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

Cell Diversity

Cell Diversity

The concept of a cell started with microscopic observations of dead cork tissue by Robert Hooke in 1665. Hooke coined the term "cell" based on the resemblance of the small subdivisions in the cork to the rooms that monks inhabited, called cells. About ten years later, Antonie van Leeuwenhoek became the first person to observe the living and moving cells under a microscope. In the century that followed, the theory that cells represented the basic unit of life developed.
Multicellular...

You might also read

Related Articles

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

Sort by

Same author

SCOT+: a comprehensive software suite for single-cell alignment using optimal transport.

Bioinformatics advances·2026

Same author

Cell type-specific gene regulatory network inference from single cell transcriptomics with ctOTVelo.

bioRxiv : the preprint server for biology·2026

Same author

Diffusion-based Representation Integration for Foundation Models Improves Spatial Transcriptomics Analysis.

bioRxiv : the preprint server for biology·2025

Same author

Inferring the regulation dynamics of oscillatory networks from scRNA-seq data.

bioRxiv : the preprint server for biology·2025

Same author

Urban contact patterns shape respiratory syncytial virus epidemics with implications for vaccination.

Science advances·2025

Same author

A deep learning model to predict glioma recurrence using integrated genomic and clinical data.

Communications medicine·2025

Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026

Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 25, 2025

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

SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation.

Pinar Demetci^1,2, Rebecca Santorella³, Manav Chakravarthy²

¹Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|October 17, 2022

Summary

This summary is machine-generated.

We developed SCOTv2, a new method for aligning single-cell measurements from different experiments. It effectively handles varying cell types and sample sizes, improving multiomic data integration for biological insights.

Keywords:

data integration manifold alignment multiomics single-cell sequencing unbalanced optimal transport

More Related Videos

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Related Experiment Videos

Last Updated: Aug 25, 2025

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

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell multiomic analysis offers deep insights into genomic regulation.
Current alignment methods struggle with separately sampled cell populations (non-coassay data).
Disproportionate cell-type representation across measurement domains hinders existing algorithms.

Purpose of the Study:

To develop an improved single-cell alignment method for non-coassay data.
To address challenges posed by unequal cell-type representation and sample sizes.
To enhance the integration of multiomic single-cell measurements.

Main Methods:

Extension of the Single Cell alignment using Optimal Transport (SCOT) method.
Application of unbalanced Gromov-Wasserstein optimal transport.
Benchmarking on five diverse non-coassay datasets (simulated and real-world).

Main Results:

SCOTv2 achieves state-of-the-art alignment performance on non-coassay single-cell data.
The method successfully handles disproportionate cell-type representation.
SCOTv2 integrates multiple single-cell measurements effectively.

Conclusions:

SCOTv2 provides a robust solution for single-cell data integration from non-coassay experiments.
The method maintains computational tractability and self-tuning capabilities.
This advancement facilitates more accurate multiomic analyses for understanding biological processes.