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prop-score-pairing
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# Propensity Score Alignment of Unpaired Multimodal Data This repo contains code to reproduce the results of our [recent paper](https://arxiv.org/abs/2404.01595) on aligning unpaired multimodal data by leveraging the propensity score. If you would like to use this repo on your own data, you will simply need to train a classifier on each modality (see [main.py](https://github.com/valence-labs/prop-score-pairing/blob/main/main.py) for example code), and then you can run the [inference script](https://github.com/valence-labs/prop-score-pairing/blob/main/inference.py) on the trained classifiers. Cross-modality prediction can be achieved with the [probing](https://github.com/valence-labs/prop-score-pairing/blob/main/probing.py) code. ### Data Preparation The two relevant datasets can be loaded by running ``` bash data.sh ``` which will download the CITE-seq data from GEO and process it, as well as generate the synthetic image data. If the data files already exist, the script will skip data generation. For a hard reset, you can run the script with the optional flag `-c`, which will first empty the dataset directory. ### Training All the code assumes you have cuda available. An example training script: ``` python main.py --dataset=GEXADT --max_epochs=250 --batch_size=256 --eval_interval=1 --run_scot ``` The command takes the following possible parameters: - `--max_epochs` - `--batch_size` - `--lr` - `--dataset`: One of "GEXADT", for CITE-seq data, or "BALLS", for the synthetic interventional balls data. - `--model`: One of "CLASSIFIER" or "VAE". - `--eval_interval`: How often to evaluate the model by computing matching metrics on the full validation set. Default is set to evaluate at every epoch (`= 1`). If the matching metrics are too slow, you may want to make this larger. - `--run_scot`: Boolean flag for whether to run Gromov-Wasserstein OT (SCOT) and store its metrics prior to training. This can be slow for larger datasets! By default, `main.py` will train a classifier on the CITE-seq data for 2 epochs, with batch size 256, and with learning rate 0.0001, which should be used for testing purposes. There are also parameters related to the VAE loss from: Yang, Karren Dai, et al. "Multi-domain translation between single-cell imaging and sequencing data using autoencoders." Nature communications 12.1 (2021): 31. - `--lamb` - `--alpha` - `--beta` ### Inference The training script will store a checkpoint with the best validation loss at `results/checkpoints/*.ckpt`. With this checkpoint, we can evaluate final matching metrics on the test set with the following example script: ``` python3 inference.py --checkpoint=results/checkpoints/CLASSIFIER-epoch=00-full_val_loss=0.81.ckpt ``` The result will be stored under `results/`, in a CSV file. The command takes the following parameters: - `--checkpoint` - `--dataset` - `--run_scot` Besides `--checkpoint`, which is self-explanatory, the remaining parameters are identical to `main.py`. Again, by default it will assume that we are doing inference on the CITE-seq data. ### Probing Another downstream task is to probe whether the matched samples resulting from a trained classifier/VAE are able to be used for cross-modality prediction. The following script takes a checkpoint and trains a 2-layer MLP (which we call a probe) minimizing MSE for prediction. __Note__: this is currently only supported for the CITE-seq data, as image translation would require a much more sophisticated architecture. ``` python3 probing.py --checkpoint=results/checkpoints/PS-epoch=06-full_val_loss=0.63.ckpt ``` This command takes the following parameters, note the training parameters are for training the MLP probe. - `--checkpoint`: This can be either a checkpoint to a VAE or classifier, or the string "gt", for ground truth matching, or "random", for a within-group random permutation. - `--max_epochs` - `--batch_size` - `--lr` - `--unbiased`: Boolean flag for whether to use the unbiased 2-stage least squares loss (this involves sampling twice from the source modality). By default, `probing.py` will run for 2 epochs with batch size 500, and learning rate 0.0001, without the unbiased loss. The default should be used for testing purposes.
