Releases: gao-lab/GLUE
Releases · gao-lab/GLUE
v0.3.2
Bug fixes:
- Fixed "real_cross" loss in PairedSCGLUETrainer and SCCLUETrainer.
v0.3.1
Bug fixes:
- Fixed NaN loss in PairedSCGLUETrainer.
- Restored rna_anchored_prior_graph as a deprecated function (to be replaced by rna_anchored_guidance_graph).
v0.3.0
New features:
- New tutorial and functions for regulatory inference (Resolves #15, #41).
- New tutorial for training on partially paired data (Resolves #24).
Enhancements:
- Modularized
scglue.dx.integration_consistencyto allow for non-raw-count input (Resolves #30). - Added documentation translation in Chinese.
v0.2.3
Minor improvements and bug fixes
Bug fixes:
- Data frame in
obsmno longer triggers an error during model training (Resolves #32).
Enhancements:
scglue.data.transfer_labelsuses a new strategy with SNN-based estimation of transfer confidence (Resolves #23).- Allow setting custom bedtools path via
scglue.config.BEDTOOLS_PATH(Resolves #22).
v0.2.2
v0.2.1
v0.2.0
New features
- Added
fit_SCGLUEfunction 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_batchinconfigure_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!