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Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray data.

Koji Kadota1, Tomokazu Konishi, Kentaro Shimizu

  • 1Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. kadota@iu.a.u-tokyo.ac.jp

Gene Regulation and Systems Biology
|November 26, 2009
PubMed
Summary
This summary is machine-generated.

The AIC-based method robustly identifies tissue-selective genes using DNA microarrays, outperforming Sprent's method, especially with fewer samples. This validates its use in the ROKU method for gene expression analysis.

Keywords:
AICdifferential expressionmicroarraytissue selectivity

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • DNA microarrays facilitate large-scale expression profiling to identify tissue-selective genes.
  • Outlier-detection methods, including AIC-based and Sprent's non-parametric, are used for this purpose but yield different results.

Purpose of the Study:

  • To investigate the robustness of AIC-based and Sprent's non-parametric methods for detecting tissue-selective gene expression patterns.
  • To evaluate method performance under varying parameter settings and reduced sample sizes.

Main Methods:

  • Applied AIC-based and Sprent's non-parametric outlier-detection methods to public microarray data from 36 normal human tissues.
  • Analyzed the impact of parameter adjustments and sample number reduction on method robustness.

Main Results:

  • The AIC-based method demonstrated superior robustness compared to Sprent's method across different parameter settings and sample sizes.
  • Sprent's method showed lower reliability, particularly when the number of samples was reduced.

Conclusions:

  • The AIC-based method is a more robust choice for identifying tissue-selective gene expression patterns.
  • Confirms the suitability of the AIC-based method within the ROKU framework and highlights limitations of Sprent's method for this application.