Release Notes#

Version 0.4.0: May 05, 2025#

General:

A new training tutorial has been added which describes the new training workflow. This includes data preparation, training, and post-training data structures using the new scripts.

Training:

A new training workflow has been added to use CellArr, a TileDB based framework, as the data store to streamline the end-to-end process. This replaces the old Zarr based workflows.
New data loaders and samplers for CellArr data have been added in the tiledb_data_models module.
A example training script has been added to show how to train models as scripts/train.py.
New scripts for creating all post-training data structures have been added in the folder scripts.
New utility methods that make use of the CellArr store: utils.query_tiledb_df to query a tiledb dataframe, utils.adata_from_tiledb to extract cells from the tiledb stores based on index, including raw counts.

General:

Exhaustive queries:

Functionality to perform exhaustive queries has been added as new methods cell_query.search_exhaustive, cell_query.search_centroid_exhaustive, and cell_query.search_cluster_centroids_exhaustive.
The kNN query method cell_query.search has been renamed to cell_query.search_nearest.

Query result filtering and interpretation:

The cell_query.compile_sample_metadata method has been expanded to allow grouping by tissue and disease (in addition to study and sample).
The methods utils.subset_by_unique_values, utils.subset_by_frequency, and utils.categorize_and_sort_by_score have been added to provide tools for filtering, sorting and summarizing query results.
The “query_stability” quality control metric has been renamed to “query_coherence” and is now deterministic (by setting a random seed).
Results from exhaustive queries can be constrained to specific metadata criteria (e.g., tissue, disease, in vitro vs in vivo, etc). using the metadata_filter argument to cell_query.search_exhaustive.
Results from exhaustive queries can be constrained by distance-to-query using the max_dist argument to cell_query.search_exhaustive.
The mappings in utils.clean_tissues and utils.clean_diseases have been expanded.

Optimizations to training:

The ASW and NMSE training evaluation metrics were added.
The triplet_selector.get_asw method was added to calculate ASW.
The ontologies.find_most_viable_parent method was added to help coarse grain cell type ontology labels.
Optimized sampling weights of study and cell type.

Updated version requirements for multiple dependencies and removed the pegasuspy dependency.
Expanded API documentation and tutorials.
Simplified model file structure and model loader methods.
Added search_centroid method to class CellQuery for cell queries using custom centroids which provides quality control statistics to assess the consistency of query results.