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Related Experiment Videos

Correlation test to assess low-level processing of high-density oligonucleotide microarray data.

Alexander Ploner1, Lance D Miller, Per Hall

  • 1Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Alexander.Ploner@meb.ki.se

BMC Bioinformatics
|April 1, 2005
PubMed
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Assessing oligonucleotide array data processing is crucial. A new correlation test using gene coregulation effectively evaluates normalization methods like MAS5, RMA, and MBEI, without needing external data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • Oligonucleotide array data analysis involves multiple low-level processing techniques.
  • The selection of these techniques significantly impacts downstream statistical analyses.
  • Currently, no established method exists to evaluate technique suitability for specific datasets without external references.

Purpose of the Study:

  • To develop a method for assessing the appropriateness of low-level processing techniques for oligonucleotide array data.
  • To evaluate the success of normalization procedures on specific datasets.
  • To introduce a novel criterion for quality control in microarray data analysis.

Main Methods:

  • Analysis of gene coregulation using statistical correlation to detect normalization issues.

Related Experiment Videos

  • Application of a correlation test to assess normalization quality across different processing methods (MAS5, RMA, MBEI).
  • Evaluation of a secondary normalization round at the probe set level.

    • Main Results:

    • Housekeeping-gene normalization failed the correlation test across evaluated datasets and processing methods.
    • Significant correlations were observed for absent genes using RMA and MBEI on a clinical dataset.
    • A second normalization round at the probe set level demonstrated significant improvement.

    Conclusions:

    • The correlation criterion offers a robust method for assessing low-level processing appropriateness in the absence of a gold standard.
    • This approach enables data-driven evaluation of normalization success for specific microarray datasets.
    • Findings highlight limitations in standard normalization techniques and suggest improvements.