Constraint-based modeling identifies cell-state specific metabolic vulnerabilities during the epithelial to mesenchymal transition

Summary

This repository contains the code from the paper Constraint-based modeling identifies cell-state specific metabolic vulnerabilities during the epithelial to mesenchymal transition by Campit, S.E., Keshamouni, V.G., and Chandrasekaran, S.

Key analyses contained in notebooks:

1. Data preprocessing for transcriptomics, proteomics, single-cell transcriptomics, CERES Score data, and other =omics datasets.
2. Constraint-based metabolic reconstruction and analysis code for simulating metabolic fluxes and growth resulting from gene and reaction knockout.
3. Statistical analyses for assessing differences between groups.

Programming languages used in this analysis

• MATLAB version R2020b Update 4
• R version 4.03
• Python version 3.8.6

Usage

Three programming languages (Python / R / MATLAB) were used, based on availability of scientific libraries and strengths in specific tasks. Thus, we would recommend the following workflow to perform the entire analysis end-to-end. We will point to specific directories and scripts that are numbered by usage.

1. Exploratory data analysis and general understanding of data distributions: notebooks/r/01_EDA/*.Rmd
2. Preprocessing bulk -omics data for COBRA: notebooks/r/02_DifferentialExpression/*.Rmd
3. Preprocessing single-cell omics data for COBRA: notebooks/r/03_Preprocess/*.Rmd
4. Performing MAGIC data imputation for single-cell COBRA analysis: notebooks/python/magic.ipynb
5. Constraint-based reconstruction and analysis for bulk -omics data: notebooks/matlab/01_bulk_analysis/RECON1/*.mlx
6. Constraint-based reconstruction and analysis for single-cell -omics data: notebooks/matlab/02_single_cell_analysis/recon1_scCOBRA.mlx
7. Generating FBA-UMAP profiles: notebooks/r/05_Embeddings/*.Rmd
8. Statistical analyses: Google Colab notebooks can be found here.

Note that there are additional QA/QC scripts and notebooks available as well.

Contributing

Contributions to make this analysis better, more robust, and easier to follow are greatly appreciated. Here are the steps we ask of you:

1. Fork the project
2. Create a new branch
3. Make your changes
4. Commit your changes
5. Push to the branch
6. Open a pull request

License

Released via GPL GNU License . See LICENSE for more information.

© 2022 The Regents of the University of Michigan

Contact

Chandrasekaran Research Group - https://systemsbiologylab.org/

Contact: csriram@umich.edu