Feedback on concatenate()

While I in the end was able to concatenate the data the way I like, the user experience wasn't as great as I had hoped, so wanted to drop some feedback. As I'm not that familiar with spatialdata yet, it might be that there are already better solutions -- please let me know if there are.

Starting situation

I have ~20 Visium Cytassist samples from a clinical trial processed with nf-core/spatialtranscriptomics (using the https://github.com/nf-core/spatialtranscriptomics/pull/67 branch that already uses spatialdata). The pipeline generates a single .zarr folder for each sample.

Desired outcome

I would like to have all samples in a single SpatialData object. The AnnData table should contain the gene expression from all samples.

Pain points

sd.concatenate enforces that the input is a list. Is there a reason this can't accept any Sequence type (e.g. dict_values)?
Usually, I pass a dictionary sample_id -> AnnData to anndata.concat, which nicely makes unique obs_names in combination with concat(..., index_unique="_"). This doesn't work with spatialdata.concatenate, which leaves me with either manipulating the obs_names for each object before concatenation, or ugly obs names with numeric sufficies (e.g. AACTCAACCTTGACCA-1_0_0_0_0_0_0_0_0_0_0_0_0_0_0_0). IMO it would be great to support a dict as input to spatialdata.concatenate, too.

The per-sample SpatialData objects all have the same names for images, shapes and coordinate systems. I currently rename them like this:

sdatas_vis = {}

for _, row in tqdm(samplesheet.iterrows(), total=samplesheet.shape[0]):
  sample = row["sample"]
  tmp_sd = sd.read_zarr(sample_path / sample / "data" / "sdata_processed.zarr")
  tmp_sd.tables["table"].obs = tmp_sd.tables["table"].obs.assign(**row)
  tmp_sd.tables["table"].obs["region"] = sample
  tmp_sd.tables["table"].uns["spatialdata_attrs"]["region"] = sample
  # rename images
  tmp_sd.images[f"{sample}_hires"] = tmp_sd.images["visium_hires_image"]
  tmp_sd.images[f"{sample}_lowres"] = tmp_sd.images["visium_lowres_image"]
  del tmp_sd.images["visium_hires_image"]
  del tmp_sd.images["visium_lowres_image"]
  # rename shapes
  tmp_sd.shapes[f"{sample}"] = tmp_sd.shapes["visium"]
  del tmp_sd.shapes["visium"]

  sdatas_vis[sample] = tmp_sd

which seems a bit cumbersome. I'm wondering if there's a better solution or what's the intended way of handling such cases. It could also be worth adding a process to the nf-core/spatialtranscriptomics pipeline that already does the concatenation step.

scverse / spatialdata