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

Ranking analysis of microarray data: a powerful method for identifying differentially expressed genes.

Yuan-De Tan1, Myriam Fornage1, Yun-Xin Fu2

  • 1Institute of Molecular Medicine, School of Public Health, University of Texas at Houston, Houston, TX 77030, USA.

Genomics
|September 19, 2006
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

Multi-ancestry genome-wide association study reveals novel genetic signals for lung function decline.

medRxiv : the preprint server for health sciences·2024
Same author

DNA Methylation Signatures of Cardiovascular Health Provide Insights into Diseases.

medRxiv : the preprint server for health sciences·2024
Same author

Biological validation of peak-width of skeletonized mean diffusivity as a VCID biomarker: The MarkVCID Consortium.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2024
Same author

WMH Contributions to Cognitive Impairment: Rationale and Design of the Diverse VCID Study.

Stroke·2024
Same author

Cardiovascular Risk Factors and Genetic Risk in Transthyretin V142I Carriers.

JACC. Heart failure·2024
Same author

Genetic risk factors underlying white matter hyperintensities and cortical atrophy.

Nature communications·2024
Same journal

Integrating transcriptomics and metabolomics reveals the molecular landscape of sperm maturation driven by regional differentiation in the epididymis of Guizhou-Guiqian semi-fine wool sheep.

Genomics·2026
Same journal

Impact of genotype on histopathology and clinical characters in a Chinese cohort with obstructive hypertrophic cardiomyopathy.

Genomics·2026
Same journal

A novel reusable transcriptome-wide association study workflow used to map key genes linked to important cattle traits.

Genomics·2026
Same journal

The large mitochondrial genome of Syndiclis anlungensis (Lauraceae): Genome structure, comparative analysis, and phylogenetic relationships with other Syndiclis species.

Genomics·2026
Same journal

DeepGEP: Deep learning for gene expression prediction from multi-omics in mammals.

Genomics·2026
Same journal

Molecular features of external Auditory Canal cholesteatoma by microbial metagenomic sequencing.

Genomics·2026
See all related articles

Ranking Analysis of Microarray Data (RAM) is a novel statistical method for analyzing gene expression. RAM efficiently identifies differentially expressed genes and estimates the false discovery rate (FDR) in complex biological studies.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray technology enables simultaneous gene expression profiling of thousands of genes.
  • Classical statistical methods are insufficient for analyzing complex microarray data.
  • Developing robust methods for large-scale statistical analysis of microarray data is crucial.

Purpose of the Study:

  • To introduce a novel statistical method, Ranking Analysis of Microarray Data (RAM), for analyzing gene expression profiles.
  • To enhance the identification of differentially expressed genes and improve false discovery rate (FDR) estimation.

Main Methods:

  • RAM employs a large-scale two-sample t-test approach.
  • It compares ranked T statistics with ranked Z values derived from a random splitting method.

Related Experiment Videos

  • A two-simulation strategy is utilized for FDR estimation.

    • Main Results:

    • RAM demonstrates higher efficiency in identifying differentially expressed genes compared to Significance Analysis of Microarrays.
    • RAM provides more accurate FDR estimation, especially under challenging conditions like small sample sizes or noise.
    • The method performs well even with large fudge factors or mixture distributions.

    Conclusions:

    • RAM is a powerful and efficient tool for analyzing microarray data.
    • The method offers advantages over existing techniques, particularly in complex disease research.
    • RAM improves the reliability of gene expression analysis and FDR estimation.