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 Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and columns,...
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
Microsoft Excel: Median, Quartile range, and Box Plots01:29

Microsoft Excel: Median, Quartile range, and Box Plots

In Microsoft Excel, calculating the median, interquartile range, and creating box plots can help understand the distribution of your data.
Median and Quartile Range: The median is calculated using the formula `=MEDIAN(range)', which provides the middle value of your data set. Quartiles divide your data into four equal parts. To find the first and third quartiles, use ‘=QUARTILE(range, 1)' and ‘=QUARTILE(range, 3)', respectively. The interquartile range (IQR), which measures data spread, is...
Overview of Minitab01:11

Overview of Minitab

Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to users...
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
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

Sex-specific mitochondrial dysregulation and metformin response in Wilson disease.

Scientific reports·2026
Same author

Partial body mass regain attenuates lipid utilization and alters the hepatic lipidome linked to HDL dysfunction during metabolic syndrome in male rats.

Physiological reports·2026
Same author

Inverse Association between the ω-3 Index and Neutrophil-Lymphocyte Ratio: Pooled Results from Four Supplementation Trials.

The Journal of nutrition·2026
Same author

Cytokine and Oxylipin Production in Resting and LPS-Stimulated Monocytes From Americans of African Ancestry Are Influenced by ALOX5 Promoter Tandem Repeat Polymorphisms.

Mediators of inflammation·2026
Same author

High- versus low-dose dietary n-3 PUFA treatment produces mixed effects on DNA methylation and epigenetic fidelity in breast adipose tissue.

medRxiv : the preprint server for health sciences·2026
Same author

Chardonnay grape marc/grape seed extract blends improve postprandial triglycerides and/or HDL cholesterol concentrations in adults with mild dyslipidemia in a randomized double blinded crossover trial.

Nutrition research (New York, N.Y.)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: May 20, 2026

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

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel.

Dmitry Grapov1, John W Newman

  • 1Department of Nutrition, University of California, Davis, CA 95616, USA.

Bioinformatics (Oxford, England)
|July 21, 2012
PubMed
Summary
This summary is machine-generated.

Interactive modules for Data Exploration and Visualization (imDEV) offers a user-friendly R and Excel interface for omics data analysis. It provides dynamic multivariate statistics and visualization tools for robust biological data insights.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: May 20, 2026

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

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Omics data analysis requires integrated environments for complex statistical computations and visualizations.
  • Existing tools may lack user-friendliness or comprehensive visualization options for large datasets.

Purpose of the Study:

  • To introduce interactive modules for Data Exploration and Visualization (imDEV), an integrated application for omics data analysis.
  • To provide a user-friendly interface combining R's statistical capabilities with Microsoft Excel for dynamic data exploration.

Main Methods:

  • imDEV is an embedded application within Microsoft Excel.
  • It interfaces R's multivariate statistical packages and visualization libraries with the spreadsheet environment.
  • The tool supports various analyses including multiple comparisons, clustering, PCA, PLS-DA, and generates 2D/3D visualizations.

Main Results:

  • imDEV enables interactive and dynamic analysis of large omics datasets.
  • It facilitates robust inferences through advanced statistical methods and false discovery correction.
  • High-quality visualizations such as heat maps, dendrograms, and biplots are generated.

Conclusions:

  • imDEV provides an accessible and powerful platform for omics data exploration and visualization.
  • The integration of R and Excel enhances the usability and accessibility of complex bioinformatics analyses.
  • This tool aids researchers in generating information-rich visualizations for better biological interpretation.