History

1.2.1 (2025-04-14)

Added functionality to generate umap of embeddings (in cellmaps_coembedding.utils)

Added functionality to evaluate embeddings using statistical analysis and visualization (functions get_embedding_eval_data and generate_embedding_evaluation_figures in cellmaps_coembedding.utils).
Update defauls (EPOCHS and DROPOUT)

Rename auto coembedding name and proteinGPS. –algorithm auto option is depreacted and –algorithm proteingps should be used. The coembedding implementation was moved to ProteinGPSCoEmbeddingGenerator class and AutoCoEmbeddingGenerator is deprecated and calls proteingps. The package name was renamed from autoembed_sc to proteingps.
Added mean_losses mean loses flag and argument in ProteinGPSCoEmbeddingGenerator. If set, uses mean of losses otherwise sum of losses.
Constants updated in ProteinGPSCoEmbeddingGenerator (triplet_margin=0.2) and in proteingps’s fit_predict (triplet_margin=0.2, lambda_reconstruction=5.0, lambda_triplet=5.0)
Bug fix: add missing a .to(device) call to ensure tensors are correctly moved to the appropriate device.
Update version bounds of required packages

Added --provenance flag to pass a path to json file with provenance information. This removes the necessity of input directory to be an RO-Crate.

Added a new coembedding algorithm accessible via flag --algorithm auto. This algorithm utilizes neural networks to generate latent embeddings, optimizing both reconstruction and triplet losses to improve embedding accuracy by learning intra- and inter-modality relationships.