Author Summary Gaussian graphical model (GGM), a probability model for characterizing conditional dependence among a set of random variables, has been widely used in studying biological networks. It is important and practical to make inference with rigorous statistical properties and high efficiency under a high-dimensional setting, which is common in biological systems that usually contain tens of thousands of molecular elements, such as genes and proteins. This work proposes a novel efficient algorithm, FastGGM, to implement asymptotically normal estimation of large GGM established by Ren et al [1]. It quickly estimates the precision matrix, partial correlations, as well as p-values and confidence intervals for the graph. Simulation studies demonstrate our algorithm outperforms the current algorithm for Ren et al. and algorithms for some other estimation methods, and real data analyses further prove its efficiency in studying biological networks. In conclusion, FastGGM is a statistically sound and computationally fast algorithm for constructing GGM with high-dimensional data. An R package for implementation can be downloaded from http://www.pitt.edu/~wec47/FastGGM.html.
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