Clarify sampling logic for small vs large control type groups

[Copilot]

Addressed review question about why the proposed code doesn't sample small groups (≤250 samples) when handling control type sampling in UMAP visualization.

Clarification Provided

The sampling logic correctly handles two cases:

• Large groups (>250 samples): Sampled down to 250
• Small groups (≤250 samples): Kept as-is without sampling

Small groups don't need sampling because they're already at or below the target size. Sampling them would be unnecessary computation with no benefit.

Context

The reviewer suggested refactoring to avoid pandas GroupBy.apply deprecation warnings:

# Sample up to 250 rows per control type
group_sizes = scdf["Metadata_control_type"].value_counts()
large_groups = group_sizes[group_sizes > 250].index
small_groups = group_sizes[group_sizes <= 250].index

sampled_large = (
    scdf[scdf["Metadata_control_type"].isin(large_groups)]
    .groupby("Metadata_control_type", group_keys=False)
    .sample(n=250, random_state=0)
)
small = scdf[scdf["Metadata_control_type"].isin(small_groups)]

scdf = pd.concat([sampled_large, small], axis=0)

This approach is correct and more efficient than the current apply(lambda grp: grp.sample(n=min(250, len(grp)))) implementation.

---

✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.