Release notes
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.models.integration_consistency to 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_labels uses 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
Minor improvements and bug fixes
Bug fixes:
Device detection is now more reliable (Resolves #17).
Enhancements:
Custom encoders and decoders can now be registered without changing package code (Resolves #14).
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_batchin 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 2021 competition in multimodal integration is here!
v0.1.1
First public release