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 for microarray assays.

Paweł Krajewski1, Jan Bocianowski

  • 1Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland. pkra@igr.poznan.pl

Journal of Applied Genetics
|August 15, 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

Identification of high-yielding and stable genotypes of barley in the cold climate of Iran using AMMI and GGE biplot models.

Scientific reports·2026
Same author

Application of NGS Technology, Association Mapping, and Physical Mapping Technologies to Identify Candidate Genes Associated with Maize (<i>Zea mays</i> L.) Hybrid Yield.

International journal of molecular sciences·2026
Same author

Integrating AMMI, Bayesian-AMMI, BLUP, and GGE biplot models to improve the identification of high-yielding and stable barley genotypes.

BMC plant biology·2026
Same author

Biocontrol of <i>Fusarium</i> and Other Fungal Diseases of Cereals Using Bacterial Compounds and Plant Extracts.

Molecules (Basel, Switzerland)·2026
Same author

Ultrastructural changes and defense strategy of yellow lupine during silver nanoparticle and Fusarium oxysporum interaction.

Scientific reports·2026
Same author

An investigation of the major volatile organic compounds released by maize plants following the application of methyl jasmonate and Z-jasmone.

Journal of plant physiology·2026
Same journal

Molecular and bioinformatic analysis of apoptotic tumor suppressor genes in cervical cancer.

Journal of applied genetics·2026
Same journal

Analysis of genetic risk factors for Leber hereditary optic neuropathy in the Polish population.

Journal of applied genetics·2026
Same journal

DUS based morphological evaluation of bell pepper (Capsicum annuum L. var. grossum Sendt.) germplasm for identification of promising breeding lines in Himachal Pradesh, India.

Journal of applied genetics·2026
Same journal

Non-coding RNAs in peripheral vascular diseases - a snRNA study.

Journal of applied genetics·2026
Same journal

Distribution of mrk genes among uopathogenic Klebsiella pneumoniae.

Journal of applied genetics·2026
Same journal

Maize chromatin introgression's effect in oat × maize addition lines.

Journal of applied genetics·2026
See all related articles

This review covers statistical methods for DNA microarray studies, from planning to validation. It highlights the need for standardized protocols and integrating analysis with quantitative genetics.

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Genomics

Background:

  • DNA microarray technology enables large-scale gene expression analysis.
  • Statistical methods are crucial for interpreting complex microarray data.
  • Comprehensive statistical approaches are needed across all experimental stages.

Purpose of the Study:

  • To review statistical methodologies employed in DNA microarray studies.
  • To cover all experimental phases: planning, data collection, preprocessing, analysis, and validation.
  • To emphasize the importance of robust statistical protocols.

Main Methods:

  • Review of statistical algorithms for differential gene expression estimation.
  • Exploration of multivariate approaches for complex data patterns.

Related Experiment Videos

  • Discussion of clustering, classification, and discrimination techniques.
  • Consideration of experimental design and data quality control.

    • Main Results:

    • Identified key statistical methods applicable to DNA microarray experiments.
    • Highlighted the necessity for standardized data processing protocols.
    • Emphasized the gap between current analysis and quantitative genetic models.

    Conclusions:

    • Standardized statistical protocols are essential for reliable microarray data analysis.
    • Further research should focus on linking microarray data analysis with quantitative genetic models.
    • Integrated statistical approaches enhance the biological insights from gene expression studies.