qRATqPCR relative expression analysis tool
qRAT is a R based standalone desktop application designed to provide a comprehensive, simple and easy to use tool for the relative quantification of real-time quantitative PCR data analysis. Functions are provided for users who have little statistical and no R programming background.

Web application
qRAT is available as web application on ShinyApps.io:
Latest Release: 0.7.0
Local Installation (using R)
Run the app using R console and RStudio. Please follow the instructions on GitHub.
Download
qRAT standalone is currently not available as easy-install for Windows.
Citation
If you use qRAT for any published research, please include the following citation:
Flatschacher, D., Speckbacher, V. & Zeilinger, S. qRAT: an R-based stand-alone application for relative expression analysis of RT-qPCR data. BMC Bioinformatics 23, 286 (2022). https://doi.org/10.1186/s12859-022-04823-7
What's qRAT?
qRAT is a R based standalone desktop application to automate the processing of raw Quantification Cycle (Cq) data files exported from virtually any qPCR instrument using well established and state-of-the-art statistical and graphical techniques. The purpose of this tool is to provide a comprehensive, straightforward, and easy-to-use solution for the relative quantification of RT-qPCR data that requires no programming knowledge or additional software installation.
The current implementation allows ΔCq calculation (relative to endogenous control(s)), ΔΔCq calculation (relative to endogenous control(s) and a reference sample), inter-plate variation correction, and automated reference gene stability analysis. Moreover, functionalities for parsing, filtering and visualisation of relative RT-qPCR data are included.
Getting Help
The User Guide gives an extensive documentation on data input, data analysis and graphical representation of the application. You can download the qRAT User Guide (PDF) or access it directly from within the qRAT application.
Example data: download the complete example dataset (ZIP). The single files and a short description are in the example_data folder on GitHub.
The documentation, alongside the app, is under constant development. If you see any mistakes or typos, please let us know.
