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

Propagating uncertainty in microarray data analysis.

Magnus Rattray1, Xuejun Liu, Guido Sanguinetti

  • 1School of Computer Science, University of Manchester, Manchester M13 9PL, UK. magnus.rattary@manchester.ac.uk

Briefings in Bioinformatics
|June 10, 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

A cell atlas of the developing human outflow tract of the heart and its adult aortic valve derivatives.

eLife·2026
Same author

DeepSynBa: Actionable Drug Combination Prediction with Complete Dose-Response Profiles.

Bioinformatics (Oxford, England)·2026
Same author

Scalable, fast and accurate differential gene expression testing from millions of cells of multiple patients.

Nature communications·2026
Same author

metaAPA: a tool for integration of PolyA site predictions from single-cell and spatial transcriptomics.

Bioinformatics advances·2026
Same author

CRAK-Velo: chromatin accessibility kinetics integration improves RNA velocity estimation.

Genome biology·2026
Same author

Extending differential gene expression testing to handle genome aneuploidy in cancer.

PLoS computational biology·2026
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
See all related articles

This review discusses methods for handling experimental uncertainty in microarray data analysis, focusing on high-density oligonucleotide arrays. Probabilistic approaches using probe sets can improve differential expression identification and data integration from replicated experiments.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • Microarray technology is prone to experimental uncertainty.
  • Accurate data processing is crucial for reliable results.

Purpose of the Study:

  • To review methods for managing uncertainty in microarray data.
  • To focus on high-density oligonucleotide array analysis.

Main Methods:

  • Utilizing multiple probes per target on arrays (e.g., Affymetrix GeneChip).
  • Estimating target concentration and measurement uncertainty.
  • Propagating uncertainty through downstream analysis using probabilistic methods.

Main Results:

  • Demonstrated use of credibility intervals for identifying differential gene expression.

Related Experiment Videos

  • Showcased improved combination of information from replicated experiments.
  • Highlighted enhanced performance in principal component analysis.

    • Conclusions:

    • Probabilistic methods effectively address experimental uncertainty in microarray data.
    • Credibility intervals enhance the reliability of downstream analyses.
    • This approach improves the accuracy of gene expression studies.