Release notes¶
v0.2.1¶
Minor improvements and dependency fixes
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_datasetAdded 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 is here!
v0.1.1¶
First public release