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Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes

Author

Listed:
  • Dørum Guro

    (Norwegian University of Life Sciences)

  • Snipen Lars

    (Norwegian University of Life Sciences)

  • Solheim Margrete

    (Norwegian University of Life Sciences)

  • Saebo Solve

    (Norwegian University of Life Sciences)

Abstract

Gene set analysis methods have become a widely used tool for including prior biological knowledge in the statistical analysis of gene expression data. Advantages of these methods include increased sensitivity, easier interpretation and more conformity in the results. However, gene set methods do not employ all the available information about gene relations. Genes are arranged in complex networks where the network distances contain detailed information about inter-gene dependencies. We propose a method that uses gene networks to smooth gene expression data with the aim of reducing the number of false positives and identify important subnetworks. Gene dependencies are extracted from the network topology and are used to smooth genewise test statistics. To find the optimal degree of smoothing, we propose using a criterion that considers the correlation between the network and the data. The network smoothing is shown to improve the ability to identify important genes in simulated data. Applied to a real data set, the smoothing accentuates parts of the network with a high density of differentially expressed genes.

Suggested Citation

  • Dørum Guro & Snipen Lars & Solheim Margrete & Saebo Solve, 2011. "Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-26, August.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:37
    DOI: 10.2202/1544-6115.1618
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    References listed on IDEAS

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    1. Wei Pan & Benhuai Xie & Xiaotong Shen, 2010. "Incorporating Predictor Network in Penalized Regression with Application to Microarray Data," Biometrics, The International Biometric Society, vol. 66(2), pages 474-484, June.
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    3. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
    4. Rahnenführer Jörg & Domingues Francisco S & Maydt Jochen & Lengauer Thomas, 2004. "Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-31, June.
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    6. Shojaie Ali & Michailidis George, 2010. "Network Enrichment Analysis in Complex Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-36, May.
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    Cited by:

    1. Brisbin Abra & Fridley Brooke L., 2013. "Bayseian genomic models for the incorporation of pathway topology knowledge into association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 505-516, August.

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