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FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks

Wang, T and Ren, Z and Ding, Y and Fang, Z and Sun, Z and MacDonald, ML and Sweet, RA and Wang, J and Chen, W (2016) FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks. PLoS Computational Biology, 12 (2). ISSN 1553-734X

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Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer’s disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named “FastGGM”.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Wang, Ttiw33@pitt.eduTIW33
Ren, Zzren@pitt.eduZREN
Fang, Zzhf9@pitt.eduZHF9
Sun, Zzhs31@pitt.eduZHS31
MacDonald, MLMLM192@pitt.eduMLM192
Sweet, RAsweet@pitt.eduSWEET
Wang, Jjiw95@pitt.eduJIW95
Chen, W
ContributionContributors NameEmailPitt UsernameORCID
Date: 1 February 2016
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: PLoS Computational Biology
Volume: 12
Number: 2
DOI or Unique Handle: 10.1371/journal.pcbi.1004755
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
School of Public Health > Biostatistics
School of Medicine > Neurology
School of Medicine > Pediatrics
School of Medicine > Psychiatry
Refereed: Yes
ISSN: 1553-734X
Date Deposited: 23 Aug 2016 13:45
Last Modified: 27 Mar 2021 10:55


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