Information enhanced model selection for Gaussian graphical model with application to metabolomic data.

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

INFORMATION CONTENT OF HIGH-ORDER ASSOCIATIONS OF THE HUMAN GUT MICROBIOTA NETWORK.

The human gastrointestinal tract is an environment that hosts an ecosystem of microorganisms essential to human health. Vital biological processes emerge from fundamental inter- and intra-species molecular interactions that influence the assembly and composition of the gut microbiota ecology. Here we quantify the complexity of the ecological relationships within the human infant gut microbiota ecosystem as a function of the information contained in the nonlinear associations of a sequence of increasingly-specified maximum entropy representations of the system. Our paradigm frames the ecological state, in terms of the presence or absence of individual microbial ecological units that are identified by amplicon sequence variants (ASV) in the gut microenvironment, as a function of both the ecological states of its neighboring units and, in a departure from standard graphical model representations, the associations among the units within its neighborhood. We characterize the order of the system based on the relative quantity of statistical information encoded by high-order statistical associations of the infant gut microbiota.

Identification of microbial interaction network: zero-inflated latent Ising model based approach.

BACKGROUND: Throughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks. METHODS AND RESULTS: To identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature. CONCLUSIONS: Constrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.

Interictal epileptiform discharges impair word recall in multiple brain areas.

OBJECTIVES: Interictal epileptiform discharges (IEDs) have been linked to memory impairment, but the spatial and temporal dynamics of this relationship remain elusive. In the present study, we aim to systematically characterize the brain areas and times at which IEDs affect memory. METHODS: Eighty epilepsy patients participated in a delayed free recall task while undergoing intracranial electroencephalography (EEG) monitoring. We analyzed the locations and timing of IEDs relative to the behavioral data in order to measure their effects on memory. RESULTS: Overall IED rates did not correlate with task performance across subjects (r = 0.03, p = 0.8). However, at a finer temporal scale, within-subject memory was negatively affected by IEDs during the encoding and recall periods of the task but not during the rest and distractor periods (p < 0.01, p < 0.001, p = 0.3, and p = 0.8, respectively). The effects of IEDs during encoding and recall were stronger in the left hemisphere than in the right (p < 0.05). Of six brain areas analyzed, IEDs in the inferior-temporal, medial-temporal, and parietal areas significantly affected memory (false discovery rate < 0.05). SIGNIFICANCE: These findings reveal a network of brain areas sensitive to IEDs with key nodes in temporal as well as parietal lobes. They also demonstrate the time-dependent effects of IEDs in this network on memory.

Variation in Utilization and Need for Tympanostomy Tubes across England and New England.

OBJECTIVES: To compare rates of typmanostomy tube insertions for otitis media with effusion with estimates of need in 2 countries. STUDY DESIGN: This cross-sectional analysis used all-payer claims to calculate rates of tympanostomy tube insertions for insured children ages 2-8 years (2007-2010) across pediatric surgical areas (PSA) for Northern New England (NNE; Maine, Vermont, and New Hampshire) and the English National Health Service Primary Care Trusts (PCT). Rates were compared with expected rates estimated using a Monte Carlo simulation model that integrates clinical guidelines and published probabilities of the incidence and course of otitis media with effusion. RESULTS: Observed rates of tympanostomy tube placement varied >30-fold across English PCT (N = 150) and >3-fold across NNE PSA (N = 30). At a 25 dB hearing threshold, the overall difference in observed to expected tympanostomy tubes provided was -3.41 per 1000 child-years in England and -0.01 per 1000 child-years in NNE. Observed incidence of insertion was less than expected in 143 of 151 PCT, and was higher than expected in one-half of the PSA. Using a 20 dB hearing threshold, there were fewer tube insertions than expected in all but 2 England and 7 NNE areas. There was an inverse relationship between estimated need and observed tube insertion rates. CONCLUSIONS: Regional variations in observed tympanostomy tube insertion rates are unlikely to be due to differences in need and suggest overall underuse in England and both overuse and underuse in NNE.