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Time ordering of gene coexpression.

Xiaoyan Leng1, Hans-Georg Müller

  • 1Division of Public Health Sciences, Department of Biostatistical Sciences, Wake Forest University School of Medicine, Medical Center Boulevard, MRI-3, Winston-Salem, NC 27157, USA. ileng@wfubmc.edu

Biostatistics (Oxford, England)
|February 24, 2006
PubMed
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This study introduces a novel method to determine gene expression time ordering using temporal microarray data. The approach successfully distinguishes gene categories and reveals natural gene timelines, aiding pathway discovery.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene expression profiles reveal gene function through coexpression dynamics.
  • Understanding temporal gene relationships is crucial for biological pathway analysis.

Purpose of the Study:

  • To define and estimate a global time shift characteristic for each gene from temporal gene expression data.
  • To develop a method for inferring time ordering of genes within biological processes.
  • To apply and validate the method using diverse biological datasets and simulations.

Main Methods:

  • Least squares estimation applied to pairwise curve alignments of gene expression profiles.
  • Inference of individual gene time shifts to establish a global time ordering.
  • Clustering and analysis of time-shifted gene expression data.

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Main Results:

  • Successfully recovered time ordering in simulated and real biological data (Drosophila development, yeast cell cycle).
  • Demonstrated excellent discrimination between Drosophila maternal and zygotic genes based on estimated time shifts.
  • Established a natural gene time order for the yeast cell cycle, aligning with known phases.

Conclusions:

  • The proposed method effectively infers gene time ordering from temporal expression profiles without requiring periodicity.
  • The technique aids in confirming known genetic pathways and discovering new temporal relationships.
  • This approach provides a valuable tool for analyzing gene dynamics in developmental and cell-cycle processes.