Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhang L
PROVIDER: S-EPMC8048149 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations becomes large. There is a burgeoning literature on approaches for analyzing large spatial datasets. In this article, we propose a divide-and-conquer strategy within the Bayesian paradigm. We partition the data into subsets, analyze each subset using a Bayesian spatial process model and then obtain approximate posterior inference for the entire dataset by combining the individual posterior distributions from each subset. Importantly, as often desired in spatial analysis, we offer full posterior predictive inference at arbitrary locations for the outcome as well as the residual spatial surface after accounting for spatially oriented predictors. We call this approach "Spatial Meta-Kriging" (SMK). We do not need to store the entire data in one processor, and this leads to superior scalability. We demonstrate SMK with various spatial regression models including Gaussian processes and tapered Gaussian processes. The approach is intuitive, easy to implement, and is supported by theoretical results presented in the supplementary material available online. Empirical illustrations are provided using different simulation experiments and a geostatistical analysis of Pacific Ocean sea surface temperature data.
| S-EPMC6474670 | biostudies-literature
Project description:Epidemiologists and health geographers routinely use small-area survey estimates as covariates to model areal and even individual health outcomes. American Community Survey (ACS) estimates are accompanied by standard errors (SEs), but it is not yet standard practice to use them for evaluating or modeling data reliability. ACS SEs vary systematically across regions, neighborhoods, socioeconomic characteristics, and variables. Failure to consider probable observational error may have substantial impact on the large bodies of literature relying on small-area estimates, including inferential biases and over-confidence in results. The issue is particularly salient for predictive models employed to prioritize communities for service provision or funding allocation. Leveraging the tenets of plausible reasoning and Bayes' theorem, we propose a conceptual framework and workflow for spatial data analysis with areal survey data, including visual diagnostics and model specifications. To illustrate, we follow Krieger et al.'s (2018) call to routinely use the Index of Concentration at the Extremes (ICE) to monitor spatial inequalities in health and mortality. We construct and examine SEs for the ICE, use visual diagnostics to evaluate our observational error model for the ICE, and then estimate an ICE-mortality gradient by incorporating the latter model into our model of sex-specific, midlife (ages 55-64), all-cause United States county mortality rates. We urge researchers to consider data quality as a criterion for variable selection prior to modeling, and to incorporate data reliability information into their models whenever possible.
| S-EPMC8297362 | biostudies-literature
Project description:Knowledge of spatial chromosomal organizations is critical for the study of transcriptional regulation and other nuclear processes in the cell. Recently, chromosome conformation capture (3C) based technologies, such as Hi-C and TCC, have been developed to provide a genome-wide, three-dimensional (3D) view of chromatin organization. Appropriate methods for analyzing these data and fully characterizing the 3D chromosomal structure and its structural variations are still under development. Here we describe a novel Bayesian probabilistic approach, denoted as "Bayesian 3D constructor for Hi-C data" (BACH), to infer the consensus 3D chromosomal structure. In addition, we describe a variant algorithm BACH-MIX to study the structural variations of chromatin in a cell population. Applying BACH and BACH-MIX to a high resolution Hi-C dataset generated from mouse embryonic stem cells, we found that most local genomic regions exhibit homogeneous 3D chromosomal structures. We further constructed a model for the spatial arrangement of chromatin, which reveals structural properties associated with euchromatic and heterochromatic regions in the genome. We observed strong associations between structural properties and several genomic and epigenetic features of the chromosome. Using BACH-MIX, we further found that the structural variations of chromatin are correlated with these genomic and epigenetic features. Our results demonstrate that BACH and BACH-MIX have the potential to provide new insights into the chromosomal architecture of mammalian cells.
| S-EPMC3561073 | biostudies-literature
Project description:Bayesian inference is the process of narrowing down the hypotheses (causes) to the one that best explains the observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method such as exponential moving average (EMA) with a discounting rate is used to improve the ability to respond to a sudden change; it is also necessary to increase the discounting rate. That is, a trade-off is established in which the followability is improved by increasing the discounting rate, but the accuracy is reduced. Here, we propose an extended Bayesian inference (EBI), wherein human-like causal inference is incorporated. We show that both the learning and forgetting effects are introduced into Bayesian inference by incorporating the causal inference. We evaluate the estimation performance of the EBI through the learning task of a dynamically changing Gaussian mixture model. In the evaluation, the EBI performance is compared with those of the EMA and a sequential discounting expectation-maximization algorithm. The EBI was shown to modify the trade-off observed in the EMA.
| S-EPMC7244155 | biostudies-literature
Project description:Single nucleotide polymorphisms (SNPs) in commercial arrays have often been discovered in a small number of samples from selected populations. This ascertainment skews patterns of nucleotide diversity and affects population genetic inferences. We propose a demographic inference pipeline that explicitly models the SNP discovery protocol in an Approximate Bayesian Computation (ABC) framework. We simulated genomic regions according to a demographic model incorporating parameters for the divergence of three well-characterized HapMap populations and recreated the SNP distribution of a commercial array by varying the number of haploid samples and the allele frequency cut-off in the given regions. We then calculated summary statistics obtained from both the ascertained and genomic data and inferred ascertainment and demographic parameters. We implemented our pipeline to study the admixture process that gave rise to the present-day Mexican population. Our estimate of the time of admixture is closer to the historical dates than those in previous works which did not consider ascertainment bias. Although the use of whole genome sequences for demographic inference is becoming the norm, there are still underrepresented areas of the world from where only SNP array data are available. Our inference framework is applicable to those cases and will help with the demographic inference.
| S-EPMC6033855 | biostudies-other
Project description:Selenium (Se) is an essential dietary element for humans and animals, and the atmosphere is an important source of Se to soils. However, estimates of global atmospheric Se fluxes are highly uncertain. To constrain these uncertainties, we use a global model of atmospheric Se cycling and a database of more than 600 sites where Se in aerosol has been measured. Applying Bayesian inference techniques, we determine the probability distributions of global Se emissions from the four major sources: anthropogenic activities, volcanoes, marine biosphere, and terrestrial biosphere. Between 29 and 36 Gg of Se are emitted to the atmosphere every year, doubling previous estimates of emissions. Using emission parameters optimized by aerosol network measurements, our model shows good agreement with the aerosol Se observations (R2 = 0.66), as well as with independent aerosol (0.59) and wet deposition measurements (0.57). Both model and measurements show a decline in Se over North America in the last two decades because of changes in technology and energy policy. Our results highlight the role of the ocean as a net atmospheric Se sink, with around 7 Gg yr-1 of Se transferred from land through the atmosphere. The constrained Se emissions represent a substantial step forward in understanding the global Se cycle.
| S-EPMC7301612 | biostudies-literature
Project description:Species tree inference from multilocus data has emerged as a powerful paradigm in the postgenomic era, both in terms of the accuracy of the species tree it produces as well as in terms of elucidating the processes that shaped the evolutionary history. Bayesian methods for species tree inference are desirable in this area as they have been shown not only to yield accurate estimates, but also to naturally provide measures of confidence in those estimates. However, the heavy computational requirements of Bayesian inference have limited the applicability of such methods to very small data sets. In this article, we show that the computational efficiency of Bayesian inference under the multispecies coalescent can be improved in practice by restricting the space of the gene trees explored during the random walk, without sacrificing accuracy as measured by various metrics. The idea is to first infer constraints on the trees of the individual loci in the form of unresolved gene trees, and then to restrict the sampler to consider only resolutions of the constrained trees. We demonstrate the improvements gained by such an approach on both simulated and biological data.
| S-EPMC7253205 | biostudies-literature
Project description:BackgroundThe combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.ResultsUsing established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping.ConclusionsUsing the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.
| S-EPMC3032670 | biostudies-literature
Project description:Inferring the environment's statistical structure and adapting behavior accordingly is a fundamental modus operandi of the brain. A simple form of this faculty based on spatial attentional orienting can be studied with Posner's location-cueing paradigm in which a cue indicates the target location with a known probability. The present study focuses on a more complex version of this task, where probabilistic context (percentage of cue validity) changes unpredictably over time, thereby creating a volatile environment. Saccadic response speed (RS) was recorded in 15 subjects and used to estimate subject-specific parameters of a Bayesian learning scheme modeling the subjects' trial-by-trial updates of beliefs. Different response models-specifying how computational states translate into observable behavior-were compared using Bayesian model selection. Saccadic RS was most plausibly explained as a function of the precision of the belief about the causes of sensory input. This finding is in accordance with current Bayesian theories of brain function, and specifically with the proposal that spatial attention is mediated by a precision-dependent gain modulation of sensory input. Our results provide empirical support for precision-dependent changes in beliefs about saccade target locations and motivate future neuroimaging and neuropharmacological studies of how Bayesian inference may determine spatial attention.
| S-EPMC4014178 | biostudies-literature
Project description:Spatial process models are being increasingly employed for analyzing data available at geocoded locations. In this article, we build a hierarchical framework with multivariate spatial processes, where the outcomes are "mixed" in the sense that some may be continuous, some binary and others may be counts. The underlying idea is to build a joint model by hierarchically building conditional distributions with different spatial processes embedded in each conditional distribution. The idea is simple and the resulting models can be fitted to multivariate spatial data using straightforward Bayesian computing methods such as Markov chain Monte Carlo methods. Bayesian inference is carried out for parameter estimation and spatial interpolation. The proposed models are illustrated using housing data collected in the Walmer district of Port Elizabeth, South Africa. Inferential interest resides in modeling spatial dependencies of dependent outcomes and associations accounting for independent explanatory variables. Comparisons across different models confirm that the selling price of a house in our data set is relatively better modeled by incorporating spatial processes.
| S-EPMC9032998 | biostudies-literature