cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1209699.v1
title: Sparse Biclustering of Transposable Data

description: We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log-likelihood. We apply an ℓ<sub>1</sub> penalty to the means of the biclusters to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of <i>k</i>-means clustering. We show that <i>k</i>-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for biclustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression dataset. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1209700.v1
title: A “Density-Based” Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models

description: We propose a new model for cluster analysis in a Bayesian nonparametric framework. Our model combines two ingredients, species sampling mixture models of Gaussian distributions on one hand, and a deterministic clustering procedure (DBSCAN) on the other. Here, two observations from the underlying species sampling mixture model share the same cluster if the distance between the densities corresponding to their latent parameters is smaller than a threshold; this yields a random partition which is coarser than the one induced by the species sampling mixture. Since this procedure depends on the value of the threshold, we suggest a strategy to fix it. In addition, we discuss implementation and applications of the model; comparison with more standard clustering algorithms will be given as well. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.1209700.v3
title: A “Density-Based” Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models

description: We propose a new model for cluster analysis in a Bayesian nonparametric framework. Our model combines two ingredients, species sampling mixture models of Gaussian distributions on one hand, and a deterministic clustering procedure (DBSCAN) on the other. Here, two observations from the underlying species sampling mixture model share the same cluster if the distance between the densities corresponding to their latent parameters is smaller than a threshold; this yields a random partition which is coarser than the one induced by the species sampling mixture. Since this procedure depends on the value of the threshold, we suggest a strategy to fix it. In addition, we discuss implementation and applications of the model; comparison with more standard clustering algorithms will be given as well. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.1209701.v3
title: Biclustering Three-Dimensional Data Arrays With Plaid Models

description: Three-dimensional data arrays (collections of individual data matrices) are increasingly prevalent in modern data and pose unique challenges to pattern extraction and visualization. This article introduces a biclustering technique for exploration and pattern detection in such complex structured data. The proposed framework couples the popular plaid model together with tools from functional data analysis to guide the estimation of bicluster responses over the array. We present an efficient algorithm that first detects biclusters that exhibit strong deviations for some data matrices, and then estimates their responses over the entire data array. Altogether, the framework is useful to home in on and display underlying structure and its evolution over conditions/time. The methods are scalable to large datasets, and can accommodate a variety of dynamic patterns. The proposed techniques are illustrated on gene expression data and bilateral trade networks. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.1209701.v2
title: Biclustering Three-Dimensional Data Arrays With Plaid Models

description: Three-dimensional data arrays (collections of individual data matrices) are increasingly prevalent in modern data and pose unique challenges to pattern extraction and visualization. This article introduces a biclustering technique for exploration and pattern detection in such complex structured data. The proposed framework couples the popular plaid model together with tools from functional data analysis to guide the estimation of bicluster responses over the array. We present an efficient algorithm that first detects biclusters that exhibit strong deviations for some data matrices, and then estimates their responses over the entire data array. Altogether, the framework is useful to home in on and display underlying structure and its evolution over conditions/time. The methods are scalable to large datasets, and can accommodate a variety of dynamic patterns. The proposed techniques are illustrated on gene expression data and bilateral trade networks. Supplementary materials are available online.
DOI: 10.6084/m9.figshare.1209697
title: Sufficient Dimension Folding for Regression Mean Function

description: In this article, we consider sufficient dimension folding for the regression mean function when predictors are matrix- or array-valued. We propose a new concept named central mean dimension folding subspace and its two local estimation methods: folded outer product of gradients estimation (folded-OPG) and folded minimum average variance estimation (folded-MAVE). We establish the asymptotic properties for folded-MAVE. A modified BIC criterion is used to determine the dimensions of the central mean dimension folding subspace. We evaluate the performances of the two local estimation methods by simulated examples and demonstrate the efficacy of folded-MAVE in finite samples. And in particular, we apply our methods to analyze a longitudinal study of primary biliary cirrhosis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1209699.v3
title: Sparse Biclustering of Transposable Data

description: We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log-likelihood. We apply an ℓ<sub>1</sub> penalty to the means of the biclusters to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of <i>k</i>-means clustering. We show that <i>k</i>-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for biclustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression dataset. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1209700.v2
title: A “Density-Based” Algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models

description: We propose a new model for cluster analysis in a Bayesian nonparametric framework. Our model combines two ingredients, species sampling mixture models of Gaussian distributions on one hand, and a deterministic clustering procedure (DBSCAN) on the other. Here, two observations from the underlying species sampling mixture model share the same cluster if the distance between the densities corresponding to their latent parameters is smaller than a threshold; this yields a random partition which is coarser than the one induced by the species sampling mixture. Since this procedure depends on the value of the threshold, we suggest a strategy to fix it. In addition, we discuss implementation and applications of the model; comparison with more standard clustering algorithms will be given as well. Supplementary materials for the article are available online.
DOI: 10.6084/m9.figshare.1209696
title: Sparsity Inducing Prior Distributions for Correlation Matrices of Longitudinal Data

description: For longitudinal data, the modeling of a correlation matrix <b> R</b> can be a difficult statistical task due to both the positive definite and the unit diagonal constraints. Because the number of parameters increases quadratically in the dimension, it is often useful to consider a sparse parameterization. We introduce a pair of prior distributions on the set of correlation matrices for longitudinal data through the partial autocorrelations (PACs), which vary independently over (−1,1). The first prior shrinks each of the PACs toward zero with increasingly aggressive shrinkage in lag. The second prior (a selection prior) is a mixture of a zero point mass and a continuous component for each PAC, allowing for a sparse representation. The structure implied under our priors is readily interpretable for time-ordered responses because each zero PAC implies a conditional independence relationship in the distribution of the data. Selection priors on the PACs provide a computationally attractive alternative to selection on the elements of <b> R</b> or <b> R</b><sup>− 1</sup> for ordered data. These priors allow for data-dependent shrinkage/selection under an intuitive parameterization in an unconstrained setting. The proposed priors are compared to standard methods through a simulation study and illustrated using a multivariate probit data example. Supplemental materials for this article (appendix, data, and R code) are available online.
DOI: 10.6084/m9.figshare.1209699.v2
title: Sparse Biclustering of Transposable Data

description: We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log-likelihood. We apply an ℓ<sub>1</sub> penalty to the means of the biclusters to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of <i>k</i>-means clustering. We show that <i>k</i>-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for biclustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression dataset. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1209696.v1
title: Sparsity Inducing Prior Distributions for Correlation Matrices of Longitudinal Data

description: For longitudinal data, the modeling of a correlation matrix <b> R</b> can be a difficult statistical task due to both the positive definite and the unit diagonal constraints. Because the number of parameters increases quadratically in the dimension, it is often useful to consider a sparse parameterization. We introduce a pair of prior distributions on the set of correlation matrices for longitudinal data through the partial autocorrelations (PACs), which vary independently over (−1,1). The first prior shrinks each of the PACs toward zero with increasingly aggressive shrinkage in lag. The second prior (a selection prior) is a mixture of a zero point mass and a continuous component for each PAC, allowing for a sparse representation. The structure implied under our priors is readily interpretable for time-ordered responses because each zero PAC implies a conditional independence relationship in the distribution of the data. Selection priors on the PACs provide a computationally attractive alternative to selection on the elements of <b> R</b> or <b> R</b><sup>− 1</sup> for ordered data. These priors allow for data-dependent shrinkage/selection under an intuitive parameterization in an unconstrained setting. The proposed priors are compared to standard methods through a simulation study and illustrated using a multivariate probit data example. Supplemental materials for this article (appendix, data, and R code) are available online.
DOI: 10.6084/m9.figshare.1209697.v1
title: Sufficient Dimension Folding for Regression Mean Function

description: In this article, we consider sufficient dimension folding for the regression mean function when predictors are matrix- or array-valued. We propose a new concept named central mean dimension folding subspace and its two local estimation methods: folded outer product of gradients estimation (folded-OPG) and folded minimum average variance estimation (folded-MAVE). We establish the asymptotic properties for folded-MAVE. A modified BIC criterion is used to determine the dimensions of the central mean dimension folding subspace. We evaluate the performances of the two local estimation methods by simulated examples and demonstrate the efficacy of folded-MAVE in finite samples. And in particular, we apply our methods to analyze a longitudinal study of primary biliary cirrhosis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1209697.v3
title: Sufficient Dimension Folding for Regression Mean Function

description: In this article, we consider sufficient dimension folding for the regression mean function when predictors are matrix- or array-valued. We propose a new concept named central mean dimension folding subspace and its two local estimation methods: folded outer product of gradients estimation (folded-OPG) and folded minimum average variance estimation (folded-MAVE). We establish the asymptotic properties for folded-MAVE. A modified BIC criterion is used to determine the dimensions of the central mean dimension folding subspace. We evaluate the performances of the two local estimation methods by simulated examples and demonstrate the efficacy of folded-MAVE in finite samples. And in particular, we apply our methods to analyze a longitudinal study of primary biliary cirrhosis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1209696.v3
title: Sparsity Inducing Prior Distributions for Correlation Matrices of Longitudinal Data

description: For longitudinal data, the modeling of a correlation matrix <b> R</b> can be a difficult statistical task due to both the positive definite and the unit diagonal constraints. Because the number of parameters increases quadratically in the dimension, it is often useful to consider a sparse parameterization. We introduce a pair of prior distributions on the set of correlation matrices for longitudinal data through the partial autocorrelations (PACs), which vary independently over (−1,1). The first prior shrinks each of the PACs toward zero with increasingly aggressive shrinkage in lag. The second prior (a selection prior) is a mixture of a zero point mass and a continuous component for each PAC, allowing for a sparse representation. The structure implied under our priors is readily interpretable for time-ordered responses because each zero PAC implies a conditional independence relationship in the distribution of the data. Selection priors on the PACs provide a computationally attractive alternative to selection on the elements of <b> R</b> or <b> R</b><sup>− 1</sup> for ordered data. These priors allow for data-dependent shrinkage/selection under an intuitive parameterization in an unconstrained setting. The proposed priors are compared to standard methods through a simulation study and illustrated using a multivariate probit data example. Supplemental materials for this article (appendix, data, and R code) are available online.
DOI: 10.6084/m9.figshare.1209696.v2
title: Sparsity Inducing Prior Distributions for Correlation Matrices of Longitudinal Data

description: For longitudinal data, the modeling of a correlation matrix <b> R</b> can be a difficult statistical task due to both the positive definite and the unit diagonal constraints. Because the number of parameters increases quadratically in the dimension, it is often useful to consider a sparse parameterization. We introduce a pair of prior distributions on the set of correlation matrices for longitudinal data through the partial autocorrelations (PACs), which vary independently over (−1,1). The first prior shrinks each of the PACs toward zero with increasingly aggressive shrinkage in lag. The second prior (a selection prior) is a mixture of a zero point mass and a continuous component for each PAC, allowing for a sparse representation. The structure implied under our priors is readily interpretable for time-ordered responses because each zero PAC implies a conditional independence relationship in the distribution of the data. Selection priors on the PACs provide a computationally attractive alternative to selection on the elements of <b> R</b> or <b> R</b><sup>− 1</sup> for ordered data. These priors allow for data-dependent shrinkage/selection under an intuitive parameterization in an unconstrained setting. The proposed priors are compared to standard methods through a simulation study and illustrated using a multivariate probit data example. Supplemental materials for this article (appendix, data, and R code) are available online.
DOI: 10.6084/m9.figshare.1209697.v2
title: Sufficient Dimension Folding for Regression Mean Function

description: In this article, we consider sufficient dimension folding for the regression mean function when predictors are matrix- or array-valued. We propose a new concept named central mean dimension folding subspace and its two local estimation methods: folded outer product of gradients estimation (folded-OPG) and folded minimum average variance estimation (folded-MAVE). We establish the asymptotic properties for folded-MAVE. A modified BIC criterion is used to determine the dimensions of the central mean dimension folding subspace. We evaluate the performances of the two local estimation methods by simulated examples and demonstrate the efficacy of folded-MAVE in finite samples. And in particular, we apply our methods to analyze a longitudinal study of primary biliary cirrhosis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1209691
title: Review Template

description: review template
DOI: 10.6084/m9.figshare.1209662
title: Development of a Screening Tool for Sleep Disordered Breathing in Children Using the Phone Oximeter

description: This dataset contains 147 csv files : 146 subject*number files and one AHI.csv file. - Each subject*number*.csv file contains the pleth and SpO2signals recorded by phone oximeter for subject with number *number*. The pleth signal was sampled at 62.5 Hz and the SpO2 signal was sampled at 1 HZ. EXPLANATION of COLUMNS “pleth” : pleth signal “spo2” : SpO2 signal - AHI.csv file contains the Apnea/Hypopnea Index for all subjects. EXPLANATION of COLUMNS “subjectNumber” : subject number compatible with each file number “AHI” : Apnea/Hypopnea Index
DOI: 10.6084/m9.figshare.1209652
title: Comparative cytogenetic analysis of <i>Cestrum</i> (Solanaceae) reveals different trends in heterochromatin and rDNA sites distribution

description: Species of <i>Cestrum</i> L. (Solanaceae) exhibit large variability in the accumulation of repetitive DNA, although their species possess a stable diploid number with 2<i>n</i> = 16. In this study, we used chromosome banding and fluorescence <i>in situ</i> hybridization (FISH) to characterize the karyotypes and populations of two species, <i>Cestrum nocturnum</i> L. and <i>C. mariquitense</i> Kunth. We also performed a karyotype comparison using 16 idiograms, of which 4 were developed in this study and 12 were obtained from the literature. <i>Cestrum nocturnum</i> displayed more bands than <i>C. mariquitense</i>, but the latter exhibited greater interpopulational variation in the band patterns. There was a tendency for large bands to be located at intercalary/terminal regions and for small bands to be located at intermediate/proximal regions. The idiogram comparison revealed a large variation in the amount, distribution, and size of heterochromatic bands. FISH with rDNA probes revealed stability in the number and location of 5S sites, while 45S was more variable in size and number of sites. Although 45S rDNA always appeared in the subterminal regions, this DNA family exhibited a mobility among chromosome pairs. These data highlight the dynamic of repetitive DNA families in these genomes, as well as the contribution for intra- and interspecific karyotype differentiation in <i>Cestrum</i>.
DOI: 10.6084/m9.figshare.1209652.v1
title: Comparative cytogenetic analysis of <i>Cestrum</i> (Solanaceae) reveals different trends in heterochromatin and rDNA sites distribution

description: Species of <i>Cestrum</i> L. (Solanaceae) exhibit large variability in the accumulation of repetitive DNA, although their species possess a stable diploid number with 2<i>n</i> = 16. In this study, we used chromosome banding and fluorescence <i>in situ</i> hybridization (FISH) to characterize the karyotypes and populations of two species, <i>Cestrum nocturnum</i> L. and <i>C. mariquitense</i> Kunth. We also performed a karyotype comparison using 16 idiograms, of which 4 were developed in this study and 12 were obtained from the literature. <i>Cestrum nocturnum</i> displayed more bands than <i>C. mariquitense</i>, but the latter exhibited greater interpopulational variation in the band patterns. There was a tendency for large bands to be located at intercalary/terminal regions and for small bands to be located at intermediate/proximal regions. The idiogram comparison revealed a large variation in the amount, distribution, and size of heterochromatic bands. FISH with rDNA probes revealed stability in the number and location of 5S sites, while 45S was more variable in size and number of sites. Although 45S rDNA always appeared in the subterminal regions, this DNA family exhibited a mobility among chromosome pairs. These data highlight the dynamic of repetitive DNA families in these genomes, as well as the contribution for intra- and interspecific karyotype differentiation in <i>Cestrum</i>.
DOI: 10.6084/m9.figshare.1209636
title: UCSC Full Length Lambda 2D Read

description: This is the full-length lambda 2D read from Dr. Mark Akeson's MinION talk on October 20, 2014.
DOI: 10.6084/m9.figshare.1209621
title: Primary Data on Prosthetic Vascular Graft Infections

description: Primary Data for PlosOne
DOI: 10.6084/m9.figshare.1209636.v1
title: UCSC Full Length Lambda 2D Read

description: This is the full-length lambda 2D read from Dr. Mark Akeson's MinION talk on October 20, 2014.
DOI: 10.6084/m9.figshare.1209620
title: Gossip-Based Solutions for Discrete Rendezvous in Populations of Communicating Agents

description: Data supporting the results of the PLoS ONE journal paper "Gossip-Based Solutions for Discrete Rendezvous in Populations of Communicating Agents"
DOI: 10.6084/m9.figshare.1209607
title: Design experiment lab04

description: We will place 12 pantraps of alternative colors in three rows in grasslands of york university. Begininng at 2:30 pm Monday October 20th untill 5:30 pm same day, for a total of three hours. Then we will count the total macroinvertebrates in each pantrap and also take note of different types. If we needed another run we will repeat the same experiment for another three hours on a different day.