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Updated: Jun 28, 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 7, 2010

szKendall: spatial-structural-zero-aware dissimilarity measures for subtype discovery using single cell Hi-C data.

Yongqi Liu¹, Sang Wan Lee¹, Taeyeon Kim¹

¹Department of Statistics, The Ohio State University, Columbus, OH, USA.

Nature Communications

|April 23, 2026

Summary

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New szKendall method improves analysis of single-cell Hi-C data by accounting for structural zeros, enhancing cell clustering and understanding genome organization variability.

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

High-throughput single-cell Hi-C provides insights into genome 3D organization variability.
Data sparsity in single-cell Hi-C presents interpretation challenges, with zeros representing structural absences or dropouts.
Existing dissimilarity measures inadequately distinguish between types of zeros, hindering biological interpretation.

Purpose of the Study:

To develop a novel dissimilarity measure that accounts for structural zeros in single-cell Hi-C data.
To improve the analysis of cell-to-cell variability in genome 3D organization.
To enhance the accuracy of cell clustering based on genomic structural features.

Main Methods:

Introduction of structural-zero-aware Kendall's tau (szKendall) metric.

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Related Experiment Videos

Last Updated: Jun 28, 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 7, 2010

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Mapping Mammalian 3D Genome Interactions with Micro-C-XL

Mapping Mammalian 3D Genome Interactions with Micro-C-XL

Published on: November 3, 2023

Leveraging spatial information in 2D contact maps.

Utilizing concordance of shared structural zeros across cells.

Main Results:

szKendall demonstrates improved capture of structural genomic features.
Enhanced performance in cell clustering compared to traditional methods like Euclidean distance and standard Kendall's tau.
szKendall effectively differentiates between structural zeros and dropouts.

Conclusions:

Structural-zero-aware dissimilarity measures are crucial for robust inference from single-cell Hi-C data.
szKendall offers a principled foundation for analyzing genome 3D organization.
The method advances the interpretation of cell-to-cell variability in chromatin architecture.