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

Introduction to R01:11

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Interpreting R Charts01:22

Interpreting R Charts

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 values—of a sample...

You might also read

Related Articles

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

Sort by
Same author

Remaining gaps and obstacles in the outpatient setting for peripheral artery disease endovascular revascularization: Lessons learned from the French experience.

Vascular medicine (London, England)·2025
Same author

Enhancing statistical analysis of real world data.

Database : the journal of biological databases and curation·2025
Same author

Sustained excess all-cause mortality post COVID-19 in 21 countries: an ecological investigation.

International journal of epidemiology·2025
Same author

AlphaMissenseR: an integrated framework for investigating missense mutations in human protein-coding genes.

Bioinformatics advances·2025
Same author

The text2term tool to map free-text descriptions of biomedical terms to ontologies.

Database : the journal of biological databases and curation·2024
Same author

Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms.

JAMIA open·2024
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

Related Experiment Video

Updated: May 26, 2026

A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

Analyzing biological data using R: methods for graphs and networks.

Nolwenn Le Meur1, Robert Gentleman

  • 1IRISA, Equipe Symbiose, Université de Rennes I, Rennes, France. nlemeur@gmail.com

Methods in Molecular Biology (Clifton, N.J.)
|December 7, 2011
PubMed
Summary
This summary is machine-generated.

This tutorial explores using R, a statistical software, to analyze biological data. It focuses on R packages for examining graph and network properties, aiding biological data interpretation.

Related Experiment Videos

Last Updated: May 26, 2026

A User-friendly and Powerful R Analysis of Large-scale Datasets
10:56

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • R is a versatile programming language and software environment extensively used for statistical analysis and data visualization.
  • The functionality of R can be significantly expanded through a wide array of specialized add-on packages.
  • Several R packages are specifically designed for the comprehensive analysis of statistical properties inherent in graphs and networks.

Purpose of the Study:

  • To provide a practical, hands-on tutorial for utilizing R methods in the examination of biological data.
  • To demonstrate how to leverage R packages for analyzing the topological and statistical characteristics of biological networks.
  • To enhance the understanding and application of network analysis techniques within biological research using R.

Main Methods:

  • Utilizing the R programming language and its extensive package ecosystem.
  • Applying graph and network analysis techniques within the R environment.
  • Focusing on specific R packages designed for network and graph statistical property analysis.

Main Results:

  • Demonstration of practical R methods for biological data analysis.
  • Illustration of how to analyze topological properties of biological networks using R.
  • Explanation of statistical property analysis for graphs and networks in R.

Conclusions:

  • R offers a powerful and extensible platform for biological data analysis, particularly for network and graph studies.
  • The use of specialized R packages facilitates in-depth examination of biological network structures and statistical properties.
  • This tutorial equips researchers with practical skills to apply R for advanced biological network analysis.