ArticleJournal of molecular cell biology2023
scHi-CSim: a flexible simulator that generates high-fidelity single-cell Hi-C data for benchmarking.
Article in Journal of molecular cell biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- szKendall: spatial-structural-zero-aware dissimilarity measures for subtype discovery using single cell Hi-C data.Nature communications · 2026Article
- scDIAGRAM: detecting chromatin compartments from individual single-cell Hi-C matrix without imputation or reference features.Briefings in bioinformatics · 2026Article
- Deciphering chromatin domain, domain community and chromunity for 3D genome maps with Mactop.Communications biology · 2025Article
- Single-Cell Hi-C Technologies and Computational Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Deciphering single-cell genomic architecture: insights into cellular heterogeneity and regulatory dynamics.Genomics & informatics · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell Hi-C technology provides an unprecedented opportunity to reveal chromatin structure in individual cells. However, high sequencing cost impedes the generation of biological Hi-C data with high sequencing depths and multiple replicates for downstream analysis. Here, we developed a single-cell Hi-C simulator (scHi-CSim) that generates high-fidelity data for benchmarking. scHi-CSim merges neighboring cells to overcome the sparseness of data, samples interactions in distance-stratified chromosomes to maintain the heterogeneity of single cells, and estimates the empirical distribution of restriction fragments to generate simulated data. We demonstrated that scHi-CSim can generate high-fidelity data by comparing the performance of single-cell clustering and detection of chromosomal high-order structures with raw data. Furthermore, scHi-CSim is flexible to change sequencing depth and the number of simulated replicates. We showed that increasing sequencing depth could improve the accuracy of detecting topologically associating domains. We also used scHi-CSim to generate a series of simulated datasets with different sequencing depths to benchmark scHi-C clustering methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.