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

Filtering genes to improve sensitivity in oligonucleotide microarray data analysis.

Stefano Calza1, Wolfgang Raffelsberger, Alexander Ploner

  • 1Department of Medical Epidemiology and Biostatistics-Karolinska Institute, Stockholm, Sweden.

Nucleic Acids Research
|August 19, 2007
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

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

Correction to 'scSuperAnnotator: A platform for benchmarking comparison and visualizing automated cellular annotation methods for scRNA-seq data'.

Nucleic acids research·2026
Same journal

Correction to 'Differentiable partition function calculation for RNA'.

Nucleic acids research·2026
Same journal

Deployment of non-canonical splicing in tunicate genomes is mediated by divergent U2AF function and changing m6A modification in U1 and U6 snRNA.

Nucleic acids research·2026
Same journal

Bacillus subtilis DnaB forms multiple protein-protein interactions essential for DNA replication initiation.

Nucleic acids research·2026
Same journal

Multiple forms of protein-protein and DNA binding are exhibited by BrxC from the BREX phage restriction system.

Nucleic acids research·2026
Same journal

Biosynthesis of glycosylated 5-hydroxycytosine in the DNA of diverse viruses.

Nucleic acids research·2026
See all related articles

FLUSH (Filtering Likely Uninformative Sets of Hybridizations) is a new method to remove uninformative probe sets from microarray data. This improves the detection of differentially expressed (DE) genes by reducing false discoveries.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray analysis involves controlling the false discovery rate (FDR) with numerous probe sets.
  • Many probe sets represent unexpressed genes or non-differentially expressed (non-DE) genes, contributing to false discoveries.

Purpose of the Study:

  • To develop and evaluate a novel filtering procedure, FLUSH (Filtering Likely Uninformative Sets of Hybridizations), to remove uninformative probe sets prior to differential gene expression analysis.
  • To improve the sensitivity and accuracy of detecting differentially expressed genes in microarray studies.

Main Methods:

  • Fitting a robust linear model to probe-level Affymetrix data, accounting for probe and array effects.
  • Implementing the FLUSH procedure to exclude probe sets with statistically small array-effects or large residual variance.

Related Experiment Videos

  • Evaluating FLUSH on spiked-in and mouse retinal degeneration datasets.

    • Main Results:

    • FLUSH filtering significantly improved the sensitivity of detecting differentially expressed (DE) genes compared to unfiltered and other filtering methods.
    • The procedure was effective on both controlled spiked-in experiments and real biological data.
    • A freely available R package, FLUSH, was developed to implement the method.

    Conclusions:

    • FLUSH is an effective method for filtering uninformative probe sets in microarray analysis.
    • This filtering enhances the detection of true DE genes, leading to more reliable biological insights.
    • The FLUSH package provides a practical tool for researchers to improve their microarray data analysis.