Clustering genomic words in human DNA using peaks and trends of distributions
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
Cite
@article{arxiv.1808.04278,
title = {Clustering genomic words in human DNA using peaks and trends of distributions},
author = {Ana Helena Tavares and Jakob Raymaekers and Peter J. Rousseeuw and Paula Brito and Vera Afreixo},
journal= {arXiv preprint arXiv:1808.04278},
year = {2021}
}