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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identifying temporally differentially expressed genes through functional principal components analysis.

Xueli Liu1, Mark C K Yang

  • 1Division of Biostatistics, City of Hope, Duarte, CA 91010-3000, USA. xuliu@coh.org

Biostatistics (Oxford, England)
|July 16, 2009
PubMed
Summary

This study introduces a robust method for analyzing time course gene microarray data, outperforming traditional approaches. It accurately identifies differential gene expression even with irregular time points and missing data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • Time course gene microarrays are crucial for identifying differential gene expression over time.
  • Traditional methods like ANOVA face limitations with irregular time intervals and missing data common in microarray experiments.

Purpose of the Study:

  • To develop a robust statistical method for analyzing longitudinal gene expression data from microarrays.
  • To address challenges posed by irregular time points and missing data in gene expression analysis.

Main Methods:

  • Functional principal components analysis (FPCA) is proposed to test hypotheses regarding changes in mean expression curves.
  • A permutation test is incorporated to enhance the method's robustness under mild assumptions.

Main Results:

  • The proposed FPCA method demonstrated superior performance compared to existing methods like extraction of differential gene expression and 2-way mixed effects ANOVA in simulations.
  • The method was successfully illustrated using real microarray data from blood cells of treated and untreated individuals.

Conclusions:

  • The developed functional principal components analysis offers a more robust and accurate approach for analyzing time course gene microarray data.
  • This method effectively handles irregularities and missing data, improving the identification of differential gene expression patterns.