veronicalatapiat / indi_nets

Methods

/media/vero/SSD-Tesis&U/Methods

Individualized Coexpression Network Generation

This repository contains scripts for generating individualized coexpression networks based on gene expression data. The coexpression networks are constructed using two different methods: SSN (Specific Sample Network) and CSN (Consensus Coexpression Network).

Prerequisites

Before using the scripts, ensure that you have the following dependencies installed:

• Python (version 3.6.7)
• numpy (version 1.20.1)
• scipy (version 1.6.2)
• pandas (version 1.2.4)

Usage

Follow the steps below to generate individualized coexpression networks:

1. Prepare your gene expression data in a matrix format, where rows represent genes and columns represent samples (cells).

   • The data should be a numerical matrix, with headers for the samples (if available).
   • If you have non-numeric headers, ensure that your data includes a separate file with the headers as textdata.

2. Run the script "ssn.py" to generate the Specific Sample Network (SSN).

   • Open a terminal or command prompt and navigate to the directory containing the script.
   • Execute the following command:

     python ssn.py <input_data_file> <output_file>
     

     • Replace <input_data_file> with the path to your gene expression data file.
     • Replace <output_file> with the desired name for the SSN output file.

3. Run the script "csn.py" to generate the Consensus Specific Network (CSN).

   • Open a terminal or command prompt and navigate to the directory containing the script.
   • Execute the following command:

     python csn.py <input_data_file> <output_directory>
     

     • Replace <input_data_file> with the path to your gene expression data file.
     • Replace <output_directory> with the desired directory where CSN output files will be saved.

Input Format

The input gene expression data should be in a matrix format, where each row represents a gene and each column represents a sample (cell). The file should be in a comma-separated values (CSV) format, and the first row should contain headers for the samples (if available).

If your data includes non-numeric headers, please ensure that you provide a separate file named "textdata.txt" that contains the headers in the same order as the columns in the gene expression data file.

Output Format

The output files generated by the scripts will be in a matrix format, where each row and column represents a gene. The values in the matrix represent the coexpression strengths between genes. The output files can be further analyzed or visualized using various network analysis tools or software.

Examples

To help you get started, we have included example input files ("example_data.csv" and "example_textdata.txt") and sample commands to run the scripts in the "examples" directory. You can use these as a reference for running the scripts on your own data.

License

This project is licensed under the MIT License.