Changelog

Changelog#

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.13.0#

Added#

  • SupervisedSampleMethod base class (tl/_base_sample_method.py) providing a shared scaffold for unsupervised and supervised sample-level methods.

  • MixMIL wrapper (tl/supervised/_mixmil.py) for the attention-based multi-instance mixed model by Engelmann et al. 2024 (https://arxiv.org/abs/2311.02455).

  • PULSAR wrapper (tl/supervised/_pulsar.py) for the zero-shot foundation model by Pang et al. 2025 (https://doi.org/10.1101/2025.11.24.685470).

  • Tests for all supervised methods in tests/test_supervised_methods.py, including fixtures with deterministic mock backends (no network access or GPU required), multi-label MixMIL tests, and PULSAR linear probe tests.

  • Base class for sample methods: (tl/_base_sample_method/BaseSampleMethod)

  • fit_linear_probe() method for sample-level methods

  • fine_tune() method for supervised sample-level methods with linear probing as a default

  • predict() method for supervised sample-level methods

  • States for sample-level methods with _check_adata_loaded() and _check_fitted()

  • Tests for supervised methods

Changed#

  • Both SupervisedSampleMethod and SampleRepresentationMethod now inherit basic functionality from BaseSampleMethod

0.12.0#

Added#

  • Foundational model interface with helical at pp/basic.py

  • Tests for helical embeddings

0.11.4#

Added#

  • Tests for all sample representation methods

  • Tests for preprocessing functions in pp/basic.py

  • Tests for evaluation utilities in tl/evaluation.py

  • conftest.py with reusable fixtures

Fixed#

  • prepare_data_for_phemd now handles dense matrices in addition to sparse ones

0.11.3#

Added#

  • An utils function _remove_negative_distances

Changed#

  • In Python implementations of GloScope, remove negative distances

0.11.2#

Added#

  • GloScope tutorial

Changed#

  • Update the rpy2 interface for R implementation of GloScope

0.11.1#

Fixed#

  • Fix bug in tl/sample_representation/GloScope_py with always accessing layer in obsm instead of a general slot

0.11.0#

Added#

  • Function tl/evaluation/trajectory_correlation to compute a corresponding SPARE metric

  • Function tl/evaluation/knn_prediction_score to compute a corresponding SPARE metric

  • Function tl/evaluation/replicate_robustness to compute a corresponding SPARE metric

  • Utils function tl/evaluation/_get_col_from_adata

  • Utils funciton tl/evaluation/_identity_up_to_suffix

0.10.0#

Added#

  • GloScope_py sample representation method (reimplementation of the original GloScope in Python for CPU and GPU)

Changed#

  • GloScope.calculate_distance_matrix now returns a NumPy array instead of a pandas DataFrame

0.9.3#

Changed#

  • Update rpy2 conversion in Gloscope.prepare_anndata()

0.9.2#

Changed#

  • Update readme with an overview and pypi link

0.9.1#

Changed#

  • Install PILOT and DiffusionEMD from PyPI, not GitHub

  • Fix actions and update documentation

0.9.0#

Changed#

  • GitHub actions files to match an updated scverse cookiecutter template

  • Breaking! Rename wherever possible: patient_representation -> patpy

  • Breaking! Rename tl.basic.py to tl.sample_representation

0.8.0#

Added#

  • persistence_evaluation method in patient_representation.tl.evaluation

  • Persistent homology file src/patient_representation/tl/persistence.py

0.7.2#

Changed#

  • Fix typo: patient_representations -> sample_representation in correlation functions

0.7.1#

Changed#

  • Fixed typo in GloScope causing empty distance matrix

0.7.0#

Added#

  • GloScope sample representation method (interface to R package via rpy2)

  • conda environment for gloscope

Changed#

  • GloScope R script now accepts n_workers argument

0.6.1#

Changed#

  • Use layers instead of obsm to store layer data in _move_layer_to_X method

0.6.0#

Changed#

  • Use cell_group_key instead of cell_type_key in MOFA and _get_pseudobulk

  • Use sample_representation instead of patient_representation in MOFA

0.5.0#

Deleted#

  • Remove mandatory filtering of cell types in and small samples in prepare_anndata method of SampleRepresentationMethod descendants

Changed#

  • Rerun example notebook with updated API

  • Add minor comments to the example notebook

0.4.0 – Synthetic data generation#

Added#

  • Functions to generate synthetic data simulating disease severity in src/datasets/synthetic.py

  • Synthetic data generation example notebook: docs/notebooks/synthetic_data_generation.ipynb

  • plot_embedding method for sample representations now accepts custom axes

0.3.0#

Sample representation refactoring:#

  • “cell type” is renamed to “cell group” everywhere to be more general

  • Some representation methods are renamed accordingly:

    • CellTypesComposition -> CellGroupComposition

    • CellTypePseudobulk -> GroupedPseudobulk

    • TotalPseudobulk -> Pseudobulk

  • patient_representation argument is renamed to sample_representation

  • “Patient representation” is now renamed to “Sample representation” eveywhere

  • The base class is now called SampleRepresentationMethod instead of PatientRepresentationMethod. This is important only for developers, users shouldn’t use it anyway

Deleted#

  • Not used SCellBow class

  • Example notebook in the documentation

0.2.0#

Added#

  • Warning about ongoing development in README

  • Function correlate_composition to the tools

  • Function correlate_cell_type_expression to the tools

  • Function correlation_volcano to the plotting

  • Patients trajectory example notebook

Changed#

  • Rename patient_representation to patpy