QurvE is an open-source, R package and software
that provides a fully automated pipeline for fitting time-resolved
biological data, including curve fitting, statistical evaluation, model
selection, dose-response analysis, and built-in functions for data
visualization.
Visit the QurvE online application
User manual for QurvE application
User manual for growth curve analysis
User manual for fluorescence curve analysis
The shiny application can be launched from within R via
QurvE::run_app().
QurvE comes in the form of an R package that can be used to create custom workflows and application-specific downstream analyses and is published in the Comprehensive R Archive Network (CRAN) and on GitHub. Alternatively, an intuitive graphical user interface (GUI) implemented as a shiny application for non-programmers is available. The app can be installed locally on Windows computers.
If you encounter bugs or any other issues while using QurvE, please
create an Issue on the Github
Issues page or write us a
message. We welcome any suggestions for new features and improvements to
make QurvE more useful and user-friendly.
Figure: QurvE enables robust,
high-throughput analysis of growth and fluorescence data.
(a)
All functionalities within QurvE are accessible via an intuitive
graphical user interface created with shiny, which can be installed
locally on Windows PCs. (b) Any type of biological growth data can
be analyzed. For commonly used cultivation devices, a growing list of
data parser functions allows the conversion of exported experimental
data into the QurvE-compatible format. In a single computation
workflow, three different types of algorithms are performed on every
sample in a dataset: (c) Five parametric models are fit to the data
to find an equation that best describes the growth curve. (d)
Relevant (log-) linear phases are extracted from each sample to perform
robust linear regression. (e) The representation of data points with
cubic smoothing splines allows extraction of growth rates over time and
applies to any curve shape. (f) Relevant parameters (growth rates,
biomass yields, rate of fluorescence increase, etc.) can be used in
combination with concentration data to analyze dose-response
relationships. This is done by applying either dose-response models or
smoothing splines. (g) Dedicated plot functions facilitate the fit
validation, the interpretation of results, and, due to the availability
of numerous customization options, the generation of suitable for
publication. (h) All computed parameters can be exported as table
files or inspected interactively from within the app. (i) All chosen
fitting options as well as numerical and graphical results can be
compiled in reports in PDF and HTML format to promote data transparency
and good scientific practice. In this spirit, raw data and results can
be exported as a single data container in the form of an .RData file
to give other researchers access to data and analysis methods used.