This repository contains the source code for the companion web-app of the following paper:

• Jean Descarpentrie ^1,2*
• Florian Bernard ^3*
• Wilfried Souleyreau ^4*
• Lucie Brisson ⁴
• Thomas Mathivet ⁴
• Ioannis S. Pateras ⁵
• Océane C. B. Martin ⁶
• Teresa Frisan ^1,2**

¹ Department of Molecular Biology, Umeå University, 90187 Umeå, Sweden.
² Umeå Centre for Microbial Research (UCMR), Umeå University, 90187 Umeå, Sweden.
³ University of Bordeaux, INSERM, U1212, Nucleic Acids: Natural and Artificial Regulations Laboratory, 33000 Bordeaux, France.
⁴ University of Bordeaux, INSERM, U1312 BRIC, Tumor and Vascular Biology Laboratory, 33600 Pessac, France.
⁵ 2nd Department of Pathology, “Attikon” University Hospital, Medical School, National and Kapodistrian University of Athens, 124 62 Athens, Greece.
⁶ University of Bordeaux, CNRS, IBGC, UMR 5095, 33000 Bordeaux, France.

Lead contact: Jean Descarpentrie
Technical contact: Wilfried Souleyreau

*Equal contributions
**Correspondence: Teresa Frisan

AURA is a universal tool for automated RNAscope analysis for high-throughput applications.

It comes as a FIJI macro (AURA_macro_v1.1.ijm) that you can directly download from the folder named AURA present in this repository. This macro was developed by Jean Descarpentrie and Wilfried Souleyreau, for any questions contact them directly.

The web-app provides an out-of-the-box solution for processing the files obtained after running the AURA macro on your images, without the need to set up and run a python script on your local machine. Once results are downloaded, files uploaded to the app and generated by the script are automatically removed.

The web-app is written in python and uses the streamlit library. It is deployed via the streamlit community cloud and accessible at:
https://aura-data-processing.streamlit.app.

If desired, the web-app can be executing locally. To do so, clone/download this repository on your local machine, install the python libraries listed in requirements.txt and run the app by executing the following command in your terminal:

streamlit run AURA_main.py

For more details on how to execute a streamlit app locally, see Streamlit documentation at: https://docs.streamlit.io/.

Additionally, we provide a Command Line Interface (CLI) version of the web-app for processing your files locally. In your terminal, execute the following file: CLI_aura_data_processor.py.

The script was developed in python 3.12.2
The following python libraries needs to be installed in your local environment when running the script:

• openpyxl 3.1.3
• pandas 2.2.1
• pydantic 2.6.4
• XlsxWriter 3.2.0

Clone or download the entire repository on your local machine. Inside the repository folder, you can run the script as following:

python3 CLI_aura_data_processing.py [-h] -i [INPUT_FOLDER] -o [OUTPUT_FOLDER] -a [Area | Count]

Results will be saved in the OUTPUT_FOLDER you indicated. Arguments to pass to the script are the following:

python3 CLI_aura_data_processing.py -h

Main options:
  -i, --input       Input folder containing .xls files
  -a, --analysis    Perform analysis on 'Count' or 'Area' data column
  -o, --output      Output folder
  
optional arguments:
  -h, --help        Show this help message and exit
  -v, --verbose     Verbose output

The source code is licensed under the MIT License.