Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct ...Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct power functions to select the optimal sample size. We revise this approach when the focus is on testing a single binomial proportion. We consider exact methods and introduce a conservative criterion to account for the typical non-monotonic behavior of the power functions, when dealing with discrete data. The main purpose of this paper is to present a Shiny App providing a user-friendly, interactive tool to apply these criteria. The app also provides specific tools to elicit the analysis and the design prior distributions, which are the core of the two-priors approach.展开更多
Objective:As an important part of metabolomics analysis,untargeted metabolomics has become a powerful tool in the study of tumor mechanisms and the discovery of metabolic markers with high-throughput spectrometric dat...Objective:As an important part of metabolomics analysis,untargeted metabolomics has become a powerful tool in the study of tumor mechanisms and the discovery of metabolic markers with high-throughput spectrometric data which also poses great challenges to data analysis,from the extraction of raw data to the identification of differential metabolites.To date,a large number of analytical tools and processes have been developed and constructed to serve untargeted metabolomics research.The different selection of analytical tools and parameter settings lead to varied results of untargeted metabolomics data.Our goal is to establish an easily operated platform and obtain a repeatable analysis result.Methods:We used the R language basic environment to construct the preprocessing system of the original data and the LAMP(Linux+Apache+MySQL+PHP)architecture to build a cloud mass spectrum data analysis system.Results:An open-source analysis software for untargeted metabolomics data(openNAU)was constructed.It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks.A reference metabolomics database based on public databases was also constructed.Conclusions:A complete analysis system platform for untargeted metabolomics was established.This platform provides a complete template interface for the addition and updating of the analysis process,so we can finish complex analyses of untargeted metabolomics with simple human-computer interactions.The source code can be downloaded from https://github.com/zjuRong/openNAU.展开更多
植被物候作为反映植被与气候变化关系的重要参量,具有重要的研究意义。本文基于R语言分布式架构Shiny构建了植被物候参数分析系统,可实现站点分布可视化、感兴趣区(Region of Interest,ROI)选取与绘制、植被指数计算与可视化、数据过滤...植被物候作为反映植被与气候变化关系的重要参量,具有重要的研究意义。本文基于R语言分布式架构Shiny构建了植被物候参数分析系统,可实现站点分布可视化、感兴趣区(Region of Interest,ROI)选取与绘制、植被指数计算与可视化、数据过滤、生长曲线轨迹拟合与物候参数提取等功能模块。用户可提取不同植被类型数码相机时间序列的植被指数,并用max方法进行平滑与去噪处理,然后选择合适的方法组合拟合植被群落季相变化轨迹,最终提取较为精确的关键物候参数。林地数据系统测试结果表明:1)相对绿度指数GI比其他相对植被指数和单波段的亮度值振幅明显,其时间序列可表征植被实际生长轨迹; 2)不同拟合方法与提取方法的组合效果不同,如klosterman与klosterman方法组合适合林地类型的植被物候参数提取,用户可综合均方根误差与季节群相变化轨迹结果,筛选出适合所选植被物候数据的拟合与物候参数提取方法组合。展开更多
文摘Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct power functions to select the optimal sample size. We revise this approach when the focus is on testing a single binomial proportion. We consider exact methods and introduce a conservative criterion to account for the typical non-monotonic behavior of the power functions, when dealing with discrete data. The main purpose of this paper is to present a Shiny App providing a user-friendly, interactive tool to apply these criteria. The app also provides specific tools to elicit the analysis and the design prior distributions, which are the core of the two-priors approach.
文摘Objective:As an important part of metabolomics analysis,untargeted metabolomics has become a powerful tool in the study of tumor mechanisms and the discovery of metabolic markers with high-throughput spectrometric data which also poses great challenges to data analysis,from the extraction of raw data to the identification of differential metabolites.To date,a large number of analytical tools and processes have been developed and constructed to serve untargeted metabolomics research.The different selection of analytical tools and parameter settings lead to varied results of untargeted metabolomics data.Our goal is to establish an easily operated platform and obtain a repeatable analysis result.Methods:We used the R language basic environment to construct the preprocessing system of the original data and the LAMP(Linux+Apache+MySQL+PHP)architecture to build a cloud mass spectrum data analysis system.Results:An open-source analysis software for untargeted metabolomics data(openNAU)was constructed.It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks.A reference metabolomics database based on public databases was also constructed.Conclusions:A complete analysis system platform for untargeted metabolomics was established.This platform provides a complete template interface for the addition and updating of the analysis process,so we can finish complex analyses of untargeted metabolomics with simple human-computer interactions.The source code can be downloaded from https://github.com/zjuRong/openNAU.
文摘植被物候作为反映植被与气候变化关系的重要参量,具有重要的研究意义。本文基于R语言分布式架构Shiny构建了植被物候参数分析系统,可实现站点分布可视化、感兴趣区(Region of Interest,ROI)选取与绘制、植被指数计算与可视化、数据过滤、生长曲线轨迹拟合与物候参数提取等功能模块。用户可提取不同植被类型数码相机时间序列的植被指数,并用max方法进行平滑与去噪处理,然后选择合适的方法组合拟合植被群落季相变化轨迹,最终提取较为精确的关键物候参数。林地数据系统测试结果表明:1)相对绿度指数GI比其他相对植被指数和单波段的亮度值振幅明显,其时间序列可表征植被实际生长轨迹; 2)不同拟合方法与提取方法的组合效果不同,如klosterman与klosterman方法组合适合林地类型的植被物候参数提取,用户可综合均方根误差与季节群相变化轨迹结果,筛选出适合所选植被物候数据的拟合与物候参数提取方法组合。
