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

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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:
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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...
Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:

You might also read

Related Articles

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

Sort by
Same author

The survival double descent: generalization dynamics of deep neural networks in time-to-event analysis.

BMC medical research methodology·2026
Same author

Socioeconomic deprivation, rurality, and travel distance negatively impact survival in early-stage pancreatic ductal adenocarcinoma but are not associated with stage at diagnosis.

Cancer epidemiology·2026
Same author

Polygenic scores for risk of pancreatic ductal adenocarcinoma: evaluation of novel and published models.

NPJ precision oncology·2026
Same author

Personalized risk prediction of financial toxicity in patients with cancer: an interpretable machine learning study.

JNCI cancer spectrum·2026
Same author

Novel Genetic Risk Loci for Pancreatic Ductal Adenocarcinoma Identified in a Genome-wide Study of African Ancestry Individuals.

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

Day-30 IL1RL1, CXCL9 and REG3α are prognostic for survival after mismatched unrelated donor transplantation.

British journal of haematology·2026
Same journal

Identification of Age-Associated Circulating Proteins and Lipids in 3800 Comorbidity-Enriched Older Adults from Japan-Based Cohorts Using Olink Assays and MRM Mass Spectrometry.

Journal of proteome research·2026
Same journal

Molecular Solution to the Paradox of Ancient Brain Preservation.

Journal of proteome research·2026
Same journal

From Method-Defined Signals to Reference Measurement Procedures: Two Decades of Mass Spectrometry-Based ProGRP Quantification.

Journal of proteome research·2026
Same journal

Proteomic Profiling of Extracellular Vesicle-Enriched Plasma Using Mag-Net for Biomarker Discovery in Pancreatic Ductal Adenocarcinoma.

Journal of proteome research·2026
Same journal

Computationally Efficient Bayesian Estimation of Graphical Networks for Omics Data.

Journal of proteome research·2026
Same journal

Hierarchy of MS-Based Evidence.

Journal of proteome research·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

A statistical model for iTRAQ data analysis.

Elizabeth G Hill1, John H Schwacke, Susana Comte-Walters

  • 1Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. hille@musc.edu

Journal of Proteome Research
|June 27, 2008
PubMed
Summary
This summary is machine-generated.

Biological and experimental factors cause variability in iTRAQ reporter ion peak areas. A statistical model incorporating these factors improves differential protein expression analysis and fold change quantification.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Jul 4, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Proteomics
  • Quantitative Mass Spectrometry
  • Statistical Modeling

Background:

  • Isobaric tag for relative and absolute quantitation (iTRAQ) experiments are widely used for differential protein expression analysis.
  • Variability in reporter ion peak areas can significantly impact the accuracy of quantitative proteomics results.
  • Understanding and modeling sources of variability is crucial for reliable protein quantification.

Purpose of the Study:

  • To identify and describe biological and experimental factors contributing to variability in iTRAQ reporter ion peak areas.
  • To develop and present a statistical model that incorporates these variability factors for improved differential protein expression analysis.
  • To demonstrate the utility of analysis of variance (ANOVA) for quantifying fold change within the developed statistical framework.

Main Methods:

  • Detailed analysis of biological and experimental factors influencing iTRAQ reporter ion peak areas.
  • Development of a statistical model to account for identified sources of variability.
  • Application of analysis of variance (ANOVA) for fold change quantification.
  • Validation of the statistical model using iTRAQ data from a spike-in study.

Main Results:

  • Identification of key biological and experimental factors that induce significant variability in iTRAQ reporter ion peak areas.
  • Demonstration that incorporating these factors into a statistical model enhances the evaluation of differential protein expression.
  • Quantification of fold change using ANOVA provides a robust measure of differential expression, accounting for variability.
  • The developed model showed utility in analyzing iTRAQ data from a spike-in experiment, confirming its effectiveness.

Conclusions:

  • Biological and experimental factors are critical sources of variability in iTRAQ experiments.
  • A statistical model incorporating these factors, coupled with ANOVA for fold change quantification, significantly improves the reliability of differential protein expression analysis.
  • This approach offers a more accurate and robust method for interpreting quantitative proteomics data from iTRAQ experiments.