Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Overview of Cell-Matrix Interactions01:24

Overview of Cell-Matrix Interactions

7.3K
The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...
7.3K
Cell-surface Signaling01:21

Cell-surface Signaling

51.9K
Hormones—or any molecule that binds to a receptor, known as a ligand—that are lipid-insoluble (water-soluble) are not able to diffuse across the cell membrane. In order to be able to affect a cell without entering it, these hormones bind to receptors on the cell membrane. When a first messenger, a hormone, binds to a receptor, a signal cascade is set off, causing second messengers, proteins inside the cell, to become activated, resulting in downstream effects.
51.9K
Interpreting R Charts01:22

Interpreting R Charts

82
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
82
Cell Migration01:09

Cell Migration

17.1K
Cell migration, the process by which cells move from one location to another, is essential for the proper development and viability of organisms throughout their life. When cells are not able to migrate properly to their ordained locations, various disorders may occur. For example, disruption in cell migration causes chronic inflammatory diseases such as arthritis.
17.1K
Cell Lines01:16

Cell Lines

7.5K
A cell line is a population of cells grown in vitro that can be subcultured over several generations. Normal cells cease to divide after a certain number of cell divisions, a process known as replicative senescence. This number, called the Hayflick limit, was conceptualized by Leonard Hayflick in 1961 when he observed that fetal cells grown in culture could only divide 40-60 times. This limit is due to the shortening of the telomeres during each round of cell division, preventing cell division...
7.5K
Overview of Cell Signaling01:23

Overview of Cell Signaling

20.4K
Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate with the environment.
Cells respond to many types of information, often through receptor proteins positioned on the membrane. For example, skin cells respond to and transmit touch...
20.4K

You might also read

Related Articles

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

Sort by
Same author

Synthetic essentiality of TRAIL/TNFSF10 in VHL-deficient renal cell carcinoma.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

The brain network underlying social participation: a multimodal, data-driven investigation.

Brain imaging and behavior·2026
Same author

Neonatal social communication and single genes predict the variability of post-pubertal social behavior in a mouse model of paternal 15q11-13 duplication.

Research square·2026
Same author

An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences.

NAR genomics and bioinformatics·2026
Same author

Neonatal social communication and single genes predict the variability of post-pubertal social behavior in a mouse model of paternal 15q11-13 duplication.

bioRxiv : the preprint server for biology·2026
Same author

Deriving Mendelian Randomization-Based Causal Networks of Brain Imaging Phenotypes and Bipolar Disorder.

Biological psychiatry. Cognitive neuroscience and neuroimaging·2025

Related Experiment Video

Updated: Jul 15, 2025

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions
08:33

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

Published on: August 5, 2020

7.0K

CCPlotR: an R package for the visualization of cell-cell interactions.

Sarah Ennis1,2,3, Pilib Ó Broin1,2, Eva Szegezdi1,3

  • 1The SFI Centre for Research Training in Genomics Data Science, Galway, H91 TK33, Ireland.

Bioinformatics Advances
|September 28, 2023
PubMed
Summary

CCPlotR is a new R package that visualizes cell-cell interactions predicted from single-cell gene expression data. It generates publication-ready figures like heatmaps and network diagrams for researchers.

More Related Videos

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

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

Published on: December 12, 2019

8.5K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K

Related Experiment Videos

Last Updated: Jul 15, 2025

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions
08:33

Kinetic Visualization of Single-Cell Interspecies Bacterial Interactions

Published on: August 5, 2020

7.0K
Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

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

Published on: December 12, 2019

8.5K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K

Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Understanding cell-cell interactions is crucial in biological research.
  • Existing tools often require complex data processing for visualization.
  • Single-cell gene expression data offers a powerful way to infer these interactions.

Purpose of the Study:

  • To introduce CCPlotR, an R package for visualizing cell-cell interactions.
  • To provide a user-friendly tool that accepts predicted interaction data as input.
  • To generate diverse, publication-ready figures for biological interpretation.

Main Methods:

  • CCPlotR is an R package that takes predicted cell-cell interaction tables as input.
  • It utilizes single-cell gene expression data outputs from other prediction tools.
  • The package generates various plot types including heatmaps, dotplots, circos plots, and network diagrams.

Main Results:

  • CCPlotR successfully generates multiple types of visualizations for cell-cell interactions.
  • The package is designed for ease of use, requiring only interaction prediction data.
  • Generated figures are publication-ready, aiding in the interpretation of biological data.

Conclusions:

  • CCPlotR serves as a valuable resource for researchers studying cell-cell interactions.
  • The package simplifies the visualization process, making complex data more accessible.
  • It supports the analysis of single-cell gene expression data for biological insights.