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Improved background correction for spotted DNA microarrays.

Charles Kooperberg1, Thomas G Fazzio, Jeffrey J Delrow

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, MP 1002, Seattle, WA 98109-1024, USA. clk@fhcr.org.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|March 26, 2002
PubMed
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This study reveals that common background correction methods for microarray data can inflate expression level variance, particularly for low expression levels. This impacts the reliability of downstream analyses like clustering.

Area of Science:

  • Genomics
  • Bioinformatics
  • Microarray Analysis

Background:

  • Microarray scanning software typically quantifies foreground and background intensities for two channels per spot.
  • A standard preprocessing step involves subtracting background from foreground intensity and calculating the ratio for expression level estimation.
  • This ratio, after averaging replicates, serves as input for downstream analyses such as clustering.

Purpose of the Study:

  • To critically evaluate the common background correction method used in microarray data analysis.
  • To demonstrate how this preprocessing impacts the variance of expression level estimates, especially at low expression levels.

Main Methods:

  • Analysis of standard microarray data preprocessing pipelines.
  • Theoretical assessment of the impact of background subtraction on variance estimation.

Related Experiment Videos

  • Examination of data loss due to foreground intensity being less than background intensity.

    • Main Results:

    • The conventional background correction method can significantly increase the variance of expression estimates when expression levels are low.
    • Spots with low expression may be discarded if foreground intensity falls below background intensity, leading to data loss.
    • This increased variance can compromise the accuracy and reliability of subsequent analyses.

    Conclusions:

    • The widely used background correction method for spotted microarrays is suboptimal for low expression levels, leading to inflated variance.
    • Alternative preprocessing strategies should be considered to improve the accuracy of expression level estimation and minimize data loss.
    • Researchers should be aware of these limitations when interpreting microarray data analyzed with standard background correction techniques.