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

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...

Electron Microscope Tomography and Single-particle Reconstruction

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

What is Evolutionary History?

What is Evolutionary History?

Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.

You might also read

Related Articles

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

Sort by

Same author

Lense: optimizing data preprocessing in single-cell omics using large language models.

Briefings in bioinformatics·2026

Same author

CARES-Net: a channel-attention residual network for multi-disease classification in small-sample <sup>1</sup>H NMR metabolomics data.

Analytica chimica acta·2026

Same author

Lense: Optimizing data preprocessing in single-cell omics using LLMs.

bioRxiv : the preprint server for biology·2026

Same author

Quantifying Cross-Modal Association Confidence for Single-Cell RNA-ATAC Integration.

bioRxiv : the preprint server for biology·2026

Same author

Trajectory-guided dimensionality reduction for multi-sample single-cell RNA-seq data reveals biologically relevant sample-level heterogeneity.

Bioinformatics (Oxford, England)·2026

Same author

Halo: a pretrained model for whole-cell segmentation from nuclei images in spatial transcriptomics.

bioRxiv : the preprint server for biology·2026

Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 29, 2026

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Published on: August 24, 2018

Pseudotime Reconstruction Using TSCAN.

Zhicheng Ji¹, Hongkai Ji²

¹Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Methods in Molecular Biology (Clifton, N.J.)

|February 14, 2019

Summary

This summary is machine-generated.

This study introduces TSCAN, an R package for analyzing single-cell RNA sequencing (scRNA-seq) data. TSCAN computationally orders cells to reveal gene expression dynamics along pseudotemporal trajectories, aiding biological process reconstruction.

Keywords:

Bioinformatics Gene expression Genomics Minimum spanning tree Pseudotime Single-cell RNA-seq

More Related Videos

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

Three-Dimensional Reconstruction of Orbital Fractures

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

Related Experiment Videos

Last Updated: Jan 29, 2026

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Designing CAD/CAM Surgical Guides for Maxillary Reconstruction Using an In-house Approach

Published on: August 24, 2018

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

Three-Dimensional Reconstruction of Orbital Fractures

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) generates data where cells often represent continuous biological processes.
Analyzing gene expression dynamics is crucial for understanding these dynamic biological processes.
Computational ordering of cells by gene expression aids in reconstructing cellular trajectories.

Purpose of the Study:

To introduce the TSCAN R package for pseudotemporal analysis of scRNA-seq data.
To demonstrate how TSCAN can reconstruct cellular pseudo-time and analyze gene expression dynamics.
To facilitate the generation of biological insights from continuous cellular processes.

Main Methods:

Application of the TSCAN R package to scRNA-seq datasets.
Computational ordering of cells based on gene expression profiles.
Analysis of gene expression dynamics along inferred pseudotemporal trajectories.

Main Results:

TSCAN enables the in silico reconstruction of cellular pseudotime from scRNA-seq data.
The package facilitates the identification of gene expression dynamics along continuous biological processes.
This approach aids in understanding cellular transitions and developmental trajectories.

Conclusions:

TSCAN is a valuable tool for pseudotemporal ordering and analysis of scRNA-seq data.
The package supports the reconstruction of biological processes and generation of novel insights.
Pseudotime analysis using TSCAN enhances the interpretation of dynamic cellular states.