HiCcompare: a method for joint normalization of Hi-C datasets and differential chromatin interaction detection

Abstract

Changes in spatial chromatin interactions are now emerging as a unifying mechanism orchestrating regulation of gene expression. Evolution of chromatin conformation capture methods into Hi-C sequencing technology now allows an insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence-and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering comparative analysis of chromatin interactions. We developed a simple and effective method HiCcompare for the joint normalization and differential analysis of multiple Hi-C datasets. The method avoids constraining Hi-C data within a rigid statistical model, allowing a data-driven normalization of biases using locally weighted linear regression (loess). The method identifies region-specific chromatin interaction changes complementary to changes due to large-scale genomic rearrangements, such as copy number variants (CNVs). HiCcompare outperforms methods for normalizing individual Hi-C datasets in detecting a priori known chromatin interaction differences in simulated and real-life settings while detecting biologically relevant changes. HiCcompare is freely available as a Bioconductor R package https://bioconductor.org/packages/HiCcompare/.

Selfish: Discovery of Differential Chromatin Interactions via a Self-Similarity Measure

“…In this work, we present new comparative methods for the analysis of Hi-C data based on the notion of self-similarity (Shechtman and Irani, 2007). We show that our self-similarity measure is robust to biases and does not need complex and computationally intensive normalization steps, such as MA (Dudoit et al, 2002) or MD (Stansfield and Dozmorov, 2017). In the first part of the paper we show that our self-similarity measure can be used in the first application domain described above, i.e., as a tool to quantify the reproducibility of Hi-C biological/technical replicate experiments.…”

“…However, the comparative analysis of Hi-C data presents computational and analytical challenges which are due to technology-driven and sequencespecific biases. Technology-driven biases include sequencing depth, cross-linking conditions, circularization length, and restriction enzyme sites length (O'Sullivan et al, 2013;Cournac et al, 2012;Stansfield and Dozmorov, 2017). Sequence-specific biases include GC content of trimmed ligation junctions, sequence uniqueness, and nucleotide composition (Yaffe and Tanay, 2011).…”