Merging multiple samples into a single SpatialData object + single merged table

[Rafael-Silva-Oliveira]

Hello!

I am trying to merge several samples into a single spatialdata object, and currently this is what I'm doing:

adatas = {
    "square_002um": [],
    "square_008um": [],
    "square_016um": [],
}
images = {}

for sample in sample_folders:
    sdata = spatialdata_io.visium_hd(
        f"{outs}/{sample}/outs",
        dataset_id=sample,
        fullres_image_file=id_to_image[sample],
    )
    # Collect tables
    for size, table in sdata.tables.items():
        table.var_names_make_unique()
        table.obs[f"sample_id"] = sample
        #Add mapping from dictionary above:
        table.obs["sfh_id"] = sample_mapping[sample]
        adatas.setdefault(size, []).append(table)

    # Collect images
    for name, img in sdata.images.items():
        images[f"{sample}_{name}"] = img

# Build one SpatialData object with merged table + all images
merged_bin_sizes = {}
for size, adata in adatas.items():
    temp_merged = ad.concat(adata, join="outer", label="sample_id", fill_value=0)
    merged_bin_sizes[size] = temp_merged
sdata_merged = SpatialData(
    images=images,
    tables=merged_bin_sizes
)

Which ultimately gives me this:

To me it seems like the way to go, but I was wondering if this is recommended or if there is another specific way of doing things to easy the downstream analysis? Perhaps even split things a bit further with the tables between 02,08 and 16 mu? Thanks