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Quantitative Biology > Genomics

arXiv:2101.10645 (q-bio)
[Submitted on 26 Jan 2021]

Title:essHi-C: Essential component analysis of Hi-C matrices

Authors:Stefano Franzini, Marco Di Stefano, Cristian Micheletti
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Abstract:Motivation: Hi-C matrices are cornerstones for qualitative and quantitative studies of genome folding, from its territorial organization to compartments and topological domains. The high dynamic range of genomic distances probed in Hi-C assays reflects in an inherent stochastic background of the interactions matrices, which inevitably convolve the features of interest with largely aspecific ones. Results: Here we introduce a discuss essHi-C, a method to isolate the specific, or essential component of Hi-C matrices from the aspecific portion of the spectrum that is compatible with random matrices. Systematic comparisons show that essHi-C improves the clarity of the interaction patterns, enhances the robustness against sequencing depth, allows the unsupervised clustering of experiments in different cell lines and recovers the cell-cycle phasing of single-cells based on Hi-C data. Thus, essHi-C provides means for isolating significant biological and physical features from Hi-C matrices.
Comments: 14 pages, 4 figures. This is the Authors' Original Version of the article, which has been accepted for publication in Bioinformatics published by Oxford University Press
Subjects: Genomics (q-bio.GN); Biomolecules (q-bio.BM); Applications (stat.AP)
Cite as: arXiv:2101.10645 [q-bio.GN]
  (or arXiv:2101.10645v1 [q-bio.GN] for this version)
  https://doi.org/10.48550/arXiv.2101.10645

Submission history

From: Marco Di Stefano [view email]
[v1] Tue, 26 Jan 2021 09:17:29 UTC (5,639 KB)
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