scatrans.recommend_workflow

scatrans.recommend_workflow(adata_input, groupby, target_group, reference_group, sample_col=None)

High-level recommendation for analysis path based on experimental design.

This is a thin, user-friendly wrapper around diagnose_design that returns actionable preset + backend suggestions.

Returns keys:

• workflow_preset: key into WORKFLOW_PRESETS (e.g. "pseudobulk_report")

• preset_config: full preset dict (label, active_score_kwargs, filter_preset)

• recommended_preset: filter_active_genes preset name

• de_backend: "scanpy" | "pydeseq2" | …

• suggested_kwargs: merged kwargs for active_score()

• warnings, recommendations, power_summary

• full_diagnosis: raw dict from diagnose_design()

Example

rec = scat.recommend_workflow(adata, “condition”, “GA”, “Ctrl”, sample_col=”sample”) adata, sig, res = scat.active_score(

adata, groupby=”condition”, target_group=”GA”, reference_group=”Ctrl”, **rec[“suggested_kwargs”],

) candidates = scat.filter_active_genes(res, preset=rec[“filter_preset”])

Parameters:

• adata_input (Any)

• groupby (str)

• target_group (str)

• reference_group (str)

• sample_col (str | None)

Return type:

dict[str, Any]