cajal 2.0.1

A library for multi-modal cell morphology analyses using Gromov-Wasserstein (GW) distance.

CAJAL

CAJAL is a Python library for multi-modal cell morphology analyses using Gromov-Wasserstein (GW) distance. Detailed information about the methods implemented in CAJAL can be found in:

K. W. Govek, P. Nicodemus, Y. Lin, J. Crawford, A. B. Saturnino, H. Cui, K. Zoga, M. P. Hart, P. G. Camara, CAJAL enables analysis and integration of single-cell morphological data using metric geometry. Nature Communications 14 (2023) 3672. DOI:10.1038/s41467-023-39424-2

R. Hu, N. N. Naseri, O. Shalem, P. G. Camara, Morphology-robust quantification of subcellular organization in complex cells. bioRxiv [Preprint] (2026) DOI:10.64898/2026.05.28.728543

Installation

CAJAL is hosted on the Python Package Index - https://pypi.org/project/cajal/

It is recommended to install CAJAL via pip, which should automatically retrieve the correct wheel for your platform and Python version. It is strongly recommended to create a virtual environment.

pip install cajal

Depending on your use case, you can install additional dependencies:

pip install "cajal[notebooks]" # dependencies for tutorial notebooks
pip install "cajal[vis]"       # visualization helpers such as ipywidgets and plotly
pip install "cajal[dl]"        # deep-learning dependencies such as torch

Installation on a standard desktop computer should take a few minutes.

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CAJAL can be also built from source, by cloning the Github repository.

pip install git+https://github.com/CamaraLab/CAJAL.git

To build CAJAL from source, a C++ compiler is required for the Gromov-Wasserstein computation and may be required for the potpourri3d library if the precompiled binaries are not compatible with your system. On Windows, we recommend Microsoft Visual C++ 14.0 or greater, which can be installed via the Microsoft C++ Build Tools. On Ubuntu, it requires g++ and may require the package python3.x-dev, which registers the Python header files with g++. The Unbalanced Gromov-Wasserstein module requires a Gnu C compiler, such as is available through MinGW, and a library implementing pthreads on windows.

CAJAL contains numerous dependencies which are currently hosted only on PyPI; as such, it is not possible at this time to provide a CAJAL conda package. (conda packages require all their dependencies to also be conda packages.) However, it should be possible to install CAJAL in a conda is conscious of, using a conda-managed Python installation and calling pip from within a conda environment.

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The easiest way to run CAJAL is via Jupyter. Install Jupyter with

pip install notebook

Then start up Jupyter from terminal / Powershell using

jupyter notebook

Docker image

We provide a Docker image which contains CAJAL and its dependencies, cajal:latest is built on top of the Docker image jupyter/base-notebook and contains numerous data science tools for further analysis of the output of CAJAL. Running the following command will launch a Jupyter notebook server on localhost with CAJAL and its dependencies installed:

docker run -it -p 8888:8888 -v C:\Users\myusername\Documents\myfolder:/home/jovyan/work camaralab/cajal:latest

The -p flag controls the port number on local host. For example, writing -p 4264:8888 will let you access the Jupyter server from 127.0.0.1:4264. The -v "bind mount" flag allows one to mount a local directory on the host machine to a folder inside the container so that you can read and write files on the host machine from within the Docker image. Here one must mount the folder on the host machine as /home/jovyan/work or /home/jovyan/some_other_folder as the primary user "jovyan" in the Docker image only has access to that directory and to the /opt/conda folder. See the Jupyter docker image documentation for more information.

Documentation

Extensive documentation, including several tutorials, can be found in CAJAL's readthedocs.io website.

New in this release

Version 2.0 of CAJAL incorporates CellAligner, an unsupervised framework that uses fused unbalanced Gromov-Wasserstein couplings to map protein distributions from morphologically distinct cells into shared anchor-cell geometries, enabling morphology-robust comparison of subcellular localization. The new functionalities are discussed in the new worked examples tutorial 6 and tutorial 7 and here.