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Resampling methods for variance estimation of singular value decomposition analyses from microarray experiments.

Debashis Ghosh1

  • 1Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Room M4057, Ann Arbor 48109-2029, USA. ghoshd@umich.edu

Functional & Integrative Genomics
|August 20, 2002
PubMed
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This study introduces the bootstrap method to assess variability in singular value decomposition analyses for gene expression data. This approach provides standard errors for reconstructing genetic networks from microarray time-course studies.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Microarray experiments enable simultaneous gene expression measurement for thousands of genes.
  • Reconstructing genetic networks from time-course microarray data is an active research area.
  • Singular value decomposition (SVD) is a key analytical tool in this field.

Purpose of the Study:

  • To assess the variability associated with singular value decomposition (SVD) analyses.
  • To introduce and evaluate the bootstrap method for calculating standard errors in SVD analyses.
  • To apply these methods to real-world gene expression datasets.

Main Methods:

  • Utilized the bootstrap resampling technique.
  • Applied the bootstrap method to SVD analyses of gene expression data.

Related Experiment Videos

  • Considered scenarios with and without experimental replicates.

    • Main Results:

    • The bootstrap method provides a viable approach for estimating standard errors in SVD analyses.
    • Demonstrated the utility of the bootstrap for SVD in both replicate and non-replicate settings.
    • Successfully applied the methodology to human foreskin and yeast gene expression datasets.

    Conclusions:

    • The bootstrap method is a valuable tool for quantifying uncertainty in SVD-based genetic network reconstruction.
    • This work addresses a gap in understanding the variability of SVD in gene expression analysis.
    • The proposed methods enhance the reliability of genetic network inference from microarray data.