Feedback on concatenate()

[grst]

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 nf-core/spatialvi#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.

[melonora]

I am a bit swamped at the moment, but I will look into implementing your suggestions. As you said it would be worthwhile to handle dicts.

[wangjiawen2013]

I have the same issue !
The per-sample SpatialData objects all have the same names for images, shapes and coordinate systems. So when I concatenate them, an keyerror occurred: KeyError: 'Images must have unique names across the SpatialData objects to concatenate'

[wangjiawen2013]

And it's better to have a way to retrieve (subset) each objects from the concatenated objects.

[LucaMarconato]

@wangjiawen2013 does SpatialData.subset() works for your use case or you would improve something?

[wangjiawen2013]

What I mean is how to concatenate multi spatialdata objects and subset each objects from the concatenated objects according to the sample names (each object have a unique name) again. The SpatialData objects from xenium all have the same names for images, shapes and coordinate systems, so I cannot concatenate them because KeyError: Images must have unique names across the SpatialData objects to concatenate.
SpatialData.subset() can only get elements, not objects.
We can concatenate and subset anndata objects well, what i mean is to concatenate and subset spatialdata objects like anndata objects.