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

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

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

Sort by
Same author

Human Papillomavirus-Related Cancer in People With HIV and Solid Organ Transplant Recipients.

JAMA network open·2026
Same author

Preoperative Manual Detorsion in Children With Intravaginal Testicular Torsion: A Single-Center Experience.

World journal of surgery·2026
Same author

Polygenic overlap and shared genomic loci between anorexia nervosa and cardiometabolic traits suggest shared biological mechanisms.

Neurobiology of disease·2026
Same author

Genetic variation in antidiabetic drug targets: associations with Parkinson's disease risk and age at onset.

NPJ Parkinson's disease·2026
Same author

Humoral and Cellular Immune Response in Patients with Hematological Disorders After Three Doses of mRNA COVID-19 Vaccine: A Single-Center Observational Study.

Vaccines·2026
Same author

Cognitive and fine motor performance in people above 65 years of age with and without HIV.

Scientific reports·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Detecting differential expression in microarray data: comparison of optimal procedures.

Elena Perelman1, Alexander Ploner, Stefano Calza

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden. lenaperelman@gmail.com <lenaperelman@gmail.com>

BMC Bioinformatics
|January 30, 2007
PubMed
Summary
This summary is machine-generated.

Comparing methods for analyzing gene expression data, the fdr2d procedure outperforms the optimal discovery procedure (ODP). A new S2d method shows promise, performing well on real data for identifying differentially expressed genes.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Related Experiment Videos

Last Updated: Jun 28, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

  • Bioinformatics
  • Statistical genomics
  • Computational biology

Background:

  • Differential gene expression analysis in microarrays commonly uses t-statistics.
  • False discovery rate (FDR) is crucial for managing multiple testing in these analyses.
  • Existing methods like ODP and fdr2d offer different theoretical advantages but their comparative performance is unclear.

Purpose of the Study:

  • To compare the performance of the optimal discovery procedure (ODP) and fdr2d.
  • To introduce and evaluate a novel procedure, S2d, combining aspects of ODP and fdr2d.

Main Methods:

  • Comparative analysis of ODP and fdr2d using simulated and real microarray datasets.
  • Development and assessment of the new S2d procedure, integrating ODP's statistic with fdr2d's FDR assessment.

Main Results:

  • The fdr2d procedure demonstrated superior performance over ODP in both simulated and real datasets.
  • Both fdr2d and ODP outperformed standard t-statistics with local FDR.
  • The S2d procedure matched fdr2d on simulated data and showed improved performance on real data.

Conclusions:

  • Incorporating standard error information enhances ODP, suggesting theoretical optimality may not translate to practical application.
  • The S2d procedure offers a slight advantage over fdr2d, particularly on real data, despite increased computational complexity and a less intuitive statistic.