This RShiny app has been developed to easily perform a survival analysis. After setting up the analysis, you can build:

1. Table of Characteristics
2. Kaplan-Meier Graph
3. Cox Model

This application aids in the visualisation of GWAS results by displaying several plots:

1. Interactive Manhattan plot
2. Circular Manhattan plots
3. Quantile-quantile (QQ) plot
4. SNP density plot

This RShiny app allows the user to select a dataset and examine it using descriptive statistics and plots automatically generated for each variable.

This RShiny app enables you to quickly visualize and assess relationships within and between the nominal variables in your data. It allows you to perform Tests of Goodness of Fit and Tests of Independence.

This RShiny mini-app aids you with study design by taking the results of your pilot study and showing the required number of participants per group to detect the observed difference with the desired power.

You can also calculate sample sizes for clustered studies and studies with binary dependent variables.

This mini-app allows you to calculate and visualise several linear regression models for any dataset within your workspace or ‘data’ folder.

This mini-app displays a Circos-style plot, which is used to visually whole genomes. These circular plots facilitate the identification and analysis of similarities and differences arising from the comparison of genomes.

This mini-app allows you to view the distribution of a numeric variable, and split that variable into two groups by any categoric variable in that dataset. These two groups can then be graphically compared. In addition, a t-test can be performed, either between two groups (two-sample) or between one group and a stated mean (one-sample).

This R shiny mini-app reads and combines mock up data about: clinic codes, demographic, genotype and clinical measures.

1. Compare cohorts side-by-side
2. Explore the participants list
3. Print an automatically generated PDF template report

This mini-app allows users to interactively visualize and explore 3D DICOM images using oro.dicom and oro.nifti packages. The exemplar images are results from Magnetic Resonances of the brain.

Uses data from the United Nations Population Division, World Health Organization, United Nations Children’s Fund amongst others to visualize world development indicators, such as data about health systems, disease prevention, reproductive health, nutrition, and population dynamics.

Mini-app to visualise drug prescription across various spatial district data zones in London. The app uses data about London prescriptions between 2011-2013, aggregated by Clinical Commisioning Group (CCG) regions.

This mini-app shows a circular visualisation of world population movement. You can explore the estimates of migration flows between and within regions for five-year periods, from 1990 to 2010.

This RShiny mini-app a visualisation tool that uses the PARR-30 model to calculate the predicted risk of readmission within 30 days of a sample dataset; according to this score the patients are classified as having either high, medium or low risk, using thresholds selected by the user. It also allows the user to calculate a PARR-30 risk score of a patient.

This RShiny mini-app provides a retinopathy image viewer and a calculated risk score for each patient, as well as, demographic information and the clinical history of the patient.

This RShiny mini-app uses simple text mining techniques to extract information from free text from radiology reports. This way, the app processes and analyses unstructured data in order to retrieve information from it.

This RShiny mini-app offers a quick visualisation of the treatment waiting times of people referred by GP with suspected cancer or breast symptoms and those subsequently diagnosed with and treated for cancer by the NHS in England.

This RShiny mini-app helps to quickly analyse and visualise the results obtained from RNA-Seq techniques. This app works with data that has already been through the processing pipeline. It includes the following analyses: Principal Component Analysis (PCA), Gene Clusters, Differential Gene Expression Analysis and Quality Control Summary.

Allows the user to perform basic exploratory analysis of Variant Calling Format (VCF) files, which are used for storing gene sequence variations. The different tabs allow you to browse the variations and visualise the SNP density and type distribution of each chromosome.