Clustering genomic words in human DNA using peaks and trends of distributions
Ana Helena Tavares (),
Peter Rousseeuw (),
Paula Brito and
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Ana Helena Tavares: University of Aveiro
Jakob Raymaekers: KU Leuven
Paula Brito: University of Porto
Vera Afreixo: University of Aveiro
Advances in Data Analysis and Classification, 2020, vol. 14, issue 1, No 4, 57-76
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.
Keywords: Classification; Pattern recognition; Robustness; Word distances; 62H30; 62P10 (search for similar items in EconPapers)
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