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

Two-stage normalization using background intensities in cDNA microarray data.

Dankyu Yoon1, Sung-Gon Yi, Ju-Han Kim

  • 1Seoul National University, San56-l, Shin Lim-Dong, Kwan Ak-Ku, Seoul 151-747, Republic of Korea. avanti@chollian.net

BMC Bioinformatics
|July 23, 2004
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

Bisoprolol vs. Carvedilol After Percutaneous Coronary Intervention in Acute Myocardial Infarction With Reduced Left Ventricular Ejection Fraction.

Circulation journal : official journal of the Japanese Circulation Society·2026
Same author

Enhancing polygenic risk prediction by modeling quantile-specific genetic effects.

Scientific reports·2026
Same author

Analysis of severity in COVID-19 patients by using longitudinal immune profiles.

iScience·2026
Same author

Optoelectronic Synaptic Transistors Based on Colloidal CdSe Nanowires for Energy-Efficient Neuromorphic Computing.

ACS applied materials & interfaces·2026
Same author

[Development and Effectiveness Evaluation of School-based Program Combined School Meal and Nutrition Education].

Jugan geon-gang gwa jilbyeong·2025
Same author

Impact of ACEI/ARB use on COVID-19 mortality in patients with ischaemic heart disease: insights from South Korean National health insurance service data.

BMC infectious diseases·2025
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

A new two-stage normalization method improves microarray data analysis by adjusting for background intensities. This approach enhances gene expression level accuracy, especially when background and signal intensities are strongly related.

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Analysis

Background:

  • Microarray experiments generate systematic variations affecting gene expression levels.
  • Normalization is crucial for accurate microarray data analysis, with background intensity being a major source of variation.
  • Existing methods correct background but do not systematically integrate it into normalization.

Purpose of the Study:

  • To propose a novel two-stage normalization method that incorporates background intensities.
  • To evaluate the performance of this method using various background measures.
  • To compare the proposed method against established normalization techniques.

Main Methods:

  • A two-stage normalization process: first, a regression model adjusts for background intensity effects; second, standard normalization (e.g., LOWESS) is applied.

Related Experiment Videos

  • Evaluation using nine different background measures.
  • Performance comparison with global median and intensity-dependent LOWESS normalization, using variability among replicated slides.

    • Main Results:

    • The proposed two-stage normalization method outperforms global median and LOWESS normalization.
    • Performance improvement is particularly significant when background and signal intensities exhibit a strong relationship.
    • The method is applicable irrespective of the specific background correction technique used in image analysis.

    Conclusions:

    • The two-stage normalization method offers superior performance for microarray data.
    • It effectively addresses variations caused by background intensities.
    • This approach provides a robust and applicable solution for accurate gene expression analysis.