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A graphical approach for quality control of oligonucleotide array data.

Dung-Tsa Chen1

  • 1Biostatistics and Bioinformatics Unit, University of Alabama at Birmingham, Birmingham, AL 35294, USA. dtchen@uab.edu

Journal of Biopharmaceutical Statistics
|October 8, 2004
PubMed
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This study introduces a graphical method for assessing oligonucleotide array data quality. The approach effectively identifies incomparable arrays, ensuring reliable gene expression analysis and data integrity.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • Quality control is crucial for oligonucleotide array data to identify and exclude ineligible arrays.
  • Incomparable arrays, often due to poor experimental control, pose a challenge in high-volume gene array studies.
  • Dimensionality reduction without data distortion is necessary for efficient array comparability examination.

Purpose of the Study:

  • To propose a novel graphical approach for examining oligonucleotide array data quality.
  • To develop methods for screening out incomparable arrays in gene expression studies.
  • To provide a tool for quantifying array comparability and verifying statistical findings.

Main Methods:

  • Utilizing percentile methods for data grouping.

Related Experiment Videos

  • Applying 2D image plots for visualizing grouped data.
  • Employing an invariant band to quantify array comparability.

    • Main Results:

    • The proposed graphical approach effectively identifies incomparable arrays.
    • The method demonstrates utility in examining overall data quality.
    • The approach serves as an exploratory tool to validate differentially expressed genes.

    Conclusions:

    • The developed graphical method is valuable for quality control in oligonucleotide array data analysis.
    • This approach aids in identifying and managing incomparable arrays.
    • The tool supports the verification of statistical findings in gene expression studies.