IMPA

IMage Perturbation Autoencoder (IMPA) is a computer vision model performing style transfer on images of cells undergoing perturbation. IMPA learns a perturbation space and uses it to perform style transfer by conditioning the latent space of an autoencoder. Through this process, the model is used to translate a cell image into what it would look like had it been treated with a certain perturbation.

The perturbation space can be expressed as:

• A prior on the perturbation space (e.g. drug embeddings reflecting compound-specific physiochemical characteristics)
• Trainable embeddings optimized alongside the model

If trained on a meaniningful prior perturbation space, IMPA learns to map unseen drugs in proximity of drugs used for training. When proximity also involves functional similarity, IMPA is able to predict the effect of unseen drugs on control cells. Moreover, distances between the style encodings of different perturbations are correlated with distances in the phenotypic space. As a result, IMPA can be used to fastly inspect active compounds based on the comparison of the style vectors learned for different perturbations.

Install repository

To run the model, clone this repository and create the environment via:

conda env create environment.yml

Navigate to the repository and install the Python package.

pip install -e .

Codebase description

All files related to the model are stored in the IMPA folder.

• utils.py: contains helper functions
• solver.py: contains the Solver class implementing the model setup, data loading and training loop.
• model.py: implements the neural network modules and initialization function.
• main.py: calls the Solver class and implements training supported by seml and sacred.
• checkpoint.py: implements the util class for handling saving and loading checkpoints.
• eval/eval.py: contains the evaluation script used during training by the Solver class.
• data/data_loader.py: implements torch dataset and data loader wrappers around the image data.

Train the models

We trained the models using the seml framework. Configurations can be found in the training_config folder. IMPA can be trained both with and without the support of seml. This is possible via two independent main files:

• main.py: train with seml on the slurm scheduling system
• main_not_seml.py: train without seml on the slurm scheduling system via sbatch files

Scripts to run the code without seml can be found in the scripts folder. In a terminal, enter:

sbatch training_config.yaml 

And the script will be submitted automatically. The logs of the run will be saved in the training_config/logs folder.

For other scheduling systems the user may be required to apply minor modifications to the main.py file to accept custom configuration files. For training with seml we redirect the user to the official page of the package.

To train the model with the provided yaml files, adapt the .yaml files to the experimental setup (i.e. add path strings referencing the used directories).

Dataset and checkpoints

Datasets are available at:

• BBBC021 https://bbbc.broadinstitute.org/BBBC021
• BBBC025 https://bbbc.broadinstitute.org/BBBC025
• RxRx1 https://www.kaggle.com/c/recursion-cellular-image-classification/overview/resources

Model checkpoints and pre-processed data are made available here.