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A test for detecting differential indirect trans effects between two groups of samples

Author

Listed:
  • Chaturvedi Nimisha
  • Menezes Renée X. de
  • Wieringen Wessel van

    (Afdeling Epidemiologie en Biostatistiek, Amsterdam Public Health Research Institute, Medische Faculteit (F-vleugel), VU Medisch Centrum, 1007 MB Amsterdam, The Netherlands)

  • Goeman Jelle J.

    (Department of Biomedical Data Sciences, Room Number S5-P, LUMC Main Building, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands)

Abstract

Integrative analysis of copy number and gene expression data can help in understanding the cis and trans effect of copy number aberrations on transcription levels of genes involved in a pathway. To analyse how these copy number mediated gene-gene interactions differ between groups of samples we propose a new method, named dNET. Our method uses ridge regression to model the network topology involving one gene’s expression level, its gene dosage and the expression levels of other genes in the network. The interaction parameters are estimated by fitting the model per gene for all samples together. However, instead of testing for differential network topology per gene, dNET tests for an overall difference in estimated parameters between two groups of samples and produces a single p-value. With the help of several simulation studies, we show that dNET can detect differential network nodes with high accuracy and low rate of false positives even in the presence of differential cis effects. We also apply dNET to publicly available TCGA cancer datasets and identify pathways where copy number mediated gene-gene interactions differ between samples with cancer stage lower than stage 3 and samples with cancer stage 3 or above.

Suggested Citation

  • Chaturvedi Nimisha & Menezes Renée X. de & Wieringen Wessel van & Goeman Jelle J., 2018. "A test for detecting differential indirect trans effects between two groups of samples," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(5), pages 1-11, October.
    • Handle: RePEc:bpj:sagmbi:v:17:y:2018:i:5:p:11:n:2
    DOI: 10.1515/sagmb-2017-0058
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    References listed on IDEAS

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    1. Jelle J. Goeman & Sara A. Van De Geer & Hans C. Van Houwelingen, 2006. "Testing against a high dimensional alternative," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 477-493, June.
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