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

You might also read

Related Articles

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

Sort by

Same author

Correction of the molecular phenotype of X-linked Dystonia-Parkinsonism reveals a non-canonical function of BRD4.

Nature communications·2026

Same author

Impact of catheter-directed thrombolysis on the socio-economic burden of pulmonary embolism in Germany: a cost-effectiveness analysis.

European heart journal. Quality of care & clinical outcomes·2026

Same author

Identifying Biomedical Entities for Datasets in Scientific Articles: 4-Step Cache-Augmented Generation Approach Using GPT-4o and PubTator 3.0.

JMIR formative research·2025

Same author

Sparse dimensionality reduction for analyzing single-cell-resolved interactions.

Bioinformatics (Oxford, England)·2025

Same author

Exploring Feature Preferences for a Treatment-Accompanying App in Patients Undergoing Radiation Therapy: Cross-Sectional Study.

JMIR cancer·2025

Same author

DNMT1-mediated regulation of somatostatin-positive interneuron migration impacts cortical architecture and function.

Nature communications·2025

Same journal

MOREshiny: a user-friendly application for the inference of phenotype-specific multi-omic regulatory networks.

Bioinformatics advances·2026

Same journal

spammR: an R package designed for analysis and integration of spatial multi-omic measurements.

Bioinformatics advances·2026

Same journal

Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.

Bioinformatics advances·2026

Same journal

vClassifier: a toolkit for high-resolution phylogenetic classification of prokaryotic viruses.

Bioinformatics advances·2026

Same journal

GWAIS-Web: a free and secure web service for ultra-fast and large-scale genome-wide association interaction studies.

Bioinformatics advances·2026

Same journal

Folding the unfoldable 2: using AlphaFold and ESMFold to explore spurious proteins.

Bioinformatics advances·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 28, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Sparse dimensionality reduction for analyzing single-cell-resolved interactions.

Niklas Brunn^1,2, Maren Hackenberg^1,2, Camila L Fullio^3,4

¹Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau 79104, Germany.

Bioinformatics Advances

|March 27, 2026

Summary

This summary is machine-generated.

This study introduces a new workflow for analyzing cell-cell interactions using sparse dimensionality reduction. The method helps pinpoint specific ligand-receptor interactions within cell pairs from single-cell transcriptomics data.

More Related Videos

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Related Experiment Videos

Last Updated: Mar 28, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Area of Science:

Computational Biology
Single-Cell Genomics
Systems Biology

Background:

Reconstructing cell-cell interactions from single-cell transcriptomics is crucial for understanding biological systems.
Existing methods often leverage known ligand-receptor interactions but can be complex to analyze downstream.

Purpose of the Study:

To develop an end-to-end dimensionality reduction workflow tailored for single-cell cell-cell interaction data.
To enhance the analysis and interpretation of interaction patterns between cell pairs.

Main Methods:

Implemented a sparse dimensionality reduction workflow.
Utilized the Boosting Autoencoder approach for sparse dimensionality reduction.
Developed a comprehensive workflow including result visualization.

Main Results:

Demonstrated that sparse dimensionality reduction can pinpoint specific ligand-receptor interactions.
Successfully related these interactions to clusters of cell pairs.
Provided a simplified analysis of interaction patterns.

Conclusions:

The proposed workflow effectively simplifies the analysis of cell-cell interaction data.
Sparse dimensionality reduction is a powerful tool for identifying specific ligand-receptor interactions in single-cell data.
The provided Jupyter notebook offers an adaptable solution for researchers.