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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...

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data.

James J Chen1, Huey-Miin Hsueh, Robert R Delongchamp

  • 1Division of Personalized Nutrition and Medicine, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, USA. jamesj.chen@fda.hhs.gov

BMC Bioinformatics
|October 27, 2007
PubMed
Summary

Microarray gene expression data show significant variability across platforms and sites, impacting reliability. Statistical methods reveal inconsistencies, highlighting the need for careful data analysis to avoid false positives in regulatory decisions.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Microarray Technology

Background:

  • Concerns exist regarding the comparability and reliability of microarray gene expression data.
  • The MicroArray Quality Control (MAQC) project offers insights into inter- and intra-platform reproducibility.
  • Previous MAQC analysis of comparability and reproducibility was deemed inadequate.

Purpose of the Study:

  • To evaluate the reproducibility and comparability of microarray gene expression data from the MAQC project.
  • To assess 12,901 common genes across four titration samples using five microarray platforms and TaqMan technology.
  • To critique the use of correlation coefficients and percent of overlapping genes (POG) as metrics for evaluating reproducibility and gene selection.

Main Methods:

  • Analysis of 293 arrays for inter- and intra-platform reproducibility.
  • Hierarchical cluster analysis to identify differences in measured intensities among platforms.
  • Analysis of variance (ANOVA) to detect inconsistent gene expression patterns.

Main Results:

  • Hierarchical clustering revealed distinct intensity differences among the five microarray platforms.
  • Thirty percent of gene expressions showed inconsistent patterns across platforms, despite high correlations.
  • Percent of Overlapping Genes (POG) was found to be an unreliable metric for gene list accuracy and selection.

Conclusions:

  • Significant differences in measured intensities exist between platforms and even between sites within the same platform.
  • While expression patterns are generally consistent within platforms, site-to-site variability is present.
  • Employing a fold-change cutoff with a non-stringent p-value cutoff can lead to 100% false positive error selection when no treatment effect is present.