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

Improved statistical tests for differential gene expression by shrinking variance components estimates.

Xiangqin Cui1, J T Gene Hwang, Jing Qiu

  • 1The Jackson Laboratory, Bar Harbor, Maine 04609, USA.

Biostatistics (Oxford, England)
|December 25, 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

Genetic architecture of the murine serum metabolome reveals carboxyl esterases as master regulators of circulating fatty acid metabolism.

bioRxiv : the preprint server for biology·2026
Same author

GPX4 regulates lipid peroxidation and ferroptosis of stored red blood cells.

Blood. Red cells & iron·2026
Same author

Contrasting the genetic architecture of cardiac glutathione against other organs: unveiling a unique tissue-specific locus.

Mammalian genome : official journal of the International Mammalian Genome Society·2026
Same author

Distinct genetic architecture of gene and isoform level QTL in the Diversity Outbred (DO) mouse population.

bioRxiv : the preprint server for biology·2026
Same author

Genetic regulation of fasting-induced longevity effects.

Genetics·2026
Same author

Longitudinal analysis of body weight reveals homeostatic and adaptive traits linked to lifespan in diversity outbred mice.

Nature communications·2026
Same journal

Risk estimation and dynamic prediction using discrete-time joint models for longitudinal and multistate data with interval and state censoring.

Biostatistics (Oxford, England)·2026
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
See all related articles

This study introduces a new statistical method, the FS-test, to improve the detection of differentially expressed genes in microarray analysis. The FS-test enhances statistical power by borrowing information across genes, offering a robust approach.

Area of Science:

  • Genomics
  • Statistical genetics
  • Bioinformatics

Background:

  • Microarray data analysis requires robust statistical methods due to limited data points per gene.
  • Existing methods for detecting differential gene expression have limitations in power and robustness.

Purpose of the Study:

  • To develop a novel statistical approach for analyzing microarray data.
  • To enhance the detection of differentially expressed genes by leveraging information across multiple genes.

Main Methods:

  • Developed an error variance estimator using the James-Stein shrinkage concept to borrow information across genes.
  • Constructed a new test statistic (FS) based on the shrinkage-based error variance estimator.
  • Compared the performance of the FS-test against existing statistics (F1, F3, F2, regularized t, B, SAM t-test) using simulated data.

Related Experiment Videos

Main Results:

  • The FS-test demonstrated superior or near-superior power in detecting differentially expressed genes across various simulated datasets.
  • The FS-test performed well under both homogeneous and heterogeneous variance conditions.
  • The proposed method effectively utilizes cross-gene information, unlike individual gene testing approaches.

Conclusions:

  • The FS-test offers a powerful and robust method for identifying differentially expressed genes in microarray studies.
  • This approach overcomes limitations of traditional methods by incorporating shared variance information.
  • The FS-test provides a valuable tool for genomic data analysis, improving the reliability of differential expression findings.