The lrd package: An R package and Shiny application for processing lexical data

Author(s):  
Nicholas P. Maxwell ◽  
Mark J. Huff ◽  
Erin M. Buchanan
2018 ◽  
Author(s):  
Jianfeng Li ◽  
Bowen Cui ◽  
Yuting Dai ◽  
Ling Bai ◽  
Jinyan Huang

The number of bioinformatics resources, such as tools/scripts and databases are growing exponentially. This poses a great challenge for users to access, manage, and integrate the corresponding bioinformatics resources. To facilitate the request, we proposed a comprehensive R package, BioInstaller, which includes the R functions, Shiny application, and the HTTP representational state transfer (REST) application programming interfaces (APIs). We also established a community-based configuration pool to collect, access and share bioinformatics resources. The source code of BioInstaller is freely available at our lab website http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller or popular package host GitHub at: https://github.com/JhuangLab/BioInstaller. Also, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller).


2020 ◽  
Author(s):  
Na Liu ◽  
Yanhong Zhou ◽  
J. Jack Lee

Abstract BackgroundWhen applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to the IPD. We aim to propose a straightforward and robust approach to help researchers to obtain IPD from published survival curves with a friendly software platform. ResultsImproving upon the existing methods, we proposed an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we develop the R package IPDfromKM and an accompanied web-based Shiny application. Both the R package and Shiny application can be used to extract raw data coordinates from published K-M curves, reconstruct IPD from data coordinates extracted, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, as well as hazard ratios. ConclusionsIPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD data and advance the use of secondary data to make informed decision in medical research.


2019 ◽  
Vol 35 (22) ◽  
pp. 4827-4829 ◽  
Author(s):  
Xiao-Fei Zhang ◽  
Le Ou-Yang ◽  
Shuo Yang ◽  
Xing-Ming Zhao ◽  
Xiaohua Hu ◽  
...  

Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis. Availability and implementation The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Supplementary data are available at Bioinformatics online.


Author(s):  
Janina Reeder ◽  
Mo Huang ◽  
Joshua S Kaminker ◽  
Joseph N Paulson

Abstract Summary We developed the MicrobiomeExplorer R package to facilitate the analysis and visualization of microbial communities. The MicrobiomeExplorer R package allows a user to perform typical microbiome analytic workflows and visualize their results, either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows, the application enables automated analysis report generation. Availability and implementation Available at https://github.com/zoecastillo/microbiomeExplorer. Supplementary information Supplementary data are available at Bioinformatics online.


JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
David M Miller ◽  
Sophia Z Shalhout

Abstract Objectives Clinico-genomic data (CGD) acquired through routine clinical practice has the potential to improve our understanding of clinical oncology. However, these data often reside in heterogeneous and semistructured data, resulting in prolonged time-to-analyses. Materials and Methods We created GENETEX: an R package and Shiny application for text mining genomic reports from electronic health record (EHR) and direct import into Research Electronic Data Capture (REDCap). Results GENETEX facilitates the abstraction of CGD from EHR and streamlines the capture of structured data into REDCap. Its functions include natural language processing of key genomic information, transformation of semistructured data into structured data, and importation into REDCap. When evaluated with manual abstraction, GENETEX had >99% agreement and captured CGD in approximately one-fifth the time. Conclusions GENETEX is freely available under the Massachusetts Institute of Technology license and can be obtained from GitHub (https://github.com/TheMillerLab/genetex). GENETEX is executed in R and deployed as a Shiny application for non-R users. It produces high-fidelity abstraction of CGD in a fraction of the time.


PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5853 ◽  
Author(s):  
Jianfeng Li ◽  
Bowen Cui ◽  
Yuting Dai ◽  
Ling Bai ◽  
Jinyan Huang

The increase in bioinformatics resources such as tools/scripts and databases poses a great challenge for users seeking to construct interactive and reproducible biological data analysis applications. Here, we propose an open-source, comprehensive, flexible R package named BioInstaller that consists of the R functions, Shiny application, the HTTP representational state transfer application programming interfaces, and a docker image. BioInstaller can be used to collect, manage and share various types of bioinformatics resources and perform interactive and reproducible data analyses based on the extendible Shiny application with Tom’s Obvious, Minimal Language and SQLite format databases. The source code of BioInstaller is freely available at our lab website, http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller, the popular package host GitHub, https://github.com/JhuangLab/BioInstaller, and the Comprehensive R Archive Network, https://CRAN.R-project.org/package=BioInstaller. In addition, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller/bioinstaller).


2018 ◽  
Author(s):  
Jianfeng Li ◽  
Bowen Cui ◽  
Yuting Dai ◽  
Ling Bai ◽  
Jinyan Huang

The number of bioinformatics resources, such as tools/scripts and databases are growing exponentially. This poses a great challenge for users to access, manage, and integrate the corresponding bioinformatics resources. To facilitate the request, we proposed a comprehensive R package, BioInstaller, which includes the R functions, Shiny application, and the HTTP representational state transfer (REST) application programming interfaces (APIs). We also established a community-based configuration pool to collect, access and share bioinformatics resources. The source code of BioInstaller is freely available at our lab website http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller or popular package host GitHub at: https://github.com/JhuangLab/BioInstaller. Also, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller).


2020 ◽  
Author(s):  
Michael A. Tabak ◽  
Jesse S. Lewis ◽  
Peter E Schlichting ◽  
Nathan P. Snow ◽  
Kurt C. VerCauteren ◽  
...  

AbstractrapidPop is a new R package for implementing occupancy models for Rapid Population Assessments (RPAs) with data from camera traps. RPAs are designed to provide quick assessments of a density index so that users can identify relative changes in density associated with changes in conditions (Schlichting et al., 2020). For example, users may want to assess if there was a change in density after an effort to cull an invasive species. rapidPop provides a Shiny Application for running occupancy models with the option to include the effect of parameters on occupancy (e.g., before or after a culling operation).


PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0262145
Author(s):  
Olatunji Johnson ◽  
Claudio Fronterre ◽  
Peter J. Diggle ◽  
Benjamin Amoah ◽  
Emanuele Giorgi

User-friendly interfaces have been increasingly used to facilitate the learning of advanced statistical methodology, especially for students with only minimal statistical training. In this paper, we illustrate the use of MBGapp for teaching geostatistical analysis to population health scientists. Using a case-study on Loa loa infections, we show how MBGapp can be used to teach the different stages of a geostatistical analysis in a more interactive fashion. For wider accessibility and usability, MBGapp is available as an R package and as a Shiny web-application that can be freely accessed on any web browser. In addition to MBGapp, we also present an auxiliary Shiny app, called VariagramApp, that can be used to aid the teaching of Gaussian processes in one and two dimensions using simulations.


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