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Probabilistic estimation of microarray data reliability and underlying gene expression.

Sven Bilke1, Thomas Breslin, Mikael Sigvardsson

  • 1Complex Systems Division, Department of Theoretical Physics, University of Lund, Sölvegatan 14A, SE-223 62 Lund, Sweden. sven@thep.lu.se

BMC Bioinformatics
|September 12, 2003
PubMed
Summary
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This study introduces an information theoretic approach for analyzing gene expression data. The method reliably identifies differential gene expression and assesses sample quality in replicated microarray experiments.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput mRNA measurement necessitates reliable data analysis and quality control.
  • Existing methods face challenges in ensuring accuracy and reproducibility.
  • An information theoretic approach is proposed for analyzing discretized gene expression data.

Purpose of the Study:

  • To develop a quantitative method for assessing gene expression reliability.
  • To establish a system for global quality control of gene expression samples and hybridizations.
  • To evaluate the effectiveness of an information theoretic approach in replicated gene expression data analysis.

Main Methods:

  • Utilized an information theoretic approach applied to discretized expression values.

Related Experiment Videos

  • Developed a method to calculate the probability of a gene's biological state given expression data.
  • Introduced sample-specific error probabilities as consistency indicators.

    • Main Results:

    • The approach quantifies gene biological states and sample-specific error probabilities.
    • Tested on murine B-cell gene expression data, outperforming the t-test.
    • Error probabilities correlated well with biological material variations, demonstrating efficiency.

    Conclusions:

    • The proposed information theoretic method effectively determines differential gene expression.
    • It provides reliable sample quality assessment in replicated microarray data.
    • The method is effective even with limited data discretization, comparable to standard techniques.