Changelog

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

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

[Unreleased]

Created CHANGELOG.md documenting project history, milestones, and design choices.
Documented the role of Moran's I (diagnostic target validation and spatial representation collapse detection) in PATHWAY_MAPPING.md and spatial_stats.py.

Refactored baseline models (HE2RNA, ViT_ST in regression.py) to accept num_pathways instead of num_genes and directly regress pathway activities.
Corrected console script entry points in pyproject.toml to map to recipes/hest/ instead of data/.
Updated setup.ps1 and setup.sh to suggest stf-compute-pathways instead of stf-build-vocab.
Cleaned up parameter descriptions and docstrings in dataset.py, trainer.py, and checkpoint.py.
Completely updated documentation files (DATALOADER.md, MODELS.md, SC_BEST_PRACTICES.md, TRAINING_GUIDE.md, TESTING.md, PRECOMPUTED_WORKFLOW.md, DATA_FORMAT.md) to reflect the pathway-exclusive paradigm and remove legacy gene-reconstruction references.

Integrated multi-loss framework containing Concordance Correlation Coefficient (CCC), Huber loss, and CLIP-style contrastive loss to improve target convergence and model robustness.
Added direct supervision head for pre-computed pathway targets, eliminating circular dependency issues from older auxiliary pathway loss architectures.
Created public inference API and model wrapping framework.
Introduced Moran's I diagnostics for Spatially Variable Gene (SVG) selection and spatial pattern evaluation.
Added licensing disclaimers and specific attribution details for MSigDB Hallmark gene sets (CC BY 4.0), HEST-1k dataset, and third-party foundation models (CTransPath, Phikon).

Resolved TypeError in transformer encoder by placing enable_nested_tensor=False in PyTorch's TransformerEncoder constructor.
Configured pytest warnings filter in pyproject.toml to suppress non-critical output noise (e.g. deprecations from third-party libraries).

Initialized core package architecture, modules, test suite, and scripts.
Implemented the quad-flow interaction system (early fusion of spatial transcriptomics and whole-slide histology features).
Added LocalPatchMixer module (Scatter-Gather depthwise 2D convolutions) to introduce localized spatial inductive biases into slide spot processing.
Added support for pre-computing histology feature extraction (e.g. using CTransPath) and building KD-Tree representations for spatial neighbor retrieval.
Developed an interactive Matplotlib visualization widget to overlay predicted pathway activities on histology slide coordinates.
Set up GitHub Actions CI workflow for automated testing.