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

JOURNAL OF MULTIVARIATE ANALYSIS

卷:165

Article

Brault, Vincent¹  Ouadah, Sarah²  Sansonnet, Laure²  Levy-Leduc, Celine² 

[1] Univ Grenoble Alpes, CNRS, LJK, F-38000 Grenoble, France

[2] Univ Paris Saclay, AgroParisTech, UMR MIA Paris, INRA, Paris, France

关键词: Hi-C data;    Multiple change-point estimation;    Nonparametric estimation;   

DOI  :  10.1016/j.jmva.2017.12.005

来源: Elsevier

【 摘 要 】

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. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free