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

New normalization methods for cDNA microarray data.

D L Wilson1, M J Buckley, C A Helliwell

  • 1CSIRO Mathematical and Information Sciences, Locked Bag 17 North Ryde 1670 NSW, Australia. dwilson@gmp.usyd.edu.au

Bioinformatics (Oxford, England)
|July 23, 2003
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

Altering ventilator inspiratory time can reduce autocycling during sleep.

Sleep medicine·2015
Same author

On the nature of consciousness and of physical reality.

Perspectives in biology and medicine·2015
Same author

Anti-resorptive agents reduce the size of resorption cavities: a three-dimensional dynamic bone histomorphometry study.

Bone·2013
Same author

Obstructive sleep apnea and pregnancy: the effect on perinatal outcomes.

Journal of perinatology : official journal of the California Perinatal Association·2012
Same author

Microbial transformation of 3,4-methylenedioxy-N-methylamphetamine and 3,4-methylenedioxyamphetamine.

Canadian journal of microbiology·2011
Same author

Host-Parasite interactions in Entamoeba histolytica and Entamoeba dispar: what have we learned from their genomes?

Parasite immunology·2011
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·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
See all related articles

This study introduces two novel normalization methods for cDNA microarrays to accurately detect differentially expressed genes. These methods address spatial trends and specialty arrays, improving gene expression analysis.

Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Normalization is crucial for cDNA microarray analysis to remove dye biases and enable accurate gene expression evaluation.
  • Existing non-linear normalization techniques provide a foundation for improved microarray data processing.

Purpose of the Study:

  • To introduce two novel normalization methods for cDNA microarrays.
  • To address challenges in microarray data analysis, including spatial intensity trends and specialty arrays.

Main Methods:

  • Development of two new non-linear normalization techniques for cDNA microarrays.
  • Implementation of methods to handle smooth spatial trends in microarray intensity.
  • Adaptation of normalization for small-scale 'boutique' arrays with high differential gene expression.

Related Experiment Videos

Main Results:

  • The presented methods extend existing non-linear normalization techniques.
  • A novel approach is introduced to manage spatial intensity variations across microarrays.
  • Normalization strategies are developed for specialty cDNA arrays with expected high differential gene expression.

Conclusions:

  • The new normalization methods enhance the accuracy of differential gene expression detection in cDNA microarrays.
  • These techniques are particularly valuable for analyzing complex datasets, including spatial trends and boutique arrays.
  • The developed methods are available in the tRMA software suite for broader application.