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

One-Way ANOVA01:18

One-Way ANOVA

13.5K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
13.5K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.3K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.3K
Combinatorial Gene Control02:33

Combinatorial Gene Control

9.7K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
9.7K
Two-Way ANOVA01:17

Two-Way ANOVA

3.5K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Benchmarking reliability and calibration of LLMs for multi-cancer early detection test communication.

JAMIA open·2026
Same author

Pan-Cancer Genomic Scars of Alternative End Joining and Single-Strand Annealing.

bioRxiv : the preprint server for biology·2026
Same author

Multivariate causal effects: a Bayesian causal regression factor model.

Biometrics·2026
Same author

A Longitudinal Comprehensive Biospecimen and Clinical Data Repository for Cancer Early Detection: The InAdvance Study.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

A Gene Expression Tumor Signature Optimizing Partial Area-Under-the-Curve (pAUC) to Improve Specificity for Indolent Prostate Cancer.

The Prostate·2026
Same author

Web-Based User Interface for Fam3PRO: A Multigene, Multicancer Risk Prediction Model for Families With Cancer History.

JCO clinical cancer informatics·2026

Related Experiment Video

Updated: Feb 23, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K

Integrative factor analysis - An unsupervised method for quantifying cross-study consistency of gene expression data.

Xin Victoria Wang1, Giovanni Parmigiani1

  • 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.

Genomics
|September 9, 2017
PubMed
Summary
This summary is machine-generated.

Integrative correlation (IGC) methods for gene expression analysis were extended to handle multiple studies. This approach identifies inconsistent studies and improves phenotype association concordance.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.0K
Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

17.3K

Related Experiment Videos

Last Updated: Feb 23, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.2K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.0K
Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
10:50

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

17.3K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Integrative analysis of multiple gene expression studies is common.
  • Integrative correlation (IGC) assesses gene co-expression consistency in two studies.
  • Extending IGC for three or more studies provides a global score per gene.

Purpose of the Study:

  • To extend Integrative Correlation (IGC) for assessing gene co-expression consistency across three or more studies, potentially from different platforms.
  • To develop a method that identifies studies with divergent expression patterns.
  • To evaluate if filtering genes using the proposed score enhances cross-study phenotype association concordance.

Main Methods:

  • Extension of the Integrative Correlation (IGC) method.
  • Application of factor analysis to assess study-specific gene co-expression consistency.
  • Development of a scoring system for gene expression patterns across multiple studies.
  • Gene filtering based on the derived scores.

Main Results:

  • The proposed method successfully assesses study-specific co-expression consistency for three or more gene expression studies.
  • The method can identify individual studies exhibiting different expression patterns compared to the majority.
  • Filtering genes using the developed score demonstrably improves the concordance of phenotype associations across studies.

Conclusions:

  • The extended IGC method with factor analysis provides a robust approach for analyzing multi-study gene expression data.
  • This method aids in identifying data heterogeneity and improving the reliability of cross-study findings.
  • The approach enhances the discovery of biologically relevant genes by improving phenotype association concordance.