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

Arrhenius Plots02:34

Arrhenius Plots

47.2K
The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
The Arrhenius equation can be used...
47.2K
Residual Plots01:07

Residual Plots

6.5K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
6.5K
Microsoft Excel: Plotting Mean, SD, and SE01:18

Microsoft Excel: Plotting Mean, SD, and SE

1.3K
In Microsoft Excel, plotting the mean along with standard deviation (SD) and standard error (SE) helps visualize data variability and reliability. To plot these values, follow these steps:
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...
1.3K
Bode Plots01:26

Bode Plots

1.3K
Bode plots are graphical tools that use logarithmic scales for frequency on the x-axis and gain in decibels on the y-axis. This logarithmic method allows a wide range of frequencies to be compactly displayed, enabling the analysis of component effects on circuit behavior across a broad frequency spectrum.
A network function represents the ratio of a system's output to its input, with the magnitude and phase angle derived from the complex network function. The decibel logarithmic gain is...
1.3K
Scatter Plot01:15

Scatter Plot

11.9K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
11.9K
Bode Plots Construction01:24

Bode Plots Construction

1.1K
The Bode plot is an essential tool in control system analysis, mapping the frequency response of a system through a magnitude plot and a phase plot, both against a logarithmic frequency axis. To construct a Bode plot, consider the transfer function H(ω):
1.1K

You might also read

Related Articles

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

Sort by
Same author

Plasma proteins associated with disability and mortality risks in Japanese community-dwelling octogenarians.

GeroScience·2026
Same author

Plasma GDF15 affects long-term dementia risk and alters neuroimmune signaling.

Science advances·2026
Same author

Circulating cell type senescence signatures track distinct dimensions of health status and trajectories in human longitudinal cohorts.

Cell reports·2026
Same author

Caloric restriction reprograms skeletal muscle molecular pathways in non-human primates: potential relevance to human aging biology.

Skeletal muscle·2026
Same author

Proteomic Age Acceleration in Multiple Sclerosis Precedes Symptom Onset and Associates with Severity.

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

Machine learning model based on plasma proteomics for the identification of Parkinson's disease.

Brain : a journal of neurology·2026
Same journal

FRIENDS GUI: A Graphical User Interface for Data Collection and Visualization of Vaping Behavior from a Passive Vaping Monitor.

Journal of open research software·2026
Same journal

PFHub: The Phase-Field Community Hub.

Journal of open research software·2024
Same journal

<i>%svy_freqs:</i> A Generic SAS Macro for Creating Publication-Quality Three-Way Cross-Tabulations.

Journal of open research software·2023
Same journal

Embo: a Python package for empirical data analysis using the Information Bottleneck.

Journal of open research software·2023
Same journal

CMakeCatchTemplate: A C++ template project.

Journal of open research software·2021
Same journal

Using SAS Macros for Multiple Mediation Analysis in R.

Journal of open research software·2021
See all related articles

Related Experiment Video

Updated: Feb 8, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

1.0K

Web Tool for Navigating and Plotting SomaLogic ADAT Files.

Foo Cheung1, Giovanna Fantoni1, Maria Conner1

  • 1Trans-NIH Center for Human Immunology, Autoimmunity and Inflammation, National Institutes of Health, Bethesda, MD 20892, US.

Journal of Open Research Software
|June 29, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a user-friendly, open-source Shiny web tool for navigating and plotting proteomic data from SomaScan assays (ADAT files). This tool empowers researchers to perform initial data analysis, quality control, and statistical assessments independently.

Keywords:
ADATSOMAscanShinyproteomic

More Related Videos

Dynamic Navigation in Endodontics: Guided Access Cavity Preparation by Means of a Miniaturized Navigation System
07:03

Dynamic Navigation in Endodontics: Guided Access Cavity Preparation by Means of a Miniaturized Navigation System

Published on: May 5, 2022

5.3K
Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

10.5K

Related Experiment Videos

Last Updated: Feb 8, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

1.0K
Dynamic Navigation in Endodontics: Guided Access Cavity Preparation by Means of a Miniaturized Navigation System
07:03

Dynamic Navigation in Endodontics: Guided Access Cavity Preparation by Means of a Miniaturized Navigation System

Published on: May 5, 2022

5.3K
Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

10.5K

Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • SomaScan™ is a proteomic platform generating data in a proprietary ADAT format.
  • Analyzing ADAT files requires specialized tools for navigation, visualization, and quality control.
  • Existing methods for ADAT file analysis can be cumbersome and require significant bioinformatics expertise.

Purpose of the Study:

  • To develop an accessible, open-source, platform-independent software tool for analyzing SomaScan ADAT files.
  • To provide researchers with a user-friendly, point-and-click interface for navigating, plotting, and performing initial analyses of proteomic data.
  • To facilitate independent data exploration, quality control, and statistical analysis for biologists working with SomaScan data.

Main Methods:

  • Development of an interactive Shiny Web Tool.
  • Implementation of a point-and-click interface for ADAT file navigation and data plotting.
  • Integration of functionalities for quality control (QC) and basic statistical analysis.
  • Provision of extensive video tutorials, example data, and source code for user accessibility.

Main Results:

  • A functional and user-friendly web tool for ADAT file analysis has been created.
  • The tool enables researchers to navigate, visualize, and perform initial analyses on their proteomic data.
  • The developed tool has been successfully applied in preliminary analyses across multiple research projects.
  • Positive feedback from biologists regarding ease of use and analytical capabilities.

Conclusions:

  • The developed Shiny Web Tool significantly enhances the accessibility and usability of SomaScan proteomic data analysis.
  • This open-source tool empowers researchers to conduct self-service data exploration and quality control.
  • The tool addresses a critical need for intuitive data analysis solutions in proteomic research.
  • Availability of tutorials, example data, and source code promotes wider adoption and collaboration.