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Related Experiment Videos

Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis

Leming Shi1, Weida Tong, Hong Fang

  • 1National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA. leming.shi@fda.hhs.gov

BMC Bioinformatics
|July 20, 2005
PubMed
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Microarray reliability issues stem from experimental problems and poor data analysis, not platform differences. Improving gene selection methods enhances cross-platform comparability for regulatory use.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Microarray technology's regulatory acceptance is hindered by diverse platforms and analysis methods.
  • A previous study suggested low cross-platform comparability, questioning microarray reliability.

Purpose of the Study:

  • To re-evaluate the cross-platform comparability of microarray data.
  • To identify the primary causes of low concordance in microarray studies.

Main Methods:

  • Reanalyzed existing microarray dataset (Tan et al.).
  • Assessed intra-platform consistency and experimental procedures.
  • Compared three gene selection methods: p-value ranking, fold-change ranking, and Significance Analysis of Microarrays (SAM).

Main Results:

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  • Low intra-platform consistency was observed, suggesting experimental procedural issues.
  • P-value ranking yielded lower cross-platform concordance than fold-change ranking or SAM.
  • Low concordance was attributed to experimental inconsistencies and suboptimal data analysis, not inherent platform differences.

Conclusions:

  • Calibrated RNA samples and reference datasets are crucial for evaluating microarray platform performance.
  • Objective assessment of laboratory proficiency and data analysis methods is necessary.
  • The MicroArray Quality Control (MAQC) project aims to establish community-wide standards for microarray quality.