title: FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks.
abstract: 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".
authors.affiliation: Division of Pulmonary Medicine, Allergy and Immunology; Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Ting
authors.initials: T
authors.lastname: Wang
authors.affiliation: Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Zhao
authors.initials: Z
authors.lastname: Ren
authors.affiliation: Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Ying
authors.initials: Y
authors.lastname: Ding
authors.affiliation: Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Zhou
authors.initials: Z
authors.lastname: Fang
authors.affiliation: Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Sun
authors.affiliation: Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Matthew L
authors.initials: ML
authors.lastname: MacDonald
authors.affiliation: Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.|Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.|VISN 4 Mental Illness Research, Education and Clinical Center (MIRECC), VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Robert A
authors.initials: RA
authors.lastname: Sweet
authors.affiliation: Division of Pulmonary Medicine, Allergy and Immunology; Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Jieru
authors.initials: J
authors.lastname: Wang
authors.affiliation: Division of Pulmonary Medicine, Allergy and Immunology; Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.|Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, United States of America.|Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001249:Asthma / Q000235:genetics / Q000378:metabolism; D002648:Child; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D008954:Models, Biological; D016011:Normal Distribution