Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

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

Nature communications·2026

Same author

Can random walking on a Hi-C contact matrix lead to data quality improvement? An assessment.

PloS one·2025

Same author

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

bioRxiv : the preprint server for biology·2025

Same author

Can Random Walking on a Hi-C Contact Matrix Lead to Data Quality Improvement? An Assessment.

bioRxiv : the preprint server for biology·2025

Same author

Agent-free, highly sensitive, and simply operated Ag<sup>+</sup> sensor with molybdenum disulfide-based field effect transistor.

Talanta·2024

Same author

Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations.

Nature communications·2024

Same journal

K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.

Briefings in bioinformatics·2026

Same journal

Accurate prediction of asparagine deamidation in biologics using advanced machine learning models.

Briefings in bioinformatics·2026

Same journal

scImmuneCo: a compendium of cell-type-specific functional modules for decoding immune responses from single-cell RNA-seq data.

Briefings in bioinformatics·2026

Same journal

scGenoByte: a GenoByte embedding transformer with biological priors for cell type annotation.

Briefings in bioinformatics·2026

Same journal

FerroScore: a statistical approach for quantifying tumor-related ferroptosis based on omics data.

Briefings in bioinformatics·2026

Same journal

METEOR: a data-adaptive Mendelian randomization method for powerful detection of shared and specific exposures underlying multiple outcomes.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 30, 2025

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

Are dropout imputation methods for scRNA-seq effective for scHi-C data?

Chenggong Han¹, Qing Xie¹, Shili Lin²

¹Ohio State University.

Briefings in Bioinformatics

|November 17, 2020

Summary

This summary is machine-generated.

Dropout events in single-cell Hi-C (scHiC) data hinder analysis. This study adapts single-cell RNA-seq methods to identify structural zeros and impute dropouts, improving downstream cell clustering.

Keywords:

dropout imputation single cell Hi-C single cell RNA-seq structural zero

More Related Videos

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Related Experiment Videos

Last Updated: Nov 30, 2025

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Area of Science:

Genomics
Computational Biology
Epigenetics

Background:

Dropout events are prevalent in single-cell Hi-C (scHiC) data, stemming from low sequencing depth and coverage.
These dropouts are often indistinguishable from structural zeros, complicating genomic structural analysis and cell clustering.
Existing imputation methods for single-cell RNA-sequencing (scRNA-seq) data are not well-established for scHiC data.

Purpose of the Study:

To adapt and evaluate existing single-cell RNA-sequencing (scRNA-seq) imputation algorithms for single-cell Hi-C (scHiC) data.
To differentiate between dropout events and structural zeros in scHiC data.
To improve the accuracy of downstream analyses, such as cell clustering, using imputed scHiC data.

Main Methods:

Adaptation of several computational methods from single-cell RNA-sequencing (scRNA-seq) literature.
Evaluation of adapted methods using extensive simulation studies.
Application and validation on real-world single-cell Hi-C (scHiC) datasets.

Main Results:

Identified specific single-cell RNA-sequencing (scRNA-seq) algorithms effective for single-cell Hi-C (scHiC) data.
Demonstrated high accuracy in distinguishing structural zeros from dropout events.
Showcased significant improvements in cell clustering accuracy after data imputation.

Conclusions:

Adapted single-cell RNA-sequencing (scRNA-seq) methods offer a powerful solution for handling zero-inflated single-cell Hi-C (scHiC) data.
Accurate identification of structural zeros and imputation of dropouts enhance the reliability of scHiC data analysis.
The proposed approach substantially improves the biological insights obtainable from scHiC studies, particularly in cell type classification.