4. Step C — Profile granules (`profile`)

Step C1. Ensure the same mc instance still holds transcripts used for detection.

Step C2. Choose the gene list genes (full panel or a subset).

Step C3. Call:

granule_adata = mc.profile(
    granules,
    genes=genes,
    buffer=0.0,
    print_itr=False,
    print_itr_interval=5000,
)

Argument	Meaning
`granules`	`DataFrame` from `detect()` (or merged rough table).
`genes`	Genes to count; `None` uses all genes present in `transcripts`.
`buffer`	Added to each sphere’s radius when querying transcripts.

Output: anndata.AnnData — sparse X: n_granules × n_genes; obs: granule metadata with coordinates renamed to global_x / global_y / global_z and granule_id added.

Step C4. Typical Scanpy QC for visualization (as in 3_detection.py): copy counts to a layer, normalize_total, log1p, pca, tsne.

Next: Step D — Manual granule subtyping

4. Step C — Profile granules (profile)

4. Step C — Profile granules (`profile`)