scholarly journals BPG: Seamless, Automated and Interactive Visualization of Scientific Data

2017 ◽  
Author(s):  
Christine P’ng ◽  
Jeffrey Green ◽  
Lauren C. Chong ◽  
Daryl Waggott ◽  
Stephenie D. Prokopec ◽  
...  
Keyword(s):  
Open Source ◽  
Interactive Visualization ◽  
Scientific Data ◽  
High Dimensional ◽  
Seamless Integration ◽  
Web Based ◽  
Link Type ◽  
Interactive Tool ◽  
High Dimensional Datasets ◽  
Publication Quality

AbstractWe introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

scholarly journals GAC: Gene Associations with Clinical, a web based application

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1039
Author(s):  
Xinyan Zhang ◽  
Manali Rupji ◽  
Jeanne Kowalski
Keyword(s):  
High Dimensional Data ◽  
Interactive Visualization ◽  
High Dimensional ◽  
Forest Plot ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Link Type ◽  
Time To Event Data ◽  
Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

scholarly journals GAC: Gene Associations with Clinical, a web based application

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1039
Author(s):  
Xinyan Zhang ◽  
Manali Rupji ◽  
Jeanne Kowalski
Keyword(s):  
High Dimensional Data ◽  
Interactive Visualization ◽  
High Dimensional ◽  
Forest Plot ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Link Type ◽  
Time To Event Data ◽  
Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

scholarly journals GAC: Gene Associations with Clinical, a web based application

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1039
Author(s):  
Xinyan Zhang ◽  
Manali Rupji ◽  
Jeanne Kowalski
Keyword(s):  
High Dimensional Data ◽  
Interactive Visualization ◽  
High Dimensional ◽  
Forest Plot ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Link Type ◽  
Time To Event Data ◽  
Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

scholarly journals GAC: Gene Associations with Clinical, a web based application

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1039 ◽  
Author(s):  
Xinyan Zhang ◽  
Manali Rupji ◽  
Jeanne Kowalski
Keyword(s):  
High Dimensional Data ◽  
Interactive Visualization ◽  
High Dimensional ◽  
Forest Plot ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Link Type ◽  
Time To Event Data ◽  
Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

scholarly journals spot: Open Source framework for scientific data repository and interactive visualization

SoftwareX ◽  
2019 ◽  
Vol 9 ◽  
pp. 328-331 ◽  
Author(s):  
Faruk Diblen ◽  
Jisk Attema ◽  
Rena Bakhshi ◽  
Sascha Caron ◽  
Luc Hendriks ◽  
...  
Keyword(s):  
Open Source ◽  
Interactive Visualization ◽  
Scientific Data ◽  
Data Repository ◽  
Open Source Framework

Web-Based Large-Scale 3D-Geovisualisation Using WebGL

2012 ◽  
Vol 1 (3) ◽  
pp. 16-25 ◽  
Author(s):  
Martin Christen ◽  
Stephan Nebiker ◽  
Benjamin Loesch
Keyword(s):  
Open Source ◽  
Broad Spectrum ◽  
Large Scale ◽  
Interactive Visualization ◽  
Web Browsers ◽  
Web Based ◽  
Virtual Globe ◽  
Open Source Framework ◽  
The Creation

In this paper, the authors present the OpenWebGlobe project (http://www.openwebglobe.org). The authors also discuss the OpenWebGlobe SDK. OpenWebGlobe SDK is an open source framework for creating massive 3D virtual globe environments and interactively exploiting them in web browsers using HTML5 and WebGL, allowing for the creation of large scale virtual 3D globes with detailed contents and their interactive visualization directly within a broad spectrum of Web browsers.

scholarly journals NASQAR: A web-based platform for high-throughput sequencing data analysis and visualization

2019 ◽  
Author(s):  
Ayman Yousif ◽  
Nizar Drou ◽  
Jillian Rowe ◽  
Mohammed Khalfan ◽  
Kristin C Gunsalus
Keyword(s):  
New York ◽  
Data Analysis ◽  
Open Source ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Web Applications ◽  
Rna Seq ◽  
Sequencing Data ◽  
Web Based ◽  
Link Type

AbstractBackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

scholarly journals Simulation and Exergy Analysis of a Refrigeration System Using an Open-Source Web-Based Interactive Tool—Comparison of the Conventional Approach and a Novel One for Avoidable Exergy Destruction Estimation

2021 ◽  
Vol 11 (23) ◽  
pp. 11535
Author(s):  
Volodymyr Voloshchuk ◽  
Paride Gullo ◽  
Eugene Nikiforovich ◽  
Nadia Buyak
Keyword(s):  
Energy Conversion ◽  
Open Source ◽  
Exergy Analysis ◽  
Exergy Destruction ◽  
Refrigeration System ◽  
Web Based ◽  
New Approach ◽  
Interactive Tool ◽  
Conversion System ◽  
Energy Conversion System

Avoidable endogenous/exogenous parts of the exergy destruction in the components of an energy conversion system can be computed by applying advanced exergy analysis. Their calculation is crucial for the correct assessment of the real thermodynamic enhancement achievable by the investigated energy conversion system. This work proposes a new approach to estimate the avoidable exergy destruction rates of system components, being more rigorous compared to the conventional method due to the elimination of the need for the implementation of theoretical assumptions associated with the idealization of processes. An open-source web-based interactive tool was implemented to contrast the results of the conventional advanced exergy analysis to those involving the new approach for avoidable exergy destruction estimation. The comparison was based on the same case study, i.e., a refrigeration system selected from the literature. It was observed that the developed tool can be properly employed for comparing the two approaches within exergy analyses, and the results obtained presented some differences for the compressor and the condenser. Compared to the new approach, the existing methodology of advanced exergy analysis suggests lower values of the avoidable part of exergy destruction, which can be reduced by improving the efficiency of the compressor and the condenser. Moreover, the avoidable parts of exergy destruction, which could be removed within these components by improving the efficiencies of the remaining components, were higher in the case of the application of the existing advanced exergetic analysis as compared with the findings obtained by the proposed approach. These differences were due to the impossibility of the existing advanced exergy analysis to implement complete thermodynamic “idealization” for the condenser and evaporator.

scholarly journals cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices

2021 ◽  
Author(s):  
Colin Megill ◽  
Bruce Martin ◽  
Charlotte Weaver ◽  
Sidney Bell ◽  
Lia Prins ◽  
...  
Keyword(s):  
Single Cell ◽  
Sparse Matrices ◽  
Hypothesis Generation ◽  
High Dimensional ◽  
Web Based ◽  
Critical Gap ◽  
Spatial Dimensions ◽  
Private And Public ◽  
Public Datasets ◽  
High Dimensional Datasets

Quickly and flexibly exploring high-dimensional datasets, such as scRNAseq data, is underserved but critical for hypothesis generation, dataset annotation, publication, sharing, and community reuse. cellxgene is a highly generalizable, web-based interface for exploring high dimensional datasets along categorical, continuous and spatial dimensions, as well as feature annotation. cellxgene is differentiated by its ability to performantly handle millions of observations, and bridges a critical gap by enabling computational and experimental biologists to iteratively ask questions of private and public datasets. In doing so, cellxgene increases the utility and reusability of datasets across the single-cell ecosystem. The codebase can be accessed at https://github.com/chanzuckerberg/cellxgene. For questions and inquiries, please contact [email protected].

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