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Article
Time delayed causal gene regulatory network inference with hidden common causes
PLoS ONE
  • Leung Yau LO, The Chinese University of Hong Kong
  • Man Leung WONG, Lingnan University
  • Kin Hong LEE, The Chinese University of Hong Kong
  • Kwong Sak LEUNG, The Chinese University of Hong Kong
Document Type
Journal article
Publication Date
9-22-2015
Publisher
Public Library of Science
Disciplines
Abstract

Inferring the gene regulatory network (GRN) is crucial to understanding the working of the cell. Many computational methods attempt to infer the GRN from time series expression data, instead of through expensive and time-consuming experiments. However, existing methods make the convenient but unrealistic assumption of causal sufficiency, i.e. all the relevant factors in the causal network have been observed and there are no unobserved common cause. In principle, in the real world, it is impossible to be certain that all relevant factors or common causes have been observed, because some factors may not have been conceived of, and therefore are impossible to measure. In view of this, we have developed a novel algorithm named HCC-CLINDE to infer an GRN from time series data allowing the presence of hidden common cause(s). We assume there is a sparse causal graph (possibly with cycles) of interest, where the variables are continuous and each causal link has a delay (possibly more than one time step). A small but unknown number of variables are not observed. Each unobserved variable has only observed variables as children and parents, with at least two children, and the children are not linked to each other. Since it is difficult to obtain very long time series, our algorithm is also capable of utilizing multiple short time series, which is more realistic. To our knowledge, our algorithm is far less restrictive than previous works. We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. The results show that our algorithm can adequately recover the true causal GRN and is robust to slight deviation from Gaussian distribution in the error terms. We have also demonstrated the potential of our algorithm on small YEASTRACT subnetworks using limited real data.

DOI
10.1371/journal.pone.0138596
E-ISSN
19326203
Funding Information
This work was supported by The Research Grants Council of the Hong Kong Special Administrative Region (http://www.ugc.edu.hk/eng/rgc/index.htm), Project References 414413: KSL and LU310111: MLW.
Publisher Statement
Copyright © 2015 Lo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
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Citation Information
Lo, L.-Y., Wong, M.-L., Lee, K.-H., & Leung, K.-S. (2015). Time delayed causal gene regulatory network inference with hidden common causes. PLoS ONE, 10(9): e0138596. doi:10.1371/journal.pone.0138596