Browse
Submit Data
Databases
API
Help

Dataset Information

8 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Discussion on "Competition on Spatial Statistics for Large Datasets".

ABSTRACT: We discuss the methods and results of the RESSTE team in the competition on spatial statistics for large datasets. In the first sub-competition, we implemented block approaches both for the estimation of the covariance parameters and for prediction using ordinary kriging. In the second sub-competition, a two-stage procedure was adopted. In the first stage, the marginal distribution is estimated neglecting spatial dependence, either according to the flexible Tuckey g and h distribution or nonparametrically. In the second stage, estimation of the covariance parameters and prediction are performed using Kriging. Vecchias's approximation implemented in the GpGp package proved to be very efficient. We then make some propositions for future competitions.

Supplementary information

The online version supplementary material available at 10.1007/s13253-021-00462-2.

SUBMITTER: Allard D

PROVIDER: S-EPMC8301736 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics

Project description:This SuperSeries is composed of the SubSeries listed below.

2016-11-08 | GSE73638 | GEO

Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets.

Project description:This article expands upon recent interest in Bayesian hierarchical models in quantitative genetics by developing spatial process models for inference on additive and dominance genetic variance within the context of large spatially referenced trial datasets. Direct application of such models to large spatial datasets are, however, computationally infeasible because of cubic-order matrix algorithms involved in estimation. The situation is even worse in Markov chain Monte Carlo (MCMC) contexts where such computations are performed for several iterations. Here, we discuss approaches that help obviate these hurdles without sacrificing the richness in modeling. For genetic effects, we demonstrate how an initial spectral decomposition of the relationship matrices negate the expensive matrix inversions required in previously proposed MCMC methods. For spatial effects, we outline two approaches for circumventing the prohibitively expensive matrix decompositions: the first leverages analytical results from Ornstein-Uhlenbeck processes that yield computationally efficient tridiagonal structures, whereas the second derives a modified predictive process model from the original model by projecting its realizations to a lower-dimensional subspace, thereby reducing the computational burden. We illustrate the proposed methods using a synthetic dataset with additive, dominance, genetic effects and anisotropic spatial residuals, and a large dataset from a Scots pine (Pinus sylvestris L.) progeny study conducted in northern Sweden. Our approaches enable us to provide a comprehensive analysis of this large trial, which amply demonstrates that, in addition to violating basic assumptions of the linear model, ignoring spatial effects can result in downwardly biased measures of heritability.

| S-EPMC2775095 | biostudies-literature

Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics [Tumor]

Project description:An expert-pathologist-reviewed epithelial ovarian cancer reference library (n = 50) used to assign the histopathology of epithelial ovarian cell lines using compositional statistics and random gene-sets

2016-11-08 | GSE73551 | GEO

A Case Study Competition Among Methods for Analyzing Large Spatial Data.

Project description:The Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alternatives to the full Gaussian process that are more amenable to handling big spatial data have been proposed. These modern methods often exploit low-rank structures and/or multi-core and multi-threaded computing environments to facilitate computation. This study provides, first, an introductory overview of several methods for analyzing large spatial data. Second, this study describes the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology. Specifically, each research group was provided with two training datasets (one simulated and one observed) along with a set of prediction locations. Each group then wrote their own implementation of their method to produce predictions at the given location and each was subsequently run on a common computing environment. The methods were then compared in terms of various predictive diagnostics. Supplementary materials regarding implementation details of the methods and code are available for this article online.Electronic supplementary materialSupplementary materials for this article are available at 10.1007/s13253-018-00348-w.

| S-EPMC6709111 | biostudies-literature

Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics [Cell lines]

Project description:Compositional statistics and random gene-sets were used to assign the tumor site of origin and histopathology of 18 epithelial ovarian cancer cell lines

2016-11-08 | GSE73637 | GEO

Microstructural damage sensitivity prediction using spatial statistics.

Project description:The vast compositional space of metallic materials provides ample opportunity to design stronger, more ductile and cheaper alloys. However, the substantial complexity of deformation micro-mechanisms makes simulation-based prediction of microstructural performance exceedingly difficult. In absence of predictive tools, tedious experiments have to be conducted to screen properties. Here, we develop a purely empirical model to forecast microstructural performance in advance, bypassing these challenges. This is achieved by combining in situ deformation experiments with a novel methodology that utilizes n-point statistics and principle component analysis to extract key microstructural features. We demonstrate this approach by predicting crack nucleation in a complex dual-phase steel, achieving substantial predictive ability (84.8% of microstructures predicted to crack, actually crack), a substantial improvement upon the alternate simulation-based approaches. This significant accuracy illustrates the utility of this alternate approach and opens the door to a wide range of alloy design tools.

| S-EPMC6391476 | biostudies-literature

Discussion on "Spatial+: A novel approach to spatial confounding" by Dupont, Wood, and Augustin.

Project description: Not available

| S-EPMC10855624 | biostudies-literature

Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics

Project description:Prior Knowledge Transfer Across Transcriptional Datasets Using Compositional Statistics

| PRJNA297464 | ENA

Natural auditory scene statistics shapes human spatial hearing.

Project description:Human perception, cognition, and action are laced with seemingly arbitrary mappings. In particular, sound has a strong spatial connotation: Sounds are high and low, melodies rise and fall, and pitch systematically biases perceived sound elevation. The origins of such mappings are unknown. Are they the result of physiological constraints, do they reflect natural environmental statistics, or are they truly arbitrary? We recorded natural sounds from the environment, analyzed the elevation-dependent filtering of the outer ear, and measured frequency-dependent biases in human sound localization. We find that auditory scene statistics reveals a clear mapping between frequency and elevation. Perhaps more interestingly, this natural statistical mapping is tightly mirrored in both ear-filtering properties and in perceived sound location. This suggests that both sound localization behavior and ear anatomy are fine-tuned to the statistics of natural auditory scenes, likely providing the basis for the spatial connotation of human hearing.

| S-EPMC4000839 | biostudies-other

Restricted Covariance Priors with Applications in Spatial Statistics.

Project description:We present a Bayesian model for area-level count data that uses Gaussian random effects with a novel type of G-Wishart prior on the inverse variance- covariance matrix. Specifically, we introduce a new distribution called the truncated G-Wishart distribution that has support over precision matrices that lead to positive associations between the random effects of neighboring regions while preserving conditional independence of non-neighboring regions. We describe Markov chain Monte Carlo sampling algorithms for the truncated G-Wishart prior in a disease mapping context and compare our results to Bayesian hierarchical models based on intrinsic autoregression priors. A simulation study illustrates that using the truncated G-Wishart prior improves over the intrinsic autoregressive priors when there are discontinuities in the disease risk surface. The new model is applied to an analysis of cancer incidence data in Washington State.

| S-EPMC4705859 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data