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

Empirical array quality weights in the analysis of microarray data.

Matthew E Ritchie1, Dileepa Diyagama, Jody Neilson

  • 1Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia. mritchie@wehi.edu.au

BMC Bioinformatics
|May 23, 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

The roles of the acetyltransferase domains of the chromatin regulators KAT6A and KAT6B in vivo.

Development (Cambridge, England)·2026
Same author

KAT6A is essential for developmental control gene expression in neural stem and progenitor cells.

PLoS genetics·2026
Same author

Acetyl-carnitine improves hyperactivity and learning deficits in <i>KAT6A</i> haploinsufficient mice.

Life science alliance·2026
Same author

Interleukin 4 selectively expands functional type 1 conventional dendritic cells from bone marrow progenitors.

Cell reports·2025
Same author

Dividing out quantification uncertainty enables assessment of differential transcript usage with limma and edgeR.

Nucleic acids research·2025
Same author

MORC2 is a phosphorylation-dependent DNA compaction machine.

Nature communications·2025
Same journal

Model-based quantification of protein-protein interaction aberrations for exploring dysregulated signalling pathways through pathway maps and gene expression levels.

BMC bioinformatics·2026
Same journal

Research on multi-trait genome association study method based on Shannon information entropy.

BMC bioinformatics·2026
Same journal

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

BMC bioinformatics·2026
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
See all related articles

This study introduces a weighted approach for microarray quality assessment, improving gene expression analysis by down-weighting unreliable arrays instead of filtering them. This method enhances the power to detect differential expression, especially in smaller experiments.

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Genetics

Background:

  • Microarray quality assessment is crucial for reliable gene expression analysis.
  • Excluding low-quality arrays (filtering) is a common practice to prevent misleading results.

Purpose of the Study:

  • To develop and evaluate a graduated approach for assessing microarray quality.
  • To improve the detection of differential gene expression by incorporating array quality information.

Main Methods:

  • A heteroscedastic linear model with shared array variance terms was fitted to assign weights to microarrays based on empirical reproducibility.
  • A novel gene-by-gene update algorithm was employed for efficient estimation of array variances.
  • Inverse variances were used as weights in differential expression analysis.

Related Experiment Videos

Main Results:

  • The method successfully assigned lower weights to less reproducible arrays from different experiments.
  • Down-weighting suspect arrays increased the power to detect differential gene expression.
  • This approach outperformed traditional filtering methods in smaller experiments.

Conclusions:

  • The proposed method complements existing normalization and spot quality procedures.
  • It allows the inclusion of poorer quality arrays that might otherwise be discarded.
  • The approach is applicable to microarray data with replication and is available in the limma R package.