scholarly journals Peer Review #1 of "MutRank: an R shiny web-application for exploratory targeted mutual rank-based coexpression analyses integrated with user-provided supporting information (v0.1)"

2020 ◽  
Author(s):  
G Ziegler
Keyword(s):  
Peer Review ◽  
Web Application ◽  
R Shiny ◽  
Mutual Rank

scholarly journals MutRank: an R shiny web-application for exploratory targeted mutual rank-based coexpression analyses integrated with user-provided supporting information

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10264
Author(s):  
Elly Poretsky ◽  
Alisa Huffaker
Keyword(s):  
Hypothesis Testing ◽  
Predictive Value ◽  
Web Application ◽  
Biosynthetic Pathway ◽  
Gene Clusters ◽  
Transcript Abundance ◽  
Specialized Metabolites ◽  
Genes Encoding ◽  
R Shiny ◽  
Mutual Rank

The rapid assignment of genotypes to phenotypes has been a historically challenging process. The discovery of genes encoding biosynthetic pathway enzymes for defined plant specialized metabolites has been informed and accelerated by the detection of gene clusters. Unfortunately, biosynthetic pathway genes are commonly dispersed across chromosomes or reside in genes clusters that provide little predictive value. More reliably, transcript abundance of genes underlying biochemical pathways for plant specialized metabolites display significant coregulation. By rapidly identifying highly coexpressed transcripts, it is possible to efficiently narrow candidate genes encoding pathway enzymes and more easily predict both functions and functional associations. Mutual Rank (MR)-based coexpression analyses in plants accurately demonstrate functional associations for many specialized metabolic pathways; however, despite the clear predictive value of MR analyses, the application is uncommonly used to drive new pathway discoveries. Moreover, many coexpression databases aid in the prediction of both functional associations and gene functions, but lack customizability for refined hypothesis testing. To facilitate and speed flexible MR-based hypothesis testing, we developed MutRank, an R Shiny web-application for coexpression analyses. MutRank provides an intuitive graphical user interface with multiple customizable features that integrates user-provided data and supporting information suitable for personal computers. Tabular and graphical outputs facilitate the rapid analyses of both unbiased and user-defined coexpression results that accelerate gene function predictions. We highlight the recent utility of MR analyses for functional predictions and discoveries in defining two maize terpenoid antibiotic pathways. Beyond applications in biosynthetic pathway discovery, MutRank provides a simple, customizable and user-friendly interface to enable coexpression analyses relating to a breadth of plant biology inquiries. Data and code are available at GitHub: https://github.com/eporetsky/MutRank.

scholarly journals MALDIViz: A Comprehensive Informatics Tool for MALDI-MS Data Visualization and Analysis

2017 ◽  
Vol 22 (10) ◽  
pp. 1246-1252 ◽  
Author(s):  
Kishore Kumar Jagadeesan ◽  
Simon Ekström
Keyword(s):  
Mass Spectrometry ◽  
High Throughput ◽  
High Throughput Screening ◽  
Web Application ◽  
Low Cost ◽  
Maldi Ms ◽  
Ionization Mass ◽  
Label Free ◽  
Label Free Detection ◽  
R Shiny

Recently, mass spectrometry (MS) has emerged as an important tool for high-throughput screening (HTS) providing a direct and label-free detection method, complementing traditional fluorescent and colorimetric methodologies. Among the various MS techniques used for HTS, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) provides many of the characteristics required for high-throughput analyses, such as low cost, speed, and automation. However, visualization and analysis of the large datasets generated by HTS MALDI-MS can pose significant challenges, especially for multiparametric experiments. The datasets can be generated fast, and the complexity of the experimental data (e.g., screening many different sorbent phases, the sorbent mass, and the load, wash, and elution conditions) makes manual data analysis difficult. To address these challenges, a comprehensive informatics tool called MALDIViz was developed. This tool is an R-Shiny-based web application, accessible independently of the operating system and without the need to install any program locally. It has been designed to facilitate easy analysis and visualization of MALDI-MS datasets, comparison of multiplex experiments, and export of the analysis results to high-quality images.

scholarly journals A R-SHINY BASED PHENOLOGY ANALYSIS SYSTEM AND CASE STUDY USING DIGITAL CAMERA DATASET

2018 ◽  
Vol XLII-3 ◽  
pp. 2571-2574
Author(s):  
Y. K. Zhou
Keyword(s):  
Data Processing ◽  
Web Application ◽  
Vegetation Index ◽  
Digital Camera ◽  
Parameter Extraction ◽  
Growth Trajectory ◽  
Phenological Data ◽  
Combination Methods ◽  
Parameters Extraction ◽  
R Shiny

Accurate extracting of the vegetation phenology information play an important role in exploring the effects of climate changes on vegetation. Repeated photos from digital camera is a useful and huge data source in phonological analysis. Data processing and mining on phenological data is still a big challenge. There is no single tool or a universal solution for big data processing and visualization in the field of phenology extraction. In this paper, we proposed a R-shiny based web application for vegetation phenological parameters extraction and analysis. Its main functions include phenological site distribution visualization, ROI (Region of Interest) selection, vegetation index calculation and visualization, data filtering, growth trajectory fitting, phenology parameters extraction, etc. the long-term observation photography data from Freemanwood site in 2013 is processed by this system as an example. The results show that: (1) this system is capable of analyzing large data using a distributed framework; (2) The combination of multiple parameter extraction and growth curve fitting methods could effectively extract the key phenology parameters. Moreover, there are discrepancies between different combination methods in unique study areas. Vegetation with single-growth peak is suitable for using the double logistic module to fit the growth trajectory, while vegetation with multi-growth peaks should better use spline method.

scholarly journals Standardised mortality ratios as a user-friendly performance metric and trigger for quality improvement in a Flemish hospital network: multicentre retrospective study

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e029857
Author(s):  
Wim Tambeur ◽  
Pieter Stijnen ◽  
Guy Vanden Boer ◽  
Pieter Maertens ◽  
Caroline Weltens ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Hospital Mortality ◽  
Web Application ◽  
Hospital Admissions ◽  
Diagnosis Related Groups ◽  
Medical Officer ◽  
Custom Made ◽  
Before And After ◽  
R Shiny ◽  
Concomitant Reduction

ObjectiveTo illustrate the development and use of standardised mortality rates (SMRs) as a trigger for quality improvement in a network of 27 hospitals.DesignThis research was a retrospective observational study. The primary outcome was in-hospital mortality. SMRs were calculated for All Patient Refined—Diagnosis-Related Groups (APR-DRGs) that reflect 80% of the Flemish hospital network mortality. Hospital mortality was modelled using logistic regression. The metrics were communicated to the member hospitals using a custom-made R-Shiny web application showing results at the level of the hospital, patient groups and individual patients. Experiences with the metric and strategies for improvement were shared in chief medical officer meetings organised by the Flemish hospital network.Setting27 Belgian hospitals.Participants1 198 717 hospital admissions for registration years 2009–2016.ResultsPatient gender, age, comorbidity as well as admission source and type were important predictors of mortality. Altogether the SMR models had a C-statistic of 88%, indicating good discriminatory capability. Seven out of ten APR-DRGs with the highest percentage of hospitals statistically significantly deviating from the benchmark involved malignancy. The custom-built web application and the trusted environment of the Flemish hospital network created an interoperable strategy to get to work with SMR findings. Use of the web application increased over time, with peaks before and after key discussion meetings within the Flemish hospital network. A concomitant reduction in crude mortality for the selected APR-DRGs from 6.7% in 2009 to 5.9% in 2016 was observed.ConclusionsThis study reported on the phased approach for introducing SMR reporting to trigger quality improvement. Prerequisites for the successful use of quality metrics in hospital benchmarks are a collaborative approach based on trust among the participants and a reporting platform that allows stakeholders to interpret and analyse the results at multiple levels.

scholarly journals EpiExploreR: A Shiny Web Application for the Analysis of Animal Disease Data

2019 ◽  
Vol 7 (12) ◽  
pp. 680 ◽  
Author(s):  
Lara Savini ◽  
Luca Candeloro ◽  
Samuel Perticara ◽  
Annamaria Conte
Keyword(s):  
Disease Transmission ◽  
Web Application ◽  
Control Measure ◽  
Animal Health ◽  
Emerging Infectious Diseases ◽  
Remotely Sensed Data ◽  
Wide Range ◽  
R Shiny ◽  
Public Health Authorities ◽  
Interactive Map

Emerging and re-emerging infectious diseases are a significant public and animal health threat. In some zoonosis, the early detection of virus spread in animals is a crucial early warning for humans. The analyses of animal surveillance data are therefore of paramount importance for public health authorities to identify the appropriate control measure and intervention strategies in case of epidemics. The interaction among host, vectors, pathogen and environment require the analysis of more complex and diverse data coming from different sources. There is a wide range of spatiotemporal methods that can be applied as a surveillance tool for cluster detection, identification of risk areas and risk factors and disease transmission pattern evaluation. However, despite the growing effort, most of the recent integrated applications still lack of managing simultaneously different datasets and at the same time making available an analytical tool for a complete epidemiological assessment. In this paper, we present EpiExploreR, a user-friendly, flexible, R-Shiny web application. EpiExploreR provides tools integrating common approaches to analyze spatiotemporal data on animal diseases in Italy, including notified outbreaks, surveillance of vectors, animal movements data and remotely sensed data. Data exploration and analysis results are displayed through an interactive map, tables and graphs. EpiExploreR is addressed to scientists and researchers, including public and animal health professionals wishing to test hypotheses and explore data on surveillance activities.

scholarly journals Mining human cancer datasets for kallikrein expression in cancer: the ‘KLK-CANMAP’ Shiny web tool

2018 ◽  
Vol 399 (9) ◽  
pp. 983-995
Author(s):  
Chenwei Wang ◽  
Leire Moya ◽  
Judith A. Clements ◽  
Colleen C. Nelson ◽  
Jyotsna Batra
Keyword(s):  
Web Application ◽  
Cancer Survival ◽  
Human Cancer ◽  
Web Tool ◽  
Significant Differential Expression ◽  
Box Plots ◽  
Kaplan Meier ◽  
R Shiny ◽  
Serine Protease Family ◽  
User Friendly

AbstractThe dysregulation of the serine-protease family kallikreins (KLKs), comprising 15 genes, has been reportedly associated with cancer. Their expression in several tissues and physiological fluids makes them potential candidates as biomarkers and therapeutic targets. There are several databases available to mine gene expression in cancer, which often include clinical and pathological data. However, these platforms present some limitations when comparing a specific set of genes and can generate considerable unwanted data. Here, several datasets that showed significant differential expression (p<0.01) in cancer vs. normal (n=118), metastasis vs. primary (n=15) and association with cancer survival (n=21) have been compiled in a user-friendly format from two open and/or publicly available databases Oncomine and OncoLnc for the 15 KLKs. The data have been included in a free web application tool: the KLK-CANMAP https://cancerbioinformatics.shinyapps.io/klk-canmap/. This tool integrates, analyses and visualises data and it was developed with the R Shiny framework. Using KLK-CANMAP box-plots, heatmaps and Kaplan-Meier graphs can be generated for the KLKs of interest. We believe this new cancer KLK focused web tool will benefit the KLK community by narrowing the data visualisation to only the genes of interest.

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