Nonparametric multiple change-point estimation for analyzing large Hi-C data matrices

Vincent Brault, Sarah Ouadah, Laure Sansonnet and Céline Lévy-Leduc

Journal of Multivariate Analysis, 2018, vol. 165, issue C, 143-165

Abstract: We propose a novel nonparametric approach to estimate the location of block boundaries (change-points) of non-overlapping blocks in a random symmetric matrix which consists of random variables whose distribution changes from block to block. Our change-point location estimators are based on nonparametric homogeneity tests for matrices. We first provide some theoretical results for these tests. Then, we prove the consistency of our change-point location estimators. Some numerical experiments are also provided in order to support our claims. Finally, our approach is applied to Hi-C data which are used in molecular biology to study the influence of chromosomal conformation on cell function.

Keywords: Hi-C data; Multiple change-point estimation; Nonparametric estimation (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1016/j.jmva.2017.12.005

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