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Related Experiment Videos

Statistical modeling of large microarray data sets to identify stimulus-response profiles.

L P Zhao1, R Prentice, L Breeden

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, WA 98006, USA. lzhao@fhcrc.org

Proceedings of the National Academy of Sciences of the United States of America
|May 10, 2001
PubMed
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This study introduces a flexible statistical model for analyzing gene expression data, identifying genes with transcriptional responses to stimuli. The method effectively detects periodic gene transcripts in yeast, providing detailed expression profiles.

Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Microarray data analysis requires robust statistical methods to identify genes responding to stimuli.
  • Existing methods may be limited by response timing, magnitude, duration, or transcript abundance.
  • Systematic heterogeneity in expression levels presents a challenge in large-scale data analysis.

Purpose of the Study:

  • To develop a statistical modeling approach for identifying genes with transcriptional responses in large microarray datasets.
  • To create a model accommodating various response characteristics and expression level variations.
  • To apply the model to identify periodically transcribed genes in budding yeast.

Main Methods:

  • Proposed a statistical model for analyzing gene expression data from microarrays.

Related Experiment Videos

  • Developed a model specifically for periodically transcribed genes.
  • Applied objective criteria to identify significant periodicity across multiple datasets.

    • Main Results:

    • The approach identified 81% of known periodic transcripts in budding yeast.
    • 1,088 genes showed significant periodicity in at least one dataset.
    • High confidence periodicity was observed in one-quarter of these genes across multiple datasets.

    Conclusions:

    • The statistical modeling approach is effective for identifying genes with transcriptional responses, including periodic patterns.
    • The method provides gene-specific information and statistical significance evaluation.
    • Detailed expression parameters like activation/deactivation times and expression levels can be estimated for periodic transcripts.