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

Using biplots to interpret gene expression patterns in plants.

Scott Chapman1, Peer Schenk, Kemal Kazan

  • 1CSIRO Plant Industry, Long Pocket Laboratories, 120 Meiers Rd, Indooroopilly 4068, Australia. scott.chapman@csiro.au

Bioinformatics (Oxford, England)
|February 12, 2002
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

From plots to commercial fields: scalable, transferable cotton morphology and productivity estimation using functional growth proxies from UAV and PlanetScope time series.

Plant phenomics (Washington, D.C.)·2026
Same author

A Large-Scale In-the-wild Dataset for Plant Disease Segmentation.

Scientific data·2026
Same author

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset.

Plant phenomics (Washington, D.C.)·2025
Same author

Phenotyping the hidden half: combining UAV phenotyping and machine learning to predict barley root traits in the field.

Journal of experimental botany·2025
Same author

Increased Incidence of Vestibular Disorders in Patients With SARS-CoV-2.

Otology & neurotology open·2024
Same author

Reintroducing face-to-face support alongside remote support to form a hybrid stop smoking service in England: a formative mixed methods evaluation.

BMC public health·2024
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

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

This study demonstrates how Principal Component Analysis (PCA) and Gabriel

Area of Science:

  • Plant biology
  • Ecology
  • Evolution
  • Genetics
  • Breeding
  • Plant pathology

Background:

  • Multivariate statistical methods are essential tools for plant biologists across various disciplines.
  • Microarray expression data provides complex, high-dimensional information crucial for understanding plant responses.

Purpose of the Study:

  • To illustrate the application of Principal Component Analysis (PCA).
  • To demonstrate the utility of Gabriel's biplot.
  • To showcase these methods using plant pathology microarray data.

Main Methods:

  • Principal Component Analysis (PCA) for dimensionality reduction.
  • Gabriel's biplot for visualizing multivariate data relationships.

Related Experiment Videos

  • Application to gene expression data from plant pathology experiments.

    • Main Results:

    • PCA effectively reduces the dimensionality of microarray expression data.
    • Gabriel's biplot facilitates the interpretation of complex expression patterns and relationships.
    • These methods reveal key insights into plant responses in pathology contexts.

    Conclusions:

    • PCA and Gabriel's biplot are powerful, accessible multivariate techniques for plant biology research.
    • These methods enhance the analysis and interpretation of high-throughput plant expression data.
    • Recommended for researchers in plant ecology, evolution, genetics, breeding, and pathology.