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

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

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.

Suggested Citation

  • Brault, Vincent & Ouadah, Sarah & Sansonnet, Laure & Lévy-Leduc, Céline, 2018. "Nonparametric multiple change-point estimation for analyzing large Hi-C data matrices," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 143-165.
    • Handle: RePEc:eee:jmvana:v:165:y:2018:i:c:p:143-165
    DOI: 10.1016/j.jmva.2017.12.005
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    References listed on IDEAS

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    1. Jesse R. Dixon & Siddarth Selvaraj & Feng Yue & Audrey Kim & Yan Li & Yin Shen & Ming Hu & Jun S. Liu & Bing Ren, 2012. "Topological domains in mammalian genomes identified by analysis of chromatin interactions," Nature, Nature, vol. 485(7398), pages 376-380, May.
    2. Vincent Brault & Maud Delattre & Emilie Lebarbier & Tristan Mary-Huard & Céline Lévy-Leduc, 2017. "Estimating the Number of Block Boundaries from Diagonal Blockwise Matrices Without Penalization," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(2), pages 563-580, June.
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    4. Cho, Haeran & Fryzlewicz, Piotr, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," LSE Research Online Documents on Economics 57147, London School of Economics and Political Science, LSE Library.
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