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Microarray blob-defect removal improves array analysis.

Jun S Song1, Kaveh Maghsoudi, Wei Li

  • 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA.

Bioinformatics (Oxford, England)
|March 3, 2007
PubMed
Summary
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Blob-like defects on Affymetrix microarrays reduce data accuracy. A new tool, microarray blob remover (MBR), effectively removes these defects, significantly improving sensitivity and reducing false discovery rates in tiling array analysis.

Area of Science:

  • Genomics
  • Bioinformatics
  • Microarray Technology

Background:

  • Affymetrix oligonucleotide microarrays can exhibit blob-like image defects.
  • These defects necessitate repeating experiments or analyzing compromised data.
  • Investigated the impact of simulated blobs on ChIP-chip tiling array analysis.

Purpose of the Study:

  • To assess the effect of blob defects on spike-in target prediction accuracy.
  • To develop and evaluate a software tool for removing microarray image defects.

Main Methods:

  • Simulated blob defects (1-9% array area) on Affymetrix ENCODE tiling arrays.
  • Analyzed data using Affymetrix tiling array software (TAS) and model-based analysis of tiling arrays (MAT).
  • Developed and applied the microarray blob remover (MBR) software to .CEL files.

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Main Results:

  • Blob defects significantly decreased sensitivity and increased false discovery rate (FDR) in tiling array analysis.
  • MBR software enabled rapid visualization, detection, and removal of blob defects.
  • Using MBR significantly improved sensitivity and FDR compared to uncorrected data.

Conclusions:

  • Microarray blob defects negatively impact data quality and analysis reliability.
  • MBR software provides an effective solution for mitigating the effects of blob defects.
  • MBR improves the accuracy and reliability of Affymetrix tiling array data analysis.