cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5309845
title: Dataset for: On the functional inverted-Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data

description: Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and imposing an inverted-Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted-Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual auto-covariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcomes correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity and radiation, on the land area of the North American West Coast. Results and predictive performance are contrasted with findings from approaches using convolution only or co-regionalization.
DOI: 10.6084/m9.figshare.5309845.v1
title: Dataset for: On the functional inverted-Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data

description: Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and imposing an inverted-Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted-Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual auto-covariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcomes correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity and radiation, on the land area of the North American West Coast. Results and predictive performance are contrasted with findings from approaches using convolution only or co-regionalization.
DOI: 10.6084/m9.figshare.5309797
title: Blood-based spectroscopy toward investigation of neurodegenerative diseases

description: ATR-FTIR spectroscopy was used for the analysis of blood samples towards the investigation and differential diagnosis of neurodegenerative diseases. This file contains raw and preprocessed data derived from patients with these diseases. Raw spectra were preprocessed to correct problems associated with data acquisition and further multivariate approaches were applied to study the respective datasets. The preprocessing of the raw spectra was performed within MATLAB software, using an in-house-developed IRootLab toolbox (http://trevisanj.github.io/irootlab/).<br><br>Abbreviations: AD: Alzheimer's disease; CBD: corticobasal degeneration; DLB: dementia with Lewy bodies; FTD: frontotemporal dementia; HC: healthy controls; MND: motor neuron disease; MSA: multiple system atrophy; PD: Parkinson's disease; PNFA: progressive nonfluent aphasia; PSD: progressive supranuclear palsy. <br>
DOI: 10.6084/m9.figshare.5309722
title: iLIR@viral: A web resource for LIR motif-containing proteins in viruses

description: Macroautophagy/autophagy has been shown to mediate the selective lysosomal degradation of pathogenic bacteria and viruses (xenophagy), and to contribute to the activation of innate and adaptative immune responses. Autophagy can serve as an antiviral defense mechanism but also as a proviral process during infection. Atg8-family proteins play a central role in the autophagy process due to their ability to interact with components of the autophagy machinery as well as selective autophagy receptors and adaptor proteins. Such interactions are usually mediated through LC3-interacting region (LIR) motifs. So far, only one viral protein has been experimentally shown to have a functional LIR motif, leaving open a vast field for investigation. Here, we have developed the iLIR@viral database (http://ilir.uk/virus/) as a freely accessible web resource listing all the putative canonical LIR motifs identified in viral proteins. Additionally, we used a curated text-mining analysis of the literature to identify novel putative LIR motif-containing proteins (LIRCPs) in viruses. We anticipate that iLIR@viral will assist with elucidating the full complement of LIRCPs in viruses.
DOI: 10.6084/m9.figshare.5309713
title: Bayesian Spatial Multivariate Receptor Modeling for Multisite Multipollutant Data

description: For the development of effective air pollution control strategies, it is crucial to identify the sources that are the principal contributors to air pollution and estimate how much each source contributes. Multivariate receptor modeling aims to address these problems by decomposing ambient concentrations of multiple air pollutants into components associated with different source types. With the expanded monitoring efforts that have been established over the past several decades, extensive multivariate air pollution data obtained from multiple monitoring sites (multisite multipollutant data) are now available. Although considerable research has been conducted on modeling multivariate space-time data in other contexts, there has been little research on spatial multivariate receptor models for multisite, multipollutant data. We present a Bayesian spatial multivariate receptor modeling (BSMRM) approach that can incorporate spatial correlations in multisite, multipollutant data into the estimation of source composition profiles and contributions, based on discrete process convolution models for multivariate spatial processes. The new BSMRM approach enables predictions of source contributions at unmonitored sites as well as simultaneously dealing with model uncertainty caused by the unknown number of sources and identifiability conditions. The new approach can also provide uncertainty estimates for the predicted source contributions at any location, which was not possible in previous multivariate receptor modeling approaches. The proposed approach is applied to 24-hour ambient air concentrations of 17 Volatile Organic Compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, between 2003 and 2005. Supplementary materials for this article, including real data and MATLAB codes for implementing BSMRM, are available online on the journal web site.
DOI: 10.6084/m9.figshare.5309722.v2
title: iLIR@viral: A web resource for LIR motif-containing proteins in viruses

description: Macroautophagy/autophagy has been shown to mediate the selective lysosomal degradation of pathogenic bacteria and viruses (xenophagy), and to contribute to the activation of innate and adaptative immune responses. Autophagy can serve as an antiviral defense mechanism but also as a proviral process during infection. Atg8-family proteins play a central role in the autophagy process due to their ability to interact with components of the autophagy machinery as well as selective autophagy receptors and adaptor proteins. Such interactions are usually mediated through LC3-interacting region (LIR) motifs. So far, only one viral protein has been experimentally shown to have a functional LIR motif, leaving open a vast field for investigation. Here, we have developed the iLIR@viral database (http://ilir.uk/virus/) as a freely accessible web resource listing all the putative canonical LIR motifs identified in viral proteins. Additionally, we used a curated text-mining analysis of the literature to identify novel putative LIR motif-containing proteins (LIRCPs) in viruses. We anticipate that iLIR@viral will assist with elucidating the full complement of LIRCPs in viruses.
DOI: 10.6084/m9.figshare.5309716
title: Representing Sparse Gaussian DAGs as Sparse R-Vines Allowing for Non-Gaussian Dependence

description: Modeling dependence in high-dimensional systems has become an increasingly important topic. Most approaches rely on the assumption of a multivariate Gaussian distribution such as statistical models on directed acyclic graphs (DAGs). They are based on modeling conditional independencies and are scalable to high dimensions. In contrast, vine copula models accommodate more elaborate features like tail dependence and asymmetry, as well as independent modeling of the marginals. This flexibility comes however at the cost of exponentially increasing complexity for model selection and estimation. We show a novel connection between DAGs with limited number of parents and truncated vine copulas under sufficient conditions. This motivates a more general procedure exploiting the fast model selection and estimation of sparse DAGs while allowing for non-Gaussian dependence using vine copulas. By numerical examples in hundreds of dimensions, we demonstrate that our approach outperforms the standard method for vine structure selection. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.5309722.v1
title: iLIR@viral: a web resource for LIR motif-containing proteins in viruses

description: Macroautophagy/autophagy has been shown to mediate the selective lysosomal degradation of pathogenic bacteria and viruses (xenophagy), and to contribute to the activation of innate and adaptative immune responses. Autophagy can serve as an antiviral defense mechanism but also as a pro-viral process during infection. Atg8-family proteins play a central role in the autophagy process due to their ability to interact with components of the autophagy machinery as well as selective autophagy receptors and adaptor proteins. Such interactions are usually mediated through LC3-interacting region (LIR) motifs. So far, only one viral protein has been experimentally shown to have a functional LIR motif, leaving open a vast field for investigation. Here, we have developed the iLIR@viral database (http://ilir.uk/virus/) as a freely accessible web resource listing all the putative canonical LIR motifs identified in viral proteins. Additionally, we used a curated text-mining analysis of the literature to identify novel putative LIR motif-containing proteins (LICRPs) in viruses. We anticipate that iLIR@viral will assist with elucidating the full complement of LIRCPs in viruses.
DOI: 10.6084/m9.figshare.5309689
title: Selection of the Regularization Parameter in Graphical Models Using Network Characteristics

description: Gaussian graphical models represent the underlying graph structure of conditional dependence between random variables, which can be determined using their partial correlation or precision matrix. In a high-dimensional setting, the precision matrix is estimated using penalized likelihood by adding a penalization term, which controls the amount of sparsity in the precision matrix and totally characterizes the complexity and structure of the graph. The most commonly used penalization term is the L1 norm of the precision matrix scaled by the regularization parameter, which determines the trade-off between sparsity of the graph and fit to the data. In this article, we propose several procedures to select the regularization parameter in the estimation of graphical models that focus on recovering reliably the appropriate network structure of the graph. We conduct an extensive simulation study to show that the proposed methods produce useful results for different network topologies. The approaches are also applied in a high-dimensional case study of gene expression data with the aim to discover the genes relevant to colon cancer. Using these data, we find graph structures, which are verified to display significant biological gene associations. Supplementary material is available online.
DOI: 10.6084/m9.figshare.5309713.v2
title: Bayesian Spatial Multivariate Receptor Modeling for Multisite Multipollutant Data

description: For the development of effective air pollution control strategies, it is crucial to identify the sources that are the principal contributors to air pollution and estimate how much each source contributes. Multivariate receptor modeling aims to address these problems by decomposing ambient concentrations of multiple air pollutants into components associated with different source types. With the expanded monitoring efforts that have been established over the past several decades, extensive multivariate air pollution data obtained from multiple monitoring sites (multisite multipollutant data) are now available. Although considerable research has been conducted on modeling multivariate space-time data in other contexts, there has been little research on spatial multivariate receptor models for multisite, multipollutant data. We present a Bayesian spatial multivariate receptor modeling (BSMRM) approach that can incorporate spatial correlations in multisite, multipollutant data into the estimation of source composition profiles and contributions, based on discrete process convolution models for multivariate spatial processes. The new BSMRM approach enables predictions of source contributions at unmonitored sites as well as simultaneously dealing with model uncertainty caused by the unknown number of sources and identifiability conditions. The new approach can also provide uncertainty estimates for the predicted source contributions at any location, which was not possible in previous multivariate receptor modeling approaches. The proposed approach is applied to 24-hour ambient air concentrations of 17 Volatile Organic Compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, between 2003 and 2005. Supplementary materials for this article, including real data and MATLAB codes for implementing BSMRM, are available online on the journal web site.
DOI: 10.6084/m9.figshare.5309716.v1
title: Representing Sparse Gaussian DAGs as Sparse R-vines Allowing for Non-Gaussian Dependence

description: Modeling dependence in high dimensional systems has become an increasingly important topic. Most approaches rely on the assumption of a multivariate Gaussian distribution such as statistical models on directed acyclic graphs (DAGs). They are based on modeling conditional independencies and are scalable to high dimensions. In contrast, vine copula models accommodate more elaborate features like tail dependence and asymmetry, as well as independent modeling of the marginals. This flexibility comes however at the cost of exponentially increasing complexity for model selection and estimation. We show a novel connection between DAGs with limited number of parents and truncated vine copulas under sufficient conditions. This motivates a more general procedure exploiting the fast model selection and estimation of sparse DAGs while allowing for non-Gaussian dependence using vine copulas. By numerical examples in hundreds of dimensions, we demonstrate that our approach outperforms the standard method for vine structure selection.
DOI: 10.6084/m9.figshare.5309713.v1
title: Bayesian Spatial Multivariate Receptor Modeling for Multi-Site Multi-Pollutant Data

description: For the development of effective air pollution control strategies, it is crucial to identify the sources that are the principal contributors to air pollution and estimate how much each source contributes. Multivariate receptor modeling aims to address these problems by decomposing ambient concentrations of multiple air pollutants into components associated with different source types. With the expanded monitoring efforts that have been established over the past several decades, extensive multivariate air pollution data obtained from multiple monitoring sites (multi-site multi-pollutant data) are now available. Although considerable research has been conducted on modeling multivariate space-time data in other contexts, there has been little research on spatial multivariate receptor models for multi-site, multi-pollutant data. We present a Bayesian spatial multivairate receptor modeling (BSMRM) approach that can incorporate spatial correlations in multi-site, multi-pollutant data into the estimation of source composition profiles and contributions, based on discrete process convolution models for multivariate spatial processes. The new BSMRM approach enables predictions of source contributions at unmonitored sites as well as simultaneously dealing with model uncertainty caused by the unknown number of sources and identifiability conditions. The new approach can also provide uncertainty estimates for the predicted source contributions at any location, which was not possible in previous multivariate receptor modeling approaches. The proposed approach is applied to 24-hour ambient air concentrations of 17 Volatile Organic Compounds (VOCs) measured at nine monitoring sites in Harris County, Texas between 2003 and 2005. Supplementary materials for this article, including real data and MATLAB codes for implementing BSMRM, are available online on the journal web site.
DOI: 10.6084/m9.figshare.5309689.v2
title: Selection of the Regularization Parameter in Graphical Models Using Network Characteristics

description: Gaussian graphical models represent the underlying graph structure of conditional dependence between random variables, which can be determined using their partial correlation or precision matrix. In a high-dimensional setting, the precision matrix is estimated using penalized likelihood by adding a penalization term, which controls the amount of sparsity in the precision matrix and totally characterizes the complexity and structure of the graph. The most commonly used penalization term is the L1 norm of the precision matrix scaled by the regularization parameter, which determines the trade-off between sparsity of the graph and fit to the data. In this article, we propose several procedures to select the regularization parameter in the estimation of graphical models that focus on recovering reliably the appropriate network structure of the graph. We conduct an extensive simulation study to show that the proposed methods produce useful results for different network topologies. The approaches are also applied in a high-dimensional case study of gene expression data with the aim to discover the genes relevant to colon cancer. Using these data, we find graph structures, which are verified to display significant biological gene associations. Supplementary material is available online.
DOI: 10.6084/m9.figshare.5309689.v1
title: Selection of the Regularization Parameter in Graphical Models using Network Characteristics

description: Gaussian graphical models represent the underlying graph structure of conditional dependence between random variables which can be determined using their partial correlation or precision matrix. In a high-dimensional setting, the precision matrix is estimated using penalized likelihood by adding a penalization term which controls the amount of sparsity in the precision matrix and totally characterizes the complexity and structure of the graph. The most commonly used penalization term is the L1 norm of the precision matrix scaled by the regularization parameter which determines the trade-off between sparsity of the graph and fit to the data. In this paper we propose several procedures to select the regularization parameter in the estimation of graphical models that focus on recovering reliably the appropriate network structure of the graph. We conduct an extensive simulation study to show that the proposed methods produce useful results for different network topologies. The approaches are also applied in a high-dimensional case study of gene expression data with the aim to discover the genes relevant to colon cancer. Using this data, we find graph structures which are verified to display significant biological gene associations. <b>Note</b>: supplementary material is available online.
DOI: 10.6084/m9.figshare.5307391
title: Community portal for the Citrusgreening disease resources

description: Huanglongbing (HLB) is a tritrophic disease complex involving citrus host trees, the Asian citrus psyllid (ACP) insect and a phloem restricted, bacterial pathogen <i>Candidatus </i>Liberibacter asiaticus (CLas). HLB is considered to be the most devastating of all citrus diseases, and there is currently no adequate control strategy. In Florida, an estimated 40-70% of all citrus trees are infected, and HLB effects include production declines (10-20% per year), diminished fruit quality and increased production costs. We have designed a web portal with information for consumers and growers as well as genomics and bioinformatics resources for citrus, ACP and CLas. The JBrowse browser provides the context for expression data and annotated features on the genome. Biocyc Pathway Tools databases model biochemical pathways within each organism and will be used to explore the entire disease complex. Micro-CT analysis of the ACP will be combined with transcriptomics data from different tissues, life stages and sexes to create a 3D atlas that will reveal the internal anatomy of ACP overlaid with the expression profile of different tissues across major life stages. All tools like JBrowse, Blast and the Atlas will connect to a central database containing gene models for citrus, ACP and multiple <i>Candidatus </i>Liberibacter pathogens. The database will allow manual curation so that the community can continuously improve the knowledgebase as more experimental research is published. The database architecture combines custom schemas and the community developed Chado schema (http://gmod.org/wiki/Chado) for compatibility with other genome databases.
DOI: 10.6084/m9.figshare.5307382
title: Hamalainen_SciRep_fitness consequences of peak effort_data.xlsx

description: These data sets were used for the analyses presented in Hämäläinen et al. (2017) Fitness consequences of peak<br>reproductive effort in a resource<br>pulse system. Scientific Reports, DOI:10.1038/s41598-017-09724-x
DOI: 10.6084/m9.figshare.5307382.v1
title: Hamalainen_SciRep_fitness consequences of peak effort_data.xlsx

description: These data sets were used for the analyses presented in Hämäläinen et al. (2017) Fitness consequences of peak<br>reproductive effort in a resource<br>pulse system. Scientific Reports, DOI:10.1038/s41598-017-09724-x