ArticleBriefings in bioinformatics2021
Are dropout imputation methods for scRNA-seq effective for scHi-C data?
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- ssJSD: A Fusion of Sparsity and Spatial Information for HiC Single-Cell Clustering.bioRxiv : the preprint server for biology · 2026Article
- szKendall: spatial-structural-zero-aware dissimilarity measures for subtype discovery using single cell Hi-C data.Nature communications · 2026Article
- Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?Briefings in bioinformatics · 2026Article
- scHiCSRS: a self-representation smoothing method with Gaussian mixture model for imputing single cell Hi-C data.BMC bioinformatics · 2025Article
- Topologically associating domains of chromatin on single-cell Hi-C data: a survey of bioinformatic tools and applications in the light of artificial intelligence.Frontiers in genetics · 2025Review
- [Advances in methods and applications of single-cell Hi-C data analysis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- scHi-CSim: a flexible simulator that generates high-fidelity single-cell Hi-C data for benchmarking.Journal of molecular cell biology · 2023Article
- HiCImpute: A Bayesian hierarchical model for identifying structural zeros and enhancing single cell Hi-C data.PLoS computational biology · 2022Article
- Article
- Single-cell Hi-C data analysis: safety in numbers.Briefings in bioinformatics · 2021Article
- ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation.Frontiers in genetics · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
The prevalence of dropout events is a serious problem for single-cell Hi-C (scHiC) data due to insufficient sequencing depth and data coverage, which brings difficulties in downstream studies such as clustering and structural analysis. Complicating things further is the fact that dropouts are confounded with structural zeros due to underlying properties, leading to observed zeros being a mixture of both types of events. Although a great deal of progress has been made in imputing dropout events for single cell RNA-sequencing (RNA-seq) data, little has been done in identifying structural zeros and imputing dropouts for scHiC data. In this paper, we adapted several methods from the single-cell RNA-seq literature for inference on observed zeros in scHiC data and evaluated their effectiveness. Through an extensive simulation study and real data analysis, we have shown that a couple of the adapted single-cell RNA-seq algorithms can be powerful for correctly identifying structural zeros and accurately imputing dropout values. Downstream analysis using the imputed values showed considerable improvement for clustering cells of the same types together over clustering results before imputation.
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Registered trials
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