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

Analysis of large-scale gene expression data.

G Sherlock1

  • 1Department of Genetics, Stanford University Medical Center, Stanford, 94306-5120, USA. sherlock@genome.stanford.edu

Current Opinion in Immunology
|March 14, 2000
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

Microarray karyotyping of maltose-fermenting Saccharomyces yeasts with differing maltotriose utilization profiles reveals copy number variation in genes involved in maltose and maltotriose utilization.

Journal of applied microbiology·2010
Same author

Analysis of large-scale gene expression data.

Briefings in bioinformatics·2002
Same author

Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

Nature genetics·2001
Same author

Missing value estimation methods for DNA microarrays.

Bioinformatics (Oxford, England)·2001
Same author

A whole-genome microarray reveals genetic diversity among Helicobacter pylori strains.

Proceedings of the National Academy of Sciences of the United States of America·2000
Same author

Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

Nature genetics·2000
Same journal

A blind spot of human T cell immunology: epitope specificity in secondary lymphoid organs.

Current opinion in immunology·2026
Same journal

Germinal center responses at barrier organ sites.

Current opinion in immunology·2026
Same journal

Ocular sarcoidosis: from clinical signs to targeted interventions.

Current opinion in immunology·2026
Same journal

On or within: spatial determinants of antigen handling in the nasal turbinates.

Current opinion in immunology·2026
Same journal

Decoding the complexity of intestinal immunity with spatial transcriptomics.

Current opinion in immunology·2026
Same journal

Reconsidering the immunological aspects of solid-phase assays for antiphospholipid antibodies detection.

Current opinion in immunology·2026
See all related articles

New microarray technologies generate vast biological data. Data analysis, using methods like clustering, is now the research bottleneck, shifting focus from data generation to interpretation.

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • cDNA and oligonucleotide microarrays have revolutionized biological research by enabling high-throughput data generation.
  • The increasing volume of data produced by these technologies presents significant analytical challenges.

Purpose of the Study:

  • To highlight the shift in biological research bottlenecks from data generation to data analysis.
  • To introduce common data analysis techniques used for microarray data.

Main Methods:

  • Application of clustering algorithms to analyze large biological datasets.
  • Specific methods discussed include hierarchical clustering, divisive clustering, self-organizing maps, and k-means clustering.

Main Results:

  • Clustering techniques are effective in making sense of complex, high-dimensional microarray data.

Related Experiment Videos

  • These methods aid in identifying patterns and relationships within biological datasets.

    • Conclusions:

    • Data analysis is the primary challenge in current microarray-based biological research.
    • Advanced analytical methods are crucial for extracting meaningful insights from genomic data.