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Related Experiment Videos

Effect of various normalization methods on Applied Biosystems expression array system data.

Catalin C Barbacioru1, Yulei Wang, Roger D Canales

  • 1Molecular Biology Division, Applied Biosystems, Foster City, CA 94404, USA. catalin.barbacioru@appliedbiosystems.com

BMC Bioinformatics
|December 19, 2006
PubMed
Summary
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Normalization methods for DNA microarrays show high concordance, ensuring reliable gene expression analysis. This robustness supports their use in clinical and regulated research settings, enhancing data accuracy and reproducibility.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • DNA microarray technology enables genome-wide gene expression profiling.
  • Clinical application of microarrays is hindered by concerns regarding reproducibility, reliability, and standardization.
  • This study addresses these issues using Applied Biosystems Human Genome Survey Microarrays.

Purpose of the Study:

  • To evaluate the impact of different normalization methods on microarray data analysis.
  • To assess the reproducibility, reliability, and accuracy of gene expression profiling using microarrays.
  • To determine the robustness of current microarray analysis methods for clinical and regulatory applications.

Main Methods:

  • Gene expression profiles of brain and universal human reference (UHR) samples were analyzed.

Related Experiment Videos

  • Five normalization methods (quantile, median, scale, VSN, cyclic loess) were applied to Applied Biosystems microarray data.
  • Real-time PCR (TaqMan assays) was used for validation of 1,000 selected genes.

    • Main Results:

    • High concordance was observed among the five normalization methods across various metrics including signal detection, variation, fold change, and reproducibility.
    • Normalization methods showed minimal impact on true positive (TP) and false positive (FP) rates in detecting differentially expressed genes.
    • Analysis revealed robustness in current microarray analysis methods, especially with the Applied Biosystems Gene Expression System.

    Conclusions:

    • Normalization methods for DNA microarrays demonstrate high concordance, supporting their reliability.
    • The robustness of these methods enhances confidence in their application for clinical and regulatory decision-making.
    • Applied Biosystems microarrays and associated analysis systems provide reliable gene expression data.