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 Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

2D Ultrasound Elasticity Imaging of Abdominal Aortic Aneurysms Using Deep Neural Networks.

IEEE transactions on computational imaging·2026
Same author

Toward Patient-Specific Partial Point Cloud to Surface Completion for Pre to Intra-operative Registration in Image-Guided Liver Interventions.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)·2026
Same author

Evaluation of Intra-operative Patient-specific Methods for Point Cloud Completion for Minimally Invasive Liver Interventions.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Investigating the Domain Adaptability of General-Purpose Foundation Models for Left Atrium Segmentation from MR Images.

Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference)·2026
Same author

Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Percutaneous renal mass biopsies with no viable lesional cells - Recognizing different histologic patterns can help predict nondiagnostic vs. true negative biopsy and guide clinical management.

Annals of diagnostic pathology·2026
Same journal

CAR-T cell therapy for multiple myeloma: An update on the current state and future potential.

Best practice & research. Clinical haematology·2025
Same journal

Cancer vaccines in hematologic malignancy: A systematic review of the rational and evidence for clinical use.

Best practice & research. Clinical haematology·2025
Same journal

Immune therapy of haematological cancers.

Best practice & research. Clinical haematology·2025
Same journal

Advances in NK cell therapy for multiple myeloma.

Best practice & research. Clinical haematology·2025
Same journal

Adoptive cellular therapies in multiple myeloma.

Best practice & research. Clinical haematology·2025
Same journal

Bispecific T-cell engager therapy for multiple myeloma.

Best practice & research. Clinical haematology·2025
See all related articles

Related Experiment Video

Updated: Jun 20, 2026

Bacterial Gene Expression Analysis Using Microarrays
29:41

Bacterial Gene Expression Analysis Using Microarrays

Published on: May 28, 2007

Analysis of DNA microarray expression data.

Richard Simon1

  • 1Biometric Research Branch, Division of Cancer Treatment & Diagnosis, National Cancer Institute, 9000 Rockville Pike, Bethesda, MD 20892-7434, USA. rsimon@nih.gov

Best Practice & Research. Clinical Haematology
|August 25, 2009
PubMed
Summary
This summary is machine-generated.

DNA microarrays generate vast biological data for disease classification. Proper statistical design and analysis are crucial to avoid misleading claims and ensure reliable findings from these powerful tools.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform
13:14

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform

Published on: August 10, 2009

Related Experiment Videos

Last Updated: Jun 20, 2026

Bacterial Gene Expression Analysis Using Microarrays
29:41

Bacterial Gene Expression Analysis Using Microarrays

Published on: May 28, 2007

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform
13:14

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform

Published on: August 10, 2009

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • DNA microarrays are essential for biological research and clinical applications, offering insights into disease mechanisms.
  • The high-dimensionality of microarray data (more variables than samples) presents significant analytical challenges.
  • Existing literature highlights substantial issues in the statistical analysis of microarray-based studies.

Purpose of the Study:

  • To provide a non-technical overview of key statistical principles for microarray expression profiling studies.
  • To guide researchers in the appropriate design and analysis of experiments using DNA microarrays.
  • To improve the reliability and interpretation of findings from microarray data.

Main Methods:

  • Focuses on the principles of statistical design for case selection in microarray studies.
  • Emphasizes the necessity of specialized statistical methods for analyzing high-dimensional data.
  • Discusses common pitfalls and best practices in microarray data analysis.

Main Results:

  • Microarray studies require rigorous objectives and suitable analytical approaches.
  • Effective analysis necessitates advanced statistical techniques due to the data's complexity.
  • Inadequate analysis can lead to misleading conclusions and unreliable prognostic classifiers.

Conclusions:

  • Adherence to sound statistical design and analysis is paramount for valid microarray research.
  • Specialized statistical methods are indispensable for interpreting complex genomic data.
  • This work aims to enhance the quality and reproducibility of microarray-based scientific discoveries.