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

On differential variability of expression ratios: improving statistical inference about gene expression changes from

M A Newton1, C M Kendziorski, C S Richmond

  • 1Department of Statistics, University of Wisconsin, Madison, WI 53792, USA. newton@stat.wisc.edu

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 8, 2001
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

Needs of paediatric patients with life-limiting disease: abridged secondary publication.

Hong Kong medical journal = Xianggang yi xue za zhi·2024
Same author

Multisystemic smooth muscle dysfunction syndrome: the first local case report.

Hong Kong medical journal = Xianggang yi xue za zhi·2024
Same author

Prevalence of motor problems in children with attention deficit hyperactivity disorder in Hong Kong.

Hong Kong medical journal = Xianggang yi xue za zhi·2016
Same author

Tumourigenic canine osteosarcoma cell lines associated with frizzled-6 up-regulation and enhanced side population cell frequency.

Veterinary and comparative oncology·2015
Same author

Osteosarcoma tissues and cell lines from patients with differing serum alkaline phosphatase concentrations display minimal differences in gene expression patterns.

Veterinary and comparative oncology·2015
Same author

Evaluating maximum likelihood estimation methods to determine the Hurst coeficient.

Physica A·2012
Same journal

Mosquito Species and Gender Identification System Based on Artificial Intelligence and Image Processing Methods.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

This study introduces a new hierarchical model for analyzing gene expression changes from cDNA microarray data, accounting for measurement errors and biological variability to identify significant expression shifts accurately.

Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Accurate inference of gene expression fold changes is crucial for understanding biological processes.
  • Standard microarray analysis methods often overlook inherent variability in intensity ratios.
  • Fluctuations in absolute gene expression levels and measurement errors complicate data interpretation.

Purpose of the Study:

  • To develop a robust statistical model for estimating gene expression fold changes from cDNA microarray data.
  • To improve the identification of significant gene expression alterations by addressing measurement error and biological variation.
  • To provide a more reliable method for analyzing differential gene expression.

Main Methods:

  • A hierarchical statistical model was employed to analyze cDNA microarray data.

Related Experiment Videos

  • The model explicitly accounts for both measurement error and biological variability in gene expression.
  • Posterior odds were derived to identify statistically significant changes in gene expression.

    • Main Results:

    • The proposed model provides more accurate estimates of gene expression fold changes compared to standard ratio-based methods.
    • Simulations demonstrated the effectiveness of the hierarchical model in various scenarios.
    • Application to Escherichia coli microarray data validated the model's practical utility.

    Conclusions:

    • The developed hierarchical model offers a statistically sound approach for analyzing gene expression data from microarrays.
    • This method enhances the reliability of identifying significant gene expression changes, crucial for biological discovery.
    • The approach is applicable to diverse genomic studies utilizing microarray technology.