Usage

📓 Notebook workflow (exploratory)

1. Create and activate the environment (see Installation).

2. Open the notebook synco_plots.ipynb.

3. Prepare the CONFIG to read your data and options:

   • paths: base, pipeline_runs, input, output

   • general: cell_lines, run_date, verbose

   • compare: prediction_method (DrugLogics or BooLEVARD), threshold, synergy_column, analysis_mode (inhibitor_combination or cell_line)

4. Run cells to build and extract results, make ring plots and ROC or PR curves.

🖥️ CLI — run from terminal

You can run Synco either with a configuration file (JSON or YAML) or using a lightweight direct-arguments mode.

📄 Config-file mode (recommended for reproducibility)

# Preview execution plan
python -m synco -c examples/synco_example_config.json --plan

# Run analysis
python -m synco -c examples/synco_example_config.json

⚡ Direct-arguments mode (no config file)

python -m synco --base data/DrugLogics \
    --pipeline-runs data/sample_raw/20250804/drabme_out \
    --input data/synco_input \
    --output results \
    --cell-lines C2BBE1,CAR1,T84 \
    --prediction-method DrugLogics \
    --plan

🔀 Override synergies file

Specify the exact experimental synergies file to use with --synergies_filename (accepts a filename relative to the --input folder or an absolute path):

python -m synco -c examples/synco_example_config.json \
    --synergies_filename data/synco_input/labdata_nochemo_hsa.csv --plan

⚙️ Configuration options

Section	Key	Description
paths	base	Base directory for pipeline data
paths	pipeline_runs	Path to pipeline prediction outputs
paths	input	Path to experimental synergy data
paths	output	Output directory for results
general	cell_lines	List of cell lines to analyse
general	run_date	Date identifier for the run
general	verbose	Print detailed output
compare	prediction_method	DrugLogics or BooLEVARD
compare	threshold	Synergy classification threshold
compare	synergy_column	Column name for synergy scores
compare	analysis_mode	inhibitor_combination or cell_line