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Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles

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  • Ali Shojaie
  • Alexandra Jauhiainen
  • Michael Kallitsis
  • George Michailidis

Abstract

Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g., wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network.

Suggested Citation

  • Ali Shojaie & Alexandra Jauhiainen & Michael Kallitsis & George Michailidis, 2014. "Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.
  • Handle: RePEc:plo:pone00:0082393
    DOI: 10.1371/journal.pone.0082393
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    References listed on IDEAS

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    1. Andrea Pinna & Nicola Soranzo & Alberto de la Fuente, 2010. "From Knockouts to Networks: Establishing Direct Cause-Effect Relationships through Graph Analysis," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-8, October.
    2. Tresch Achim & Markowetz Florian, 2008. "Structure Learning in Nested Effects Models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-28, March.
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    Cited by:

    1. Xiao Guo & Hai Zhang, 2020. "Sparse directed acyclic graphs incorporating the covariates," Statistical Papers, Springer, vol. 61(5), pages 2119-2148, October.
    2. Xiao Guo & Hai Zhang & Yao Wang & Yong Liang, 2019. "Structure learning of sparse directed acyclic graphs incorporating the scale-free property," Computational Statistics, Springer, vol. 34(2), pages 713-742, June.

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