cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23305088
title: Additional file 7 of Single-cell and spatial transcriptomics reveal changes in cell heterogeneity during progression of human tendinopathy

description: Additional file 7: Table S5. Differentially expressed genes of different endothelial cell subcluster.
DOI: 10.6084/m9.figshare.7590875
title: On Analysis of Changes in Rates and Averaged Correlated Rates

description: Changes in rates and averaged correlated rates are two common analyses for binary response data in longitudinal factorial studies. The traditional logistic regression for repeated measures can be used to obtain statistical inference for these two analyses, however, it has two potential deficiencies: first, the model may not converge, and second, the resulted statistical inference in terms of a linear contrast of odds ratios seems hard to be interpreted and understood by practitioners. The current article applies the methods in Fan and derives statistical inferential procedures for the analysis of changes in rates and averaged correlated rates in terms of the rate difference (RD), the odds ratio (OR), and the rate ratio (RR). Numerical assessments through simulation studies and examples were employed to demonstrate advantages of statistical inference in terms of the RD over the OR or the RR in different aspects including model convergence and interpretation of the results. The current article can be viewed as special case studies of the methods by Fan while it provides clearer and more direct comparisons of the methods when analyzing changes in rates and averaged correlated rates in longitudinal factorial studies.
DOI: 10.6084/m9.figshare.7590875.v2
title: On Analysis of Changes in Rates and Averaged Correlated Rates

description: Changes in rates and averaged correlated rates are two common analyses for binary response data in longitudinal factorial studies. The traditional logistic regression for repeated measures can be used to obtain statistical inference for these two analyses, however, it has two potential deficiencies: first, the model may not converge, and second, the resulted statistical inference in terms of a linear contrast of odds ratios seems hard to be interpreted and understood by practitioners. The current article applies the methods in Fan and derives statistical inferential procedures for the analysis of changes in rates and averaged correlated rates in terms of the rate difference (RD), the odds ratio (OR), and the rate ratio (RR). Numerical assessments through simulation studies and examples were employed to demonstrate advantages of statistical inference in terms of the RD over the OR or the RR in different aspects including model convergence and interpretation of the results. The current article can be viewed as special case studies of the methods by Fan while it provides clearer and more direct comparisons of the methods when analyzing changes in rates and averaged correlated rates in longitudinal factorial studies.
DOI: 10.6084/m9.figshare.7590866.v2
title: Cauchy Combination Test: A Powerful Test With Analytic <i>p</i>-Value Calculation Under Arbitrary Dependency Structures

description: <b><i>Abstract–</i>Combining individual <i>p</i>-values to aggregate multiple small effects has a long-standing interest in statistics, dating back to the classic Fisher’s combination test. In modern large-scale data analysis, correlation and sparsity are common features and efficient computation is a necessary requirement for dealing with massive data. To overcome these challenges, we propose a new test that takes advantage of the Cauchy distribution. Our test statistic has a simple form and is defined as a weighted sum of Cauchy transformation of individual <i>p</i>-values. We prove a nonasymptotic result that the tail of the null distribution of our proposed test statistic can be well approximated by a Cauchy distribution under arbitrary dependency structures. Based on this theoretical result, the <i>p</i>-value calculation of our proposed test is not only accurate, but also as simple as the classic <i>z</i>-test or <i>t</i>-test, making our test well suited for analyzing massive data. We further show that the power of the proposed test is asymptotically optimal in a strong sparsity setting. Extensive simulations demonstrate that the proposed test has both strong power against sparse alternatives and a good accuracy with respect to <i>p</i>-value calculations, especially for very small <i>p</i>-values. The proposed test has also been applied to a genome-wide association study of Crohn’s disease and compared with several existing tests. Supplementary materials for this article are available online.</b>
DOI: 10.6084/m9.figshare.7590866.v1
title: Cauchy combination test: a powerful test with analytic <i>p</i>-value calculation under arbitrary dependency structures<sup>*</sup>

description: Combining individual <i>p</i>-values to aggregate multiple small effects has a long-standing interest in statistics, dating back to the classic Fisher's combination test. In modern large-scale data analysis, correlation and sparsity are common features and efficient computation is a necessary requirement for dealing with massive data. To overcome these challenges, we propose a new test that takes advantage of the Cauchy distribution. Our test statistic has a simple form and is defined as a weighted sum of Cauchy transformation of individual <i>p</i>-values. We prove a non-asymptotic result that the tail of the null distribution of our proposed test statistic can be well approximated by a Cauchy distribution under arbitrary dependency structures. Based on this theoretical result, the <i>p</i>-value calculation of our proposed test is not only accurate, but also as simple as the classic <i>z</i>-test or <i>t</i>-test, making our test well suited for analyzing massive data. We further show that the power of the proposed test is asymptotically optimal in a strong sparsity setting. Extensive simulations demonstrate that the proposed test has both strong power against sparse alternatives and a good accuracy with respect to <i>p</i>-value calculations, especially for very small <i>p</i>-values. The proposed test has also been applied to a genome-wide association study of Crohn's disease and compared with several existing tests.
DOI: 10.6084/m9.figshare.7590875.v1
title: On analysis of changes in rates and averaged correlated rates

description: Changes in rates and averaged correlated rates are two common analyses for binary response data in longitudinal factorial studies. The traditional logistic regression for repeated measures can be used to obtain statistical inference for these two analyses, however, it has two potential deficiencies: first, the model may not converge, and second, the resulted statistical inference in terms of a linear contrast of odds ratios seems hard to be interpreted and understood by practitioners. The current article applies the methods in Fan (2017) and derives statistical inferential procedures for the analysis of changes in rates and averaged correlated rates in terms of the rate difference (RD), the odds ratio (OR), and the rate ratio (RR). Numerical assessments through simulation studies and examples were employed to demonstrate advantages of statistical inference in terms of the RD over the OR or the RR in different aspects including model convergence and interpretation of the results. The current article can be viewed as special case studies of the methods by Fan (2017) while it provides clearer and more direct comparisons of the methods when analyzing changes in rates and averaged correlated rates in longitudinal factorial studies.
DOI: 10.6084/m9.figshare.23305088.v1
title: Additional file 7 of Single-cell and spatial transcriptomics reveal changes in cell heterogeneity during progression of human tendinopathy

description: Additional file 7: Table S5. Differentially expressed genes of different endothelial cell subcluster.
DOI: 10.6084/m9.figshare.13357728
title: Additional file 9 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin

description: Additional file 9. Raw result of ConSurf analysis for HAP40.
DOI: 10.6084/m9.figshare.23305067.v1
title: Additional file 1 of Single-cell and spatial transcriptomics reveal changes in cell heterogeneity during progression of human tendinopathy

description: Additional file 1: Table S1. Donor and sample information.
DOI: 10.3886/icpsr30422
title: Associated Press Health Care Reform Survey, by Stanford University with the Robert Wood Johnson Foundation, August-September 2010 [United States]
title: Archival Version

description: Conducted by Knowledge Networks, this survey measured public opinion about the health care overhaul that was passed by the United States Congress in March 2010. It measured support and opposition to certain general goals of the overhaul, as well as support and opposition to specific parts of the legislation. It had a particular focus on what people knew about the bill and what misperceptions they may have about what was and wasn't in the legislation. In addition, the survey investigated beliefs about the consequences of the legislation on future taxes, health insurance costs, access to health care, and the quality of health care. Other topics investigated by the survey include health status, health insurance status, trust in the federal government, approval/disapproval of the Obama Administration's performance, political ideology, religion, religiosity, and sources of news. The data file also includes demographic information collected by Knowledge Networks' initial KnowledgePanel(R) profile survey, such as age, gender, education, household size and composition, income, marital status, employment status, and ZIP code.
description: The survey was conducted using the web-enabled KnowledgePanel(R), a probability-based panel created by Knowledge Networks designed to be representative of the United States population. Households are recruited into the panel by randomly selecting telephone numbers and residential addresses. With sampling conducted throughout the year, the panel includes about 50,000 adult members ages 18 and older. For all new panel members, demographic information is collected in an online "profile" survey. After the survey is completed, the panel member becomes active and is available to be sampled for other surveys, such as this survey on health care reform. For this survey, (ICPSR 30422), respondents were randomly selected from the adult active panel members.
Knowledge Networks provides an internet appliance and internet service connection to KnowledgePanel recruits who do not already have internet access. People who already have computers and internet service are permitted to participate using their own equipment. Panelists receive unique log-in information for accessing surveys online, and then are sent emails three to four times a month inviting them to participate in research.
More information about KnowledgePanel is available on the
Knowledge Networks Web site.
description: Datasets: 
DS1: Associated Press Health Care Reform Survey, by Stanford University with the Robert Wood Johnson Foundation, August-September 2010 [United States]
description: Adult household population of the United States.
description: web-based surveyThe SPSS data file, ASCII data file, and SPSS setup use the Windows-1252 character encoding.
DOI: 10.3886/icpsr30422.v2
title: Associated Press Health Care Reform Survey, by Stanford University with the Robert Wood Johnson Foundation, August-September 2010 [United States]
title: Version 2

description: Conducted by Knowledge Networks, this survey measured public opinion about the health care overhaul that was passed by the United States Congress in March 2010. It measured support and opposition to certain general goals of the overhaul, as well as support and opposition to specific parts of the legislation. It had a particular focus on what people knew about the bill and what misperceptions they may have about what was and wasn't in the legislation. In addition, the survey investigated beliefs about the consequences of the legislation on future taxes, health insurance costs, access to health care, and the quality of health care. Other topics investigated by the survey include health status, health insurance status, trust in the federal government, approval/disapproval of the Obama Administration's performance, political ideology, religion, religiosity, and sources of news. The data file also includes demographic information collected by Knowledge Networks' initial KnowledgePanel(R) profile survey, such as age, gender, education, household size and composition, income, marital status, employment status, and ZIP code.
description: The survey was conducted using the web-enabled KnowledgePanel(R), a probability-based panel created by Knowledge Networks designed to be representative of the United States population. Households are recruited into the panel by randomly selecting telephone numbers and residential addresses. With sampling conducted throughout the year, the panel includes about 50,000 adult members ages 18 and older. For all new panel members, demographic information is collected in an online "profile" survey. After the survey is completed, the panel member becomes active and is available to be sampled for other surveys, such as this survey on health care reform. For this survey, (ICPSR 30422), respondents were randomly selected from the adult active panel members.
Knowledge Networks provides an internet appliance and internet service connection to KnowledgePanel recruits who do not already have internet access. People who already have computers and internet service are permitted to participate using their own equipment. Panelists receive unique log-in information for accessing surveys online, and then are sent emails three to four times a month inviting them to participate in research.
More information about KnowledgePanel is available on the
Knowledge Networks Web site.
description: Datasets: 
DS1: Associated Press Health Care Reform Survey, by Stanford University with the Robert Wood Johnson Foundation, August-September 2010 [United States]
description: Adult household population of the United States.
description: web-based surveyThe SPSS data file, ASCII data file, and SPSS setup use the Windows-1252 character encoding.
DOI: 10.6084/m9.figshare.23305073
title: Additional file 3 of Single-cell and spatial transcriptomics reveal changes in cell heterogeneity during progression of human tendinopathy

description: Additional file 3: Table S2. Differentially expressed genes of each cluster.
DOI: 10.6084/m9.figshare.7590872.v1
title: L2RM: Low-rank Linear Regression Models for High-dimensional Matrix Responses

description: The aim of this paper is to develop a low-rank linear regression model (L2RM) to correlate a high-dimensional response matrix with a high dimensional vector of covariates when coefficient matrices have low-rank structures. We propose a fast and efficient screening procedure based on the spectral norm of each coefficient matrix in order to deal with the case when the number of covariates is extremely large. We develop an efficient estimation procedure based on the trace norm regularization, which explicitly imposes the low rank structure of coefficient matrices. When both the dimension of response matrix and that of covariate vector diverge at the exponential order of the sample size, we investigate the sure independence screening property under some mild conditions. We also systematically investigate some theoretical properties of our estimation procedure including estimation consistency, rank consistency and non-asymptotic error bound under some mild conditions. We further establish a theoretical guarantee for the overall solution of our two-step screening and estimation procedure. We examine the finite-sample performance of our screening and estimation methods using simulations and a large-scale imaging genetic dataset collected by the Philadelphia Neurodevelopmental Cohort (PNC) study.
DOI: 10.6084/m9.figshare.13357719.v1
title: Additional file 6 of The evolution of the huntingtin-associated protein 40 (HAP40) in conjunction with huntingtin

description: Additional file 6. Phylogenetic tree of HTT orthologs.
DOI: 10.6084/m9.figshare.23305082
title: Additional file 5 of Single-cell and spatial transcriptomics reveal changes in cell heterogeneity during progression of human tendinopathy

description: Additional file 5: Table S4. Enrichment analysis of differentially expressed genes in normal and disease tenocytes.
DOI: 10.15156/bio/sh391521.07fu
title: SH391521.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh391522.07fu
title: SH391522.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1757439.08fu
title: SH1757439.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1757440.08fu
title: SH1757440.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh391523.07fu
title: SH391523.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh391524.07fu
title: SH391524.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.