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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Updated: Jul 1, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

Novel Spatial-Structural-Zero-Aware Dissimilarity Measures for Subtype Discovery Using Single Cell Hi-C Data.

Yongqi Liu, Victor Jin, Shili Lin

Biorxiv : the Preprint Server for Biology

|August 8, 2025

Summary

This summary is machine-generated.

A new metric, structural-zeros-aware Kendall's tau (szKendall), improves analysis of single-cell Hi-C data by distinguishing true structural zeros from sequencing dropouts. This enhances understanding of cell-to-cell variability in genome organization.

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Last Updated: Jul 1, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

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Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

Area of Science:

Genomics
Computational Biology
Biophysics

Background:

High-throughput single-cell Hi-C (scHi-C) enables studying 3D genome organization variability.
scHi-C data is often sparse, posing analytical challenges.
Existing methods struggle to differentiate structural zeros (SZs) from dropouts (DOs).

Purpose of the Study:

To develop a novel dissimilarity metric for scHi-C data analysis.
To address the limitations of current methods in handling data sparsity and zero types.
To improve the capture of cell-to-cell variability in 3D genome structure.

Main Methods:

Introduction of structural-zeros-aware Kendall's tau (szKendall).
szKendall incorporates 2D contact matrix structure and shared SZs.
Evaluation through simulations and real scHi-C datasets.

Main Results:

szKendall effectively captures key structural features in scHi-C data.
The novel metric demonstrates superior performance in cell clustering.
Distinguishing SZs from DOs is crucial for accurate analysis.

Conclusions:

szKendall offers a more accurate approach to scHi-C data dissimilarity.
This method advances the analysis of single-cell 3D genomics.
SZ-aware metrics are vital for understanding cell-specific genome organization.