This resource provides the Python code to reproduce all the figures and key results described in Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer

The analysis done can be briefly described as:

• Discrete Modelling: Asynchonous Update on Ising model of Wild-Type Small Cell Lung Cancer(WT-SCLC) network. This is done by the set of codes given here.
• Continuous Modelling: RACIPE is used to generate an ensemble of continuous models of WT-SCLC. For this we have used RACIPE-1.0 package which can be found here.
• Experimental Data Analysis: Using SCLC cell line data like CCLE and GSM73160, verification of Theoretical results predicted by Ising Model and RACIPE is done and patterns seen in datasets are explained using the models used. This is done by the set of codes given here.

All the figures presented in the paper including Supplementary Figures (apart from the Schematics and Network Representation) are provided in the Figures folder. Details of reproducing the figures are briefly given in the in the README file in each folder containing the subfigures.

Boolean Simulation data generated using Fast-Bool and Edge Perturbation data generated using Edge_Perturbation are provided in the Simulation_Data folder. Since RACIPE simulation data files are quite huge, they are uploaded to this drive link.

This folder contains all the codes required for data analysis and simulation of WT-SCLC network. These are the basic framework of codes which some scripts used for figure production relies on.

1. Clone the GitHub Repository

git clone https://github.com/csbBSSE/CSB-SCLC

2. Set the working directory to CSB-SCLC

3. Install all the Required Python Packages (Conda is preferable)

while read requirement; do conda install --yes $requirement || pip install $requirement; done < requirements.txt

4. Go to the folder corresponding to the figure that needs to be reproduced and follow the instructions given there

5. Voila! you are done

• Some of the codes have an option of running processes in Parallel. Just make sure that you don't give spawn more processes than your CPUs can handle.
• Installing Python packages using Conda would be preferable. Using Intel Python Distribution gives significant speed boosts in some of the codes.
• Codes like UMAP_analysis and Bool.py may take longer times. Just be patient and don't Ctrl+C it even if you have to wait for some time (Just Don't do it. Time is precious)

Python(Tested on Version 3.8.5)