"""
Reusable analysis workflow modules for the DecoupleRpy agent.

Architecture
------------
Workflows implement dataset-agnostic analysis steps that compose cleanly
for any dataset whose manifest declares them applicable.

Separation of concerns
~~~~~~~~~~~~~~~~~~~~~~~
- Dataset-specific knowledge (GEO URL, condition column, expected sample
  counts, contrast definition) lives in src/datasets/manifests/*.yaml.
- Reusable logic (how to validate a condition column, how to extract
  collapse parameters, how to rank activity scores) lives here.
- MCP tool wiring lives in src/tools/dataset_tools.py.

This means adding a new PDAC dataset requires only a new YAML manifest.
No workflow module needs editing unless the dataset introduces a genuinely
new analysis type (e.g. ATAC-seq, spatial) not covered by existing modules.

Modules
-------
metadata_validation   Validate AnnData.obs against a dataset manifest.
microarray            Microarray-specific preprocessing helpers (probe collapse).
activity_scoring      TF and pathway activity scoring via decoupleR methods.
activity_stats        Statistical analysis of activity scores (DE, ranking).
survival              Survival analysis utilities (placeholder).

Usage from MCP tools
--------------------
    from src.workflows.microarray import get_collapse_params, recommend_analysis_path
    from src.workflows.activity_stats import get_contrast_groups

These functions read from parsed manifest dicts and return plain Python
values — they have no side effects and require no external dependencies
beyond the standard library.
"""