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

Statistical methods in cardiac gene expression profiling: from image to function.

Sek Won Kong1

  • 1Department of Cardiology, Children's Hospital Boston, Harvard Medical School, Boston, MA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|June 15, 2007
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

Safeguarding genomic integrity in pluripotent stem-cell therapies.

Lancet (London, England)·2026
Same author

Lithium and the Brain-Bone Axis: A Bridge between Osteoporosis and Alzheimer's Disease.

Current osteoporosis reports·2026
Same author

A sex-specific genome-wide association study of blood lipid levels in All of Us.

medRxiv : the preprint server for health sciences·2025
Same author

Genetic Landscape of Opsoclonus-Myoclonus-Ataxia Syndrome in Children.

Pediatric neurology·2025
Same author

Artificial intelligence in pediatric healthcare: current applications, potential, and implementation considerations.

Clinical and experimental pediatrics·2025
Same author

Neuropilin-2 functions as a coinhibitory receptor to regulate antigen-induced inflammation and allograft rejection.

The Journal of clinical investigation·2025
Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Microarray data analysis simplifies complex gene expression profiles. This guide covers background correction, normalization, and statistical methods for accurate interpretation of gene expression data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Microarray technology offers genome-scale gene expression data, widely used in clinical medicine.
  • Analysis and interpretation of this data present significant challenges.
  • Standardized strategies are needed for reliable microarray data analysis.

Purpose of the Study:

  • To outline comprehensive strategies for microarray data analysis.
  • To detail methods for processing scanned microarray images into quantifiable transcript abundance.
  • To explain techniques for minimizing technical variability and systematic biases.

Main Methods:

  • Image processing and summarization of microarray data.
  • Background correction and normalization to reduce technical variability.

Related Experiment Videos

  • Gene filtering, statistical comparison, cluster analysis, and data visualization.

    • Main Results:

    • Accurate numerical values representing transcript abundance are obtained.
    • Technical variability and systematic biases are minimized through proper procedures.
    • Coexpressed genes are identified and grouped by biological function and cellular location.

    Conclusions:

    • Effective microarray data analysis involves image processing, normalization, and statistical methods.
    • Coexpressed genes can be identified and functionally categorized.
    • Integrating prior knowledge with statistical results enables robust inference from gene expression profiles.