Author(s): Tavares, Ana Helena ; Raymaekers, Jakob ; Rousseeuw, Peter J. ; Brito, Paula ; Afreixo, Vera
Date: 2020
Persistent ID: http://hdl.handle.net/10773/30267
Origin: RIA - Repositório Institucional da Universidade de Aveiro
Subject(s): Classification; Pattern recognition; Robustness; Word distances
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.
