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Clustering genomic words in human DNA using peaks and trends of distributions

Author

Listed:
  • Ana Helena Tavares

    (University of Aveiro)

  • Jakob Raymaekers

    (KU Leuven)

  • Peter J. Rousseeuw

    (KU Leuven)

  • Paula Brito

    (University of Porto)

  • Vera Afreixo

    (University of Aveiro)

Abstract

In this work we seek clusters of genomic words in human DNA by studying their inter-word lag distributions. Due to the particularly spiked nature of these histograms, a clustering procedure is proposed that first decomposes each distribution into a baseline and a peak distribution. An outlier-robust fitting method is used to estimate the baseline distribution (the ‘trend’), and a sparse vector of detrended data captures the peak structure. A simulation study demonstrates the effectiveness of the clustering procedure in grouping distributions with similar peak behavior and/or baseline features. The procedure is applied to investigate similarities between the distribution patterns of genomic words of lengths 3 and 5 in the human genome. These experiments demonstrate the potential of the new method for identifying words with similar distance patterns.

Suggested Citation

  • Ana Helena Tavares & Jakob Raymaekers & Peter J. Rousseeuw & Paula Brito & Vera Afreixo, 2020. "Clustering genomic words in human DNA using peaks and trends of distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 57-76, March.
    • Handle: RePEc:spr:advdac:v:14:y:2020:i:1:d:10.1007_s11634-019-00362-x
    DOI: 10.1007/s11634-019-00362-x
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    References listed on IDEAS

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