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

Abstract

Motivation: High-throughput conformation capture experiments such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps is critical in evaluating the reproducibility of replicate experiments and identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging. Results: We present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.

FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data

“…A central task in Hi-C matrix analysis is the comparison of multiple datasets [41,[60][61][62][63]. FAN-C can systematically identify differences at all scales of the chromatin organisation hierarchy.…”

HiC-DC+: systematic 3D interaction calls and differential analysis for Hi-C and HiChIP

“…The loops were then filtered at FDR < 0.05 and absolute fold change > 1.5. We performed the differential contact calling on Hi-C data in HAP1 and WAPL knockout HAP1 cells at 25 kb resolution using mutliHiCcompare 15 , Selfish 16 , and diffHiC 14 . All differential interactions were filtered at FDR < 0.05 and absolute fold change > 1.5.…”

“…To benchmark our differential HiC interactions against diffHiC 14 , multiHiCcompare 15 , and Selfish 16 , we ran HiC-DC+ and other tools on Hi-C data in HAP1 and WAPL knockout HAP1 cells at 25 kb resolution 17 . Haarhuis et al showed that removing WAPL affects chromosome topology on a global scale through the formation of longer loops and strongly increased interaction frequencies between nearby TADs 17 .…”

Detecting local changes in chromatin architecture with false discovery control

“…Computationally generated Hi-C data from these polymer simulations provide a unique means to test methods designed for Hi-C data analysis, as these simulated data resemble real Hi-C data, yet are unmarred by experimental biases, and their ground truth is known. We compared locdiffr, testing with both wFDR and wFDX, to existing tools multiHiCcompare 48 , selfish 49 , TopDom 17 , and edgeR 50 . MultiHiCcompare and selfish were designed specifically for differential analysis of Hi-C data, while TopDom calls TADs in a single experiment, and edgeR is typically used for differential gene expression analysis.…”