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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

You might also read

Related Articles

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

Sort by
Same author

Development of a Novel Internal Fixation Model for Rat Radial Fractures: Fracture Healing Assessment and Dorsal Root Ganglion Isolation.

Journal of visualized experiments : JoVE·2026
Same author

The effects of remimazolam on emergence agitation in patients undergoing nasal surgery: a clinical randomized controlled trial.

PeerJ·2026
Same author

Bioactive Platinum Nanozymes Accelerate Diabetic Wound Healing via Anti-Inflammation and Macrophage Polarization Modulation.

International journal of nanomedicine·2026
Same author

Large-scale paired chain BCR analysis reveals antibody clonal family inference bias and enhances resolution with machine learning.

PLoS computational biology·2026
Same author

Atractylodis Rhizoma-Atractylodis Macrocephala Rhizoma herbal pair restores intestinal mucosal barrier function in ulcerative colitis via activating Epac1/Rap1 pathway and inhibiting PI3K/AKT pathway.

Chinese journal of natural medicines·2026
Same author

Inflammatory Bowel Disease Burden in Six Different Dietary Regions and Worldwide.

Digestive diseases (Basel, Switzerland)·2026
Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: May 14, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Aggregation effect in microarray data analysis.

Linlin Chen1, Anthony Almudevar, Lev Klebanov

  • 1School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, USA.

Methods in Molecular Biology (Clifton, N.J.)
|February 7, 2013
PubMed
Summary
This summary is machine-generated.

Researchers must understand microarray data limitations for accurate gene regulatory network analysis. This study highlights common pitfalls to improve network inference and pathway association studies.

More Related Videos

Automating Aggregate Quantification in Caenorhabditis elegans
07:50

Automating Aggregate Quantification in Caenorhabditis elegans

Published on: October 14, 2021

Measuring Transcellular Interactions through Protein Aggregation in a Heterologous Cell System
04:47

Measuring Transcellular Interactions through Protein Aggregation in a Heterologous Cell System

Published on: May 22, 2020

Related Experiment Videos

Last Updated: May 14, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Automating Aggregate Quantification in Caenorhabditis elegans
07:50

Automating Aggregate Quantification in Caenorhabditis elegans

Published on: October 14, 2021

Measuring Transcellular Interactions through Protein Aggregation in a Heterologous Cell System
04:47

Measuring Transcellular Interactions through Protein Aggregation in a Heterologous Cell System

Published on: May 22, 2020

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Gene regulatory network inference from microarray data is a rapidly growing field.
  • Numerous challenges and potential pitfalls exist in network analysis.
  • A comprehensive understanding of these issues is crucial for reliable results.

Purpose of the Study:

  • To critically discuss common pitfalls in gene regulatory network analysis using microarray data.
  • To raise awareness among investigators about specific data features that can affect network inference.
  • To improve the accuracy of identifying associations between network elements and biological pathways.

Main Methods:

  • Review and critical discussion of existing methodologies for gene regulatory network inference.
  • Analysis of specific characteristics of microarray data relevant to network analysis.
  • Identification of potential biases and limitations inherent in the data.

Main Results:

  • Microarray data possesses inherent features that can lead to misinterpretations in network analysis.
  • Failure to account for these features can result in inaccurate identification of gene associations.
  • Understanding these data characteristics is vital for robust network construction.

Conclusions:

  • Investigators must be cognizant of specific microarray data properties to avoid analytical pitfalls.
  • Awareness of these limitations enhances the reliability of inferred gene regulatory networks.
  • This work provides essential insights for researchers studying gene networks and biological pathways.