Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

DNA Microarrays

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...

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

You might also read

Related Articles

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

Sort by

Same author

Multi-modal data integration reveals functionally credible predictive biomarkers in ovarian cancer.

Genome medicine·2026

Same author

Household income during childhood and risk of youth suicide attempts and deaths: a register-based study from Finland.

Social psychiatry and psychiatric epidemiology·2026

Same author

Identification of monotonically classifying pairs of genes for ordinal disease outcomes.

Bioinformatics advances·2026

Same author

A multi-center study on the consistency of drug sensitivity testing in patients with acute myeloid leukemia.

NPJ precision oncology·2026

Same author

Uncovering the similarities of lipidome-wide markers of carotid artery plaque and metabolic dysfunction-associated fatty liver disease: the Young Finns study.

Scientific reports·2026

Same author

FUSE: data-driven functional segmentation of DNA methylation data.

Bioinformatics (Oxford, England)·2026

Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 25, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations.

Reija Autio¹, Sami Kilpinen, Matti Saarela

¹Department of Signal Processing, Tampere University of Technology, Tampere, Finland. reija.autio@tut.fi

BMC Bioinformatics

|February 12, 2009

Summary

This summary is machine-generated.

Comparing gene expression microarray data is challenging. Array Generation based gene Centering (AGC) normalization effectively integrates Affymetrix datasets across different array generations and labs, improving data comparability.

More Related Videos

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Published on: August 5, 2008

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Related Experiment Videos

Last Updated: Jun 25, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Published on: August 5, 2008

Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Gene expression microarray technologies are vital in biological and medical research.
Integrating data across different experiments is difficult due to variations in experimental conditions and normalization methods.
Different generations of Affymetrix microarrays pose challenges for data integration due to distinct probe sets.

Purpose of the Study:

To identify systematic approaches for normalizing Affymetrix gene expression data from diverse array generations and laboratories.
To compare and evaluate the accuracy of five distinct normalization methods.

Main Methods:

The study analyzed 6,926 Affymetrix experiments across five array generations.
Five normalization methods were compared: standardization, housekeeping gene normalization, equalized quantile normalization, Weibull distribution normalization, and Array Generation based gene Centering (AGC).
Data normalization was performed first within samples, then between samples using AGC.

Main Results:

Array Generation based gene Centering (AGC) normalization yielded the best results.
AGC normalization significantly enhances the comparability of gene expression values across different Affymetrix array generations.
This method proved superior for normalizing data from multiple array generations, enabling comparable gene values across thousands of samples.

Conclusions:

The AGC method enables significantly more comparable gene expression values across array generations compared to methods without array generation-specific normalization.
AGC is the optimal method for normalizing data from diverse Affymetrix array generations.
This approach facilitates the integration of large-scale gene expression datasets.