v0.2.0

New features

• Added fit_SCGLUE function to simplify model training
  • Incorporates weighted adversarial alignment by default, with increased robustness on datasets with highly-skewed cell type compositions
• Added support for batch effect correction, which can be activated by setting use_batch in configure_dataset
• Added a model diagnostics metric “integration consistency score”

Enhancements

Support for training directly on disk-backed AnnData objects, scaling to almost infinite number of cells

Bug fixes

Fixed a bug where the graph dataset was not shuffled across epochs

Experimental features

• A partially paired GLUE model for utilizing paired cells whenever available
• The CLUE model that won the NeurIPS 2020 competition in multimodal integration (modality matching task) is here!