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

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

You might also read

Related Articles

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

Sort by

Same author

Nitric Oxide Conjugation Transforms NIR-II AIEgens Into Potent Hypoxia-Tolerant Type I Photosensitizers.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

IMATAC imputes single-cell ATAC-seq data by deep hierarchical network with denoising autoencoder.

Briefings in bioinformatics·2025

Same author

Editorial: Expanding insights into structure, function, and disorder of genome by the power of artificial intelligence in bioinformatics.

Frontiers in genetics·2025

Same author

Dual balanced augmented topological noncoding RNA disease triplet association in heterogeneous graphs.

Briefings in bioinformatics·2025

Same author

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·2025

Same author

DeepExDC interprets genomic compartmentalization changes in single-cell Hi-C data.

Briefings in bioinformatics·2025

Search research articles

Related Experiment Video

Updated: Aug 26, 2025

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 6, 2010

scHiCPTR: unsupervised pseudotime inference through dual graph refinement for single-cell Hi-C data.

Hongqiang Lyu¹, Erhu Liu¹, Zhifang Wu¹

¹School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Shaanxi 710049, China.

Bioinformatics (Oxford, England)

|October 7, 2022

Summary

We developed scHiCPTR, a novel pipeline for inferring cell developmental trajectories from single-cell Hi-C data. This method accurately reconstructs complex biological pathways, advancing chromosomal organization studies.

More Related Videos

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Last Updated: Aug 26, 2025

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 6, 2010

Capturing Chromosome Conformation Across Length Scales

Capturing Chromosome Conformation Across Length Scales

Published on: January 20, 2023

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Genomics
Computational Biology
Epigenetics

Background:

Single-cell Hi-C technology enables dynamic studies of chromosomal organization.
Inferring developmental trajectories from sparse, noisy single-cell Hi-C data is challenging due to high dimensionality and diverse trajectory topologies.

Purpose of the Study:

To present scHiCPTR, an unsupervised graph-based pipeline for pseudotime inference from single-cell Hi-C contact matrices.
To develop a robust method capable of handling various developmental trajectory structures.

Main Methods:

scHiCPTR employs a workflow including imputation, embedding, graph construction, and dual graph refinement.
The pipeline utilizes parallel graph pruning to reduce noise and establish global developmental directionality.
It is designed to accommodate linear, bifurcated, and circular developmental trajectories.

Main Results:

scHiCPTR achieves higher performance in pseudotime inference compared to existing methods.
The inferred developmental trajectories demonstrate significant biological relevance.
The method is competitive with established single-cell RNA-seq analysis tools.

Conclusions:

scHiCPTR provides an effective solution for reconstructing cell differentiation pathways using single-cell Hi-C data.
The pipeline's ability to handle diverse topologies and noise makes it a valuable tool for studying chromosomal organization dynamics.