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

Quality optimised analysis of general paired microarray experiments.

Erik Kristiansson1, Anders Sjögren, Mats Rudemo

  • 1Mathematical Statistics, Chalmers University of Technology. erikkr@math.chalmers.se

Statistical Applications in Genetics and Molecular Biology
|May 2, 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

Reply to Bonomo and Hujer, "Enhancing clinical utility of AI-based antimicrobial resistance models: a perspective".

mBio·2026
Same author

Antibiotic resistance gene analyses in microbial communities: challenges and opportunities.

Nature communications·2026
Same author

Antibiotic resistance gradient along a large Scandinavian river influenced by wastewater treatment plants.

FEMS microbiology ecology·2026
Same author

Confidence-based prediction of antibiotic resistance at the patient level.

mBio·2026
Same author

Strain-Level Typing of <i>Streptococcus pyogenes</i> Using Optical DNA Mapping.

ACS infectious diseases·2025
Same author

Data limitations hinder the development of AI-based decision support for the treatment of antibiotic-resistant bacteria.

Future microbiology·2025
Same journal

Balanced mediated pathway detection in genomic data.

Statistical applications in genetics and molecular biology·2026
Same journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
See all related articles

This study introduces a new linear model for paired microarray experiments, improving quality control and providing more accurate p-values. The method enhances statistical analysis for complex biological data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray experiments are prone to quality issues, necessitating robust quality control measures.
  • Complex experimental designs often involve multiple conditions and correlated data, challenging standard statistical analyses.

Purpose of the Study:

  • To propose a generalized linear model for paired microarray experiments to address quality variations and correlations.
  • To improve the accuracy of statistical inference and p-value estimation in microarray data analysis.

Main Methods:

  • A linear model generalizing the paired two-sample method, incorporating quality variation through different variance scales.
  • Modeling shared sources of variation using covariances between arrays.
  • Employing an empirical Bayes approach for moderating gene-wise variance estimates.

Related Experiment Videos

Main Results:

  • The proposed model effectively handles unequal variances and strong correlations observed in real microarray data.
  • Empirical distributions of test statistics show improved agreement with null distributions compared to previous methods.
  • The method demonstrates superior performance over alternatives in simulation studies, yielding more accurate p-values.

Conclusions:

  • The developed linear model offers a more statistically sound approach for analyzing paired microarray data, especially under complex conditions.
  • Accurate p-value estimation is crucial, as standard assumptions of independence and identical variances can lead to inflated significance.
  • This approach enhances the reliability of findings from microarray experiments, contributing to better biological insights.