell Biol. 57, 229–249 (1988)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceSan Francisco State UniversitySan FranciscoUSA

Personalised recommendations