ema-tool

ema-tool is a Python library designed to facilitate the initial comparison of diverse embedding spaces in biomedical data. By incorporating user-defined metadata on the natural grouping of data points, ema-tool enables users to compare global statistics and understand the differences in clustering of natural groupings across different embedding spaces.

Overview

• Features
• Installation
• Colab Notebook Examples
• Links to Embedding Scripts
• License

Features

Given a set of samples and metadata, and at least two embedding spaces, the ema-tool provides visualisations to compare the following aspects of the embedding spaces:

• Unsupervised Clusters: ema-tool provides a simple interface to cluster samples in the embedding space using the KMeans algorithm and compare against user-defined metadata.
• Dimensionality Reduction: ema-tool allows users to reduce the dimensionality of the embedding space using PCA, t-SNE, or UMAP.
• Pairwise Distances: ema-tool computes pairwise distances between samples in the embedding space. Different distance metrics are available, including Euclidean, Cosine, and Mahalanobis.

The following figure provides an overview of the ema-tool workflow:

Installation

You can install the ema library through pip, or access examples locally by cloning the github repo.

Installing the ema library

pip install ema-emb

Cloning the ema repo

git clone https://github/pia-francesca/ema

cd ema                         # enter project directory
pip3 install .                 # install dependencies
jupyter lab colab_notebooks    # open notebook examples in jupyter for local exploration

Colab Notebook Example

An example of how to use the ema-tool library is provided in the following colab notebook:

Links to Embedding Scripts

To allow a flexible use, ema-tool does not include the scripts for generating the embeddings. However, here are some links to external scripts for generating protein embeddings from fasta files using the following models:

• ESM model
• ProtT5 model

Contact

If you have any questions or suggestions, please feel free to reach out to the authors: francesca.risom@hpi.de.

License

This project is licensed under the MIT License - see the LICENSE file for details.