This repository contains scripts and datasets used in combinatorial drug screening analyses. It includes tools for adjusting dose responses, calculating synergy scores, and visualizing interaction effects between drugs.

• Scripts: Includes R and Python scripts for analysis and data restructuring.
• Data: Example datasets used in analyses.
• Results: Visualizations such as dose-response curves and synergy scores.

To use the scripts in this repository, you will need Python and R. Instructions for installing these can be found at:

• Python: https://www.python.org/downloads/
• R: https://cran.r-project.org/

Choose one to follow based on data restructuring needs. Note: limit of 12 blocks in Matrix structure.

• Example Table Data Structure (Windows)
• Example Data Matrix Structure (Windows)
• Example multi-drug Data Structure (Windows)

• Example Table Data Structure (MacOS)
• Example Data Matrix Structure (MacOS)
• Example multi-drug Data Structure (MacOS)

When using this script [https://github.com/eipm/Combinatorial-Drug-Screening-Data-Analysis/blob/main/AZ-Synergyfinder.r], you may need to adjust certain parameters based on your preferences. Here are some considerations:

• Adjusting Plot Titles: Modify the plot titles to reflect the specific context of your experiment. You can customize titles to include additional information such as experimental conditions, treatment doses, or any other relevant details.

• Data Imputation: If your dataset contains missing values, consider adjusting the data imputation method (impute_method) or disabling imputation altogether (impute = FALSE). Choose an imputation strategy that aligns with your data characteristics and analysis goals.

• Noise Handling: Depending on the noise level in your data, you may choose to enable or disable noise handling (noise). Adjust the noise parameter based on the variability observed in your experimental measurements.

• Method Selection: Experiment with different synergy calculation methods (method) to assess their suitability for your dataset. Each method may capture different aspects of synergy, so it's beneficial to explore multiple approaches and compare their results.

• Parameter Adjustment: Fine-tune parameters such as Emin and Emax to customize the synergy calculation process according to the expected range of interaction effects in your experimental system.

• Plot Formatting: Customize plot aesthetics, including colors, themes, and axis labels, to enhance readability and presentation quality. You can modify plot elements using ggplot2 functions to align with your visual preferences.

• Output Directory: Update the output_dir variable to specify the directory where generated plots and data files will be saved. Organize your output directory structure according to your project organization preferences.

• Output Formats: Adjust the output format (output_format) to match your requirements. Supported formats include PDF, PNG, JPEG, SVG, etc. Choose the format that best suits your downstream analysis or presentation needs.

By considering these adjustments based on your preferences, you can tailor the script to effectively analyze variable screens involving both drugs in your combinatorial drug screening experiments.

Testing your data restructuring and analysis process ensures that your data is correctly prepared for combinatorial drug screening analysis. Here’s how you can use the Restructure-Raw2Synergy.py script for data restructuring and then analyze the data either using the AZ-Synergyfinder.r script or via the more user-friendly SynergyFinder+ website:

1. Download Example Data:

   • Download the example raw data file for restructuring from this link.

2. Run Data Restructuring Script:

   • Execute the Restructure-Raw2Synergy.py script to transform the raw data into the format required by SynergyFinder. Adjust the script parameters such as file_path and output_path as needed.

3. Verify the Restructured Data:

   • Ensure that the output data is correctly formatted according to the SynergyFinder's specifications. This verification step is crucial before proceeding to the analysis phase.

4. Analysis Options:

   • Using AZ-Synergyfinder.r Script:
     • For users comfortable with coding, run the AZ-Synergyfinder.r script to analyze the restructured data. Adjust analysis parameters such as synergy calculation methods and plotting preferences as described in the usage instructions.
   • Using SynergyFinder+ Website:
     • For a more user-friendly approach with less coding, visit the SynergyFinder+ website at SynergyFinder+ Dashboard.
     • Follow the instructions on the website to upload your data and utilize the online tools to calculate synergy scores and visualize interaction effects.

5. Choose Your Analysis Tool:

   • Decide which analysis tool to use based on your comfort with coding and specific needs of your project. The script provides detailed control over the analysis parameters, while the website offers a straightforward, interactive way to analyze data.

By following these steps, you can effectively prepare and analyze your combinatorial drug screening data, utilizing the tools that best suit your expertise and research goals.

For synergy calculations and graphical analysis, we utilize SynergyFinder. More details and documentation can be found on their Bioconductor page:

• SynergyFinder Bioconductor

• Omar Alawa - OA - https://github.com/YourGitHub