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

Nonparametric methods for identifying differentially expressed genes in microarray data.

Olga G Troyanskaya1, Mitchell E Garber, Patrick O Brown

  • 1Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.

Bioinformatics (Oxford, England)
|November 9, 2002
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

Single-cell epigenetic landscape, microenvironment interactions, and gene regulatory modules of non-functioning pituitary adenomas.

Cell systems·2026
Same author

Shared multicellular injury programs of acute and chronic kidney disease enable mechanistic patient stratification.

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

Single-cell profiling reveals epithelial and immune responses in BK polyomavirus-infected human kidney biopsies.

JCI insight·2026
Same author

Multi-modal tissue-aware graph neural network for <i>in silico</i> genetic discovery.

bioRxiv : the preprint server for biology·2026
Same author

Gene-centered representation of coding and regulatory variation enables outcome prediction.

bioRxiv : the preprint server for biology·2026
Same author

Variant-resolved prediction of context-specific isoform variation with a graph-based attention model.

Cell genomics·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

Identifying disease markers from gene expression data is crucial for clinical care. This study compares three methods for robustly finding differentially expressed genes, finding they effectively identify relevant markers in simulated and real biological data.

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Gene expression experiments systematically identify disease markers.
  • Robust identification of differentially expressed genes from microarray data is essential for clinical applications.
  • Differentially expressed genes exhibit significant expression differences between experimental groups.

Purpose of the Study:

  • To compare the performance of three model-free methods for identifying differentially expressed genes.
  • To assess methods using simulated and biological data under varying noise levels and p-value cutoffs.
  • To provide a robust approach for identifying disease-related gene markers.

Main Methods:

  • Comparison of three model-free approaches: nonparametric t-test, Wilcoxon rank sum test, and an ideal discriminator method.

Related Experiment Videos

  • Systematic performance assessment using simulated datasets with controlled noise levels.
  • Evaluation on biological datasets, including lung tumor and lymphoma data.

    • Main Results:

    • All evaluated methods demonstrated very low false positive rates on simulated data.
    • The Wilcoxon rank sum test was the most conservative, suitable for biological validation.
    • Nonparametric t-test or higher p-value cutoffs are appropriate for more inclusive marker lists.
    • Methods successfully identified biologically relevant differentially expressed genes in lung tumor and lymphoma datasets, enabling clear group separation.

    Conclusions:

    • The evaluated methods offer a convenient and robust approach for identifying differentially expressed genes.
    • These methods facilitate further biological and clinical analysis of potential disease markers.
    • The choice of method and p-value cutoff can be tailored based on the need for conservative or inclusive gene lists.