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Space Oriented Rank-Based Data Integration

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  • Lin Shili

    (The Ohio State University)

Abstract

Integration of data from multiple omics platforms has become a major challenge in studying complex systems and traits. For integrating data from multiple platforms, the underlying spaces from which the top ranked elements come from are likely to be different. Thus, taking the underlying spaces into consideration explicitly is important, as failure to do so would lead to inefficient use of data and might render biases and/or sub-optimal results. We propose two space oriented classes of heuristic algorithms for integrating ranked lists from omic scale data. These algorithms are either Borda inspired or Markov chain based that take the underlying spaces of the individual ranked lists into account explicitly. We applied this set of algorithms to a number of problems, including one that aims at aggregating results from three cDNA and two Affymetrix gene expression studies in which the underlying spaces between Affymetrix and cDNA platforms are clearly different.

Suggested Citation

  • Lin Shili, 2010. "Space Oriented Rank-Based Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-25, April.
    • Handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:20
    DOI: 10.2202/1544-6115.1534
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    References listed on IDEAS

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    1. Shili Lin & Jie Ding, 2009. "Integration of Ranked Lists via Cross Entropy Monte Carlo with Applications to mRNA and microRNA Studies," Biometrics, The International Biometric Society, vol. 65(1), pages 9-18, March.
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    Citations

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    Cited by:

    1. Giuseppe Jurman & Samantha Riccadonna & Roberto Visintainer & Cesare Furlanello, 2012. "Algebraic Comparison of Partial Lists in Bioinformatics," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-20, May.
    2. Debarshi Kumar Sanyal & Plaban Kumar Bhowmick & Partha Pratim Das & Samiran Chattopadhyay & T. Y. S. S. Santosh, 2019. "Enhancing access to scholarly publications with surrogate resources," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1129-1164, November.
    3. Švendová, Vendula & Schimek, Michael G., 2017. "A novel method for estimating the common signals for consensus across multiple ranked lists," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 122-135.
    4. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.
    5. Yoo, Yeawon & Escobedo, Adolfo R. & Skolfield, J. Kyle, 2020. "A new correlation coefficient for comparing and aggregating non-strict and incomplete rankings," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1025-1041.

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