MSIght

MSIght is a powerful and flexible tool for integrating multispectral imaging data with histological analysis and proteomics-based insights. It is designed to streamline your data workflows, from pre-processing and registration to clustering and visualization.

Installation

You can install MSIght from PyPI with:

pip install MSIght

If you prefer to install from source, clone the GitHub repository and run:

git clone https://github.com/lingjunli-research/MSIght.git
cd MSIght
pip install .

A Quick Example

Here’s a quick example showing how to load data, apply an analysis pipeline, and visualize the results:

from MSIght.refactor_segment import cluster_msi
from MSIght import refactor_common_functions

filename = "my_data.imzML"
output_directory = "results"
sample_name = "sample_001"
sigma = 1.0
structuring_element_size = 3
pca_components = 2
tsne_components = 2
tsne_perplexity = 30
tsne_learning_rate = 200
tsne_iterations = 500
k_means_cluster_number = 5

# Run the clustering pipeline
results = cluster_msi(filename, output_directory, sample_name, sigma, structuring_element_size,
                      pca_components, tsne_components, tsne_perplexity,
                      tsne_learning_rate, tsne_iterations, k_means_cluster_number)

# The returned 'results' can now be used for further analysis or visualization
# For example, you can use refactor_common_functions to process or plot your data

Documentation

For more detailed usage guides, tutorials, and API references, please see the following pages:

These pages provide comprehensive details on each module, as well as code examples, parameters, and return values for all public functions.

Contributing

We welcome contributions! If you want to report a bug, suggest a feature, or contribute code:

  1. Check the issue tracker for existing reports or requests.

  2. Submit a new issue if your topic isn’t covered.

  3. Fork the repository, make your changes, and open a pull request.

Your feedback and involvement help improve MSIght for everyone.