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Related Experiment Videos

Analysis of high density expression microarrays with signed-rank call algorithms.

W-m Liu1, R Mei, X Di

  • 1Applied Research and Product Development, Affymetrix, Inc, 3380 Central Expressway, Santa Clara, CA 95051, USA. wei-min_liu@affymetrix.com

Bioinformatics (Oxford, England)
|December 20, 2002
PubMed
Summary

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New rank-based algorithms robustly detect and compare gene expression from high-density oligonucleotide microarrays. Users can adjust parameters for optimal specificity and sensitivity in gene expression analysis.

Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • High-density oligonucleotide microarrays are crucial for gene expression analysis.
  • Accurate detection and comparison of gene expression are essential across experiments.
  • Robustness against outliers and user-adjustable parameters are needed for microarray data.

Purpose of the Study:

  • To develop rank-based algorithms for gene expression detection and comparison calls.
  • To ensure algorithms are robust against data outliers and offer adjustable specificity/sensitivity.
  • To provide reliable gene expression analysis for oligonucleotide microarrays.

Main Methods:

  • Utilized rank-based algorithms employing discrimination scores for detection calls.
  • Employed intensity differences and Wilcoxon's signed-rank test for comparison calls.

Related Experiment Videos

  • Incorporated adjustable parameters, p-value calculations, and novel normalization factors for robust analysis.

    • Main Results:

    • Developed rank-based algorithms for robust gene expression detection and comparison.
    • Algorithms allow user adjustment of specificity and sensitivity levels.
    • Successfully handled scanner saturation and provided confidence levels via p-values.

    Conclusions:

    • The presented rank-based algorithms offer a robust method for gene expression analysis using microarrays.
    • User-adjustable parameters enhance the flexibility of gene expression comparison.
    • These algorithms improve the reliability of detecting and comparing expressed genes across diverse experimental conditions.