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

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.
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Vincent Brault, Sarah Ouadah, Laure Sansonnet, Céline Lévy-Leduc. Nonparametric multiple change-point estimation for analyzing large Hi-C data matrices. Journal of Multivariate Analysis, Elsevier, 2018, 165, pp.143-165. ⟨http://www.sciencedirect.com/science/article/pii/S0047259X17307753⟩. ⟨10.1016/j.jmva.2017.12.005⟩. ⟨hal-01468198v1⟩

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