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 Experiment Videos

Statistical methods for identifying differentially expressed gene combinations.

Yen-Yi Ho1, Leslie Cope, Marcel Dettling

  • 1Johns Hopkins University, Baltimore, MD, USA.

Methods in Molecular Biology (Clifton, N.J.)
|March 5, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Development of the cf-MMSP assay for enhanced analytical sensitivity of methylated DNA in breast cancer liquid biopsy.

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

This study identifies jointly differentially expressed gene groups for better phenotype discrimination. It reviews and compares statistical approaches for finding these gene pairs within regulatory networks.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Understanding gene expression regulatory networks is crucial for decoding biological functions.
  • Identifying coordinated gene expression changes across different conditions aids in this understanding.

Purpose of the Study:

  • To review and compare statistical approaches for identifying jointly differentially expressed gene groups.
  • To assess the effectiveness of these methods in improving phenotype discrimination.

Main Methods:

  • Statistical analysis of gene expression data.
  • Comparison of multiple computational approaches for group gene identification.
  • Evaluation using simulated datasets.

Main Results:

Related Experiment Videos

  • Jointly expressed gene groups offer superior phenotype discrimination compared to individual genes.
  • The chapter presents a comparative analysis of various identification strategies.

Conclusions:

  • Identifying jointly differentially expressed genes is a key strategy for understanding gene regulatory networks.
  • The reviewed methods provide valuable tools for biological data analysis.