CellScanner v2.0

CellScanner is a tool that counts different microbial species in flow cytometry data of communities separately with the help of a classifier that is trained on mono-cultures. It is implemented in Python and compatible with Windows, Mac and Linux.

How to use it

Graphical User Interface (GUI)

Linux and macOS

To run CellScanner you first need to get it along with its corresponding dependencies. To this end, you may run the following chunk to get CellScanner and create a conda environment for it:

git clone https://github.com/msysbio/CellScanner.git
cd CellScanner
conda create -n cellscanner python=3.12.2 
conda activate cellscanner
pip install -r requirements.txt

To run CellScanner with its GUI, you may now run:

# Always remember to activate your conda environment, if you set one for CellScanner
conda activate cellscanner
# If `python` returns an error message that is not there, try with `python3` instead
python Cellscanner.py

This will pop-up CellScanner where you can now import your data, fill in your training parameters and predict your species.

Keep in mind that the CellScanner GUI is a PyQt5 app, meaning it requires from the host to have a working X11 windowing system.

Windows

In Windows, you may also follow the steps described above for the Linux and macOS systems. If you try through a WSL however, as already mentioned an X11 is required, which you would have to set up on your own.

Alternatively, you can build an .exe on your own Attention! Do not use a WSL. Also, that pyinstaller is available.

Then, after you make sure you have activated the cellscanner conda environment, you may run:

pyinstaller --onefile --icon=logo.ico --add-data "logo.png:." Cellscanner.py

.. or

you can simply download the .exe of CellScanner v2.0 from here.

Command Line Interface (CLI)

Assuming you already have CellScanner locally in a conda environment (see above), to run CellScanner using its CLI, you have first to fill in the config.yml file. In this file, you may provide all the arguments you would do in the GUI case as well.

Required arguments are mentioned as such, while it is important to remember that when providing monoculture data for the training step, their names (species_names in the yaml file) need to be in the same order as their filenames.

species_files:
  directories:
    - directory:
        path: ./Testfiles/
        filenames:
          - BH_mono_48h_100.fcs
          - FP_mono_48h_100.fcs
          - RI_mono_48h_100.fcs
          - BT_mono_48h_100.fcs
        species_names:
          - Bacteroides
          - Faecalibacterium
          - Roseburia
          - Bacteroides

Once your configuration file is ready, you may run CellScanner CLI :

conda activate cellscanner
python CellscannerCLI.py --config config.yml

When using the CLI version, CellScanner is independent of PyQt5, thus no X11 issues should be occurred.

The logic

CellScanner v2.0 is based on the first version of the tool.

For an overview of what is under the hood, you may have a look on the User manual of CellScanner v1.0. For the new features that have been added, a manuscript is in process. 📝