https://github.com/lolab-msm/sclc-tf-network-analysis
This repository provides source MATLAB codes for the structural analysis of Small Cell Lung Cancer Transcription Factor network using Dense Spanning Trees (DST) and Minimum Dense Spanning Trees (MDST)
https://github.com/lolab-msm/sclc-tf-network-analysis
Last synced: 11 months ago
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This repository provides source MATLAB codes for the structural analysis of Small Cell Lung Cancer Transcription Factor network using Dense Spanning Trees (DST) and Minimum Dense Spanning Trees (MDST)
- Host: GitHub
- URL: https://github.com/lolab-msm/sclc-tf-network-analysis
- Owner: LoLab-MSM
- License: mit
- Created: 2023-09-15T23:32:12.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-10-31T21:29:30.000Z (over 2 years ago)
- Last Synced: 2025-02-23T04:45:54.027Z (over 1 year ago)
- Language: MATLAB
- Homepage:
- Size: 88 MB
- Stars: 1
- Watchers: 6
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Structural Analysis of Small Cell Lung Cancer Transcription Factor Network
This repository provides source MATLAB codes to reproduce our results of the structural analysis of the Small Cell Lung Cancer transcription factor network using Dense Spanning Trees (DST) and Minimum Dense Spanning Trees (MDST). For the details, please see our [publication](https://www.nature.com/articles/s41540-023-00316-2).
Here we utilized our previously published Dense(or Sparse) Spanning Tree methods whose source codes and detailed explanations are available [here](https://github.com/mustafaozen/Dense-Sparse-Spanning-Trees) and [here](http://www.iapress.org/index.php/soic/article/view/855).
Note: to reproduce the NON-NE to NE state transition simulation results in the paper, the source transition trajectory files are needed which could not be uploaded here due to large file sizes. However, they will be shared separately upon request. Please email to: mozen@altoslabs.com or clopez@altoslabs.com. Nevertheless, using the provided source codes, one can run new transition simulations and perform all the analyses. In this case, the results may slightly change due to the random nature of the asynchronous simulations, but the trend will be the same.