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HiCDiff: single-cell Hi-C data denoising with diffusion models.

Yanli Wang¹, Jianlin Cheng¹

¹Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, United States.

Briefings in Bioinformatics

|June 10, 2024

Summary

This summary is machine-generated.

We developed HiCDiff, a novel generative diffusion model, to effectively denoise single-cell Hi-C data. This method enhances the analysis of chromosomal contact matrices from individual cells, improving genomic research accuracy.

Keywords:

Hi-C data denoising deep learning diffusion model single-cell Hi-C

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Area of Science:

Genomics
Computational Biology
Biotechnology

Background:

Single-cell Hi-C (ScHi-C) is a powerful technique for studying genome conformation in individual cells.
ScHi-C data is characterized by sparsity and noise, hindering its application and analysis.
Existing methods struggle to effectively denoise ScHi-C data.

Purpose of the Study:

To develop a novel computational method for denoising single-cell Hi-C data.
To introduce the first generative diffusion model, HiCDiff, for processing chromosomal contact matrices.
To evaluate the performance of HiCDiff on various ScHi-C datasets.

Main Methods:

Development of HiCDiff, a generative diffusion model utilizing a deep residual network.
Training HiCDiff in both unsupervised and supervised learning modes.
Benchmarking HiCDiff against existing non-diffusion and state-of-the-art deep learning methods.

Main Results:

HiCDiff substantially reduces noise in single-cell Hi-C data.
Unsupervised HiCDiff outperforms most supervised non-diffusion methods.
HiCDiff achieves performance comparable to state-of-the-art supervised methods.
The model also demonstrates effectiveness in denoising bulk Hi-C data.

Conclusions:

Generative diffusion models, like HiCDiff, represent a valuable approach for denoising ScHi-C data.
HiCDiff enhances the utility and reliability of single-cell Hi-C data for biological research.
The developed method shows promise for both single-cell and bulk Hi-C data analysis.