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Inferring gene regulatory networks by an order independent algorithm using incomplete data sets

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Listed:
  • Rosa Aghdam
  • Mojtaba Ganjali
  • Parisa Niloofar
  • Changiz Eslahchi

Abstract

Analyzing incomplete data for inferring the structure of gene regulatory networks (GRNs) is a challenging task in bioinformatic. Bayesian network can be successfully used in this field. k -nearest neighbor, singular value decomposition (SVD)-based and multiple imputation by chained equations are three fundamental imputation methods to deal with missing values. Path consistency (PC) algorithm based on conditional mutual information (PCA--CMI) is a famous algorithm for inferring GRNs. This algorithm needs the data set to be complete. However, the problem is that PCA--CMI is not a stable algorithm and when applied on permuted gene orders, different networks are obtained. We propose an order independent algorithm, PCA--CMI--OI, for inferring GRNs. After imputation of missing data, the performances of PCA--CMI and PCA--CMI--OI are compared. Results show that networks constructed from data imputed by the SVD-based method and PCA--CMI--OI algorithm outperform other imputation methods and PCA--CMI. An undirected or partially directed network is resulted by PC-based algorithms. Mutual information test (MIT) score, which can deal with discrete data, is one of the famous methods for directing the edges of resulted networks. We also propose a new score, ConMIT, which is appropriate for analyzing continuous data. Results shows that the precision of directing the edges of skeleton is improved by applying the ConMIT score.

Suggested Citation

  • Rosa Aghdam & Mojtaba Ganjali & Parisa Niloofar & Changiz Eslahchi, 2016. "Inferring gene regulatory networks by an order independent algorithm using incomplete data sets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 893-913, April.
  • Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:893-913
    DOI: 10.1080/02664763.2015.1079307
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    References listed on IDEAS

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    1. Kalisch, Markus & Mächler, Martin & Colombo, Diego & Maathuis, Marloes H. & Bühlmann, Peter, 2012. "Causal Inference Using Graphical Models with the R Package pcalg," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i11).
    2. Marco Di Zio & Mauro Scanu & Lucia Coppola & Orietta Luzi & Alessandra Ponti, 2004. "Bayesian networks for imputation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 309-322, May.
    3. Rosa Aghdam & Mojtaba Ganjali & Changiz Eslahchi, 2014. "IPCA-CMI: An Algorithm for Inferring Gene Regulatory Networks based on a Combination of PCA-CMI and MIT Score," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-10, April.
    4. Robert Drake & Apratim Guha, 2014. "A mutual information-based k -sample test for discrete distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 2011-2027, September.
    5. Jianhua Ruan, 2010. "A Top-Performing Algorithm for the DREAM3 Gene Expression Prediction Challenge," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-8, February.
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