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License: MIT

MORPHӔUS is Python-based software for morphology-aware classification of single cells and multicellular tissue structures in whole-slide multiplex images of tissue using the variational autoencoder deep learning network architecture.

Installation

Install MORPHӔUS into a dedicated Conda environment and activate it with the following commands:

conda create -n morphaeus -c conda-forge -c labsyspharm python=3 vae
conda activate morphaeus

If conda is not already installed, you can download it by following the instructions provided here.

Program Execution

Make a copy of the config.yml file in the vae folder of the installed MORPHӔUS Conda environment and modify all applicable paths and configuration settings. Run the program with the following command:

vae <path/to/config.yml>

The pipeline supports progress bookmarking, allowing the program to pick up where it left off between runs. Users may elect to re-run a particular module by adding the --module flag as follows:

vae --module <MODULE_NAME> <path/to/config.yml>

Available modules:

  • GENERATE_CELLCUTTER_INPUT
  • DETECT_ARTIFACTS (under development)
  • RUN_CELLCUTTER
  • GENERATE_IMAGE_GALLERY
  • REMOVE_BACKGROUND
  • TRAIN_VAE
  • ENCODE_IMAGES
  • SALIENCY_MAP

Note: In order to re-run a module, the corresponding checkpoint file must be removed from the checkpoints folder in the output directory.

MORPHӔUS Source Code

MORPHӔUS source code is freely-available for academic use and archived on Zenodo.

Funding Acknowledgments

This work was supported by Ludwig Cancer Research and the Ludwig Center at Harvard (P.K.S., S.S.), the Gray Foundation, and by NIH NCI grants U01-CA284207, and U2C-CA233262. S.S. is supported by the BWH President’s Scholars Award. Results shown in this study are in part based upon data generated by the Human Tumor Atlas Network (HTAN, https://humantumoratlas.org/).

References

Baker GJ., Novikov E. et al. Morphology-Aware Profiling of Highly Multiplexed Tissue Images using Variational Autoencoders. bioRxiv (2025) https://doi.org/10.1101/2025.06.23.661064