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Significance analysis of time course microarray experiments.

John D Storey1, Wenzhong Xiao, Jeffrey T Leek

  • 1Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. jstorey@u.washington.edu

Proceedings of the National Academy of Sciences of the United States of America
|September 6, 2005
PubMed
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A new method accurately identifies differentially expressed genes in time course studies. This approach successfully found thousands of genes changing over time in human studies, outperforming existing methods.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Understanding genome-wide dynamic gene expression regulation is crucial.
  • Existing methods are insufficient for analyzing time course gene expression data.

Purpose of the Study:

  • To develop and validate a novel statistical method for identifying differentially expressed genes in time course microarray studies.
  • To apply the method to human studies investigating gene expression changes in response to endotoxin and aging.

Main Methods:

  • A significance method for analyzing time course microarray data was developed.
  • The method accommodates typical comparisons and sampling schemes.
  • It was implemented in the open-source 'edge' software package.

Related Experiment Videos

Main Results:

  • In an endotoxin study, 7,409 genes showed differential expression over time at a 1% false-discovery rate.
  • In a kidney cortex aging study, 417 genes exhibited age-related expression changes at a 10% false-discovery rate.
  • Complex expression patterns, beyond simple exponential changes, were observed in 47% of age-related genes.

Conclusions:

  • The proposed method effectively identifies differentially expressed genes in time course studies.
  • It offers superior performance compared to existing approaches.
  • The 'edge' software package provides a freely available tool for this analysis.