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

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...

You might also read

Related Articles

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

Sort by
Same author

Genetic architecture of the murine serum metabolome reveals carboxyl esterases as master regulators of circulating fatty acid metabolism.

bioRxiv : the preprint server for biology·2026
Same author

GPX4 regulates lipid peroxidation and ferroptosis of stored red blood cells.

Blood. Red cells & iron·2026
Same author

Contrasting the genetic architecture of cardiac glutathione against other organs: unveiling a unique tissue-specific locus.

Mammalian genome : official journal of the International Mammalian Genome Society·2026
Same author

Distinct genetic architecture of gene and isoform level QTL in the Diversity Outbred (DO) mouse population.

bioRxiv : the preprint server for biology·2026
Same author

Genetic regulation of fasting-induced longevity effects.

Genetics·2026
Same author

Longitudinal analysis of body weight reveals homeostatic and adaptive traits linked to lifespan in diversity outbred mice.

Nature communications·2026

Related Experiment Video

Updated: Jun 28, 2026

An Automated Culture System for Maintaining and Differentiating Human-Induced Pluripotent Stem Cells
06:11

An Automated Culture System for Maintaining and Differentiating Human-Induced Pluripotent Stem Cells

Published on: January 26, 2024

Randomization in laboratory procedure is key to obtaining reproducible microarray results.

Hyuna Yang1, Christina A Harrington, Kristina Vartanian

  • 1The Jackson Laboratory, Bar Harbor, ME, USA.

Plos One
|November 15, 2008
PubMed
Summary

Reproducibility of gene expression microarray data is a concern. Batch effects in sample processing significantly impact results, necessitating randomization to avoid confounding biological findings with procedural artifacts.

More Related Videos

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Performing Custom MicroRNA Microarray Experiments
07:04

Performing Custom MicroRNA Microarray Experiments

Published on: October 28, 2011

Related Experiment Videos

Last Updated: Jun 28, 2026

An Automated Culture System for Maintaining and Differentiating Human-Induced Pluripotent Stem Cells
06:11

An Automated Culture System for Maintaining and Differentiating Human-Induced Pluripotent Stem Cells

Published on: January 26, 2024

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Performing Custom MicroRNA Microarray Experiments
07:04

Performing Custom MicroRNA Microarray Experiments

Published on: October 28, 2011

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Gene expression microarray technology has advanced significantly.
  • Reproducibility of data across different laboratories remains a challenge.
  • Combining data from public repositories requires reliable and consistent results.

Purpose of the Study:

  • To investigate the reproducibility of gene expression microarray data across multiple laboratories.
  • To identify sources of variability in microarray data generation.
  • To assess the impact of sample processing on analysis outcomes.

Main Methods:

  • A common set of RNA samples was analyzed five times in four distinct laboratories.
  • Affymetrix GeneChip arrays were utilized for gene expression profiling.
  • Statistical analysis was performed to identify sources of variation.

Main Results:

  • Significant differences in gene expression results were observed between laboratories.
  • Batch effects in array processing were identified as a primary cause of these discrepancies.
  • Confounding of batch processing with experimental factors can lead to artifactual gene lists.

Conclusions:

  • Sample processing has a substantial impact on microarray analysis results.
  • Randomization in laboratory procedures is crucial to prevent confounding of biological factors with procedural effects.
  • Ensuring consistent sample handling is vital for reliable gene expression data.