I would like to add a anndata.merge function, with similar functionality to xarray.merge.
Example
>>> expr_adata
AnnData object with n_obs × n_vars = 3000 × 15000
layers: 'counts'
>>> pca_adata
AnnData object with n_obs × n_vars = 3000 × 15000
uns: 'pca'
obsm: 'X_pca'
varm: 'PCs'
>>> ad.merge([expr_adata, pca_adata])
AnnData object with n_obs × n_vars = 3000 × 15000
layers: 'counts'
uns: 'pca'
obsm: 'X_pca'
varm: 'PCs'Use cases
Partial-AnnDatas returned from functions
Many scanpy function take an anndata object, produce a number of elements, and add them back to the original anndata object. We could instead produce a new object which only holds the new elements, then ad.merge the results together. By itself, this is the exact same thing, but this refactoring would allow a few new uses.
Instead of updating the original, we could keep the results seperate. This would be useful for generating multiple parameterizations, or having a lightweight object to pass to further objects – as opposed to mutating or copying the whole original object.
Seperating parts of analyses
We could want to keep elements from annotation or analysis seperate until we need them. We could avoid keeping the large arrays in layers for a velocity analysis, until we actually want them.
scirpy
Scirpy has a function for doing this specifically with immense receptor data: scirpy.pp.merge_with_ir. This would be a more general case. The IR AnnData here is a bit like the "partial-AnnDatas" discussed above.
(please let me know if this isn't the case @grst)
Previous discussion
This has been suggested and discussed a number of places.
Requirements
This would require full support for adata.X = None #467
Implimenting this would fit well with an anndata.align (#531) function (e.g. pass multiple anndata objects, return them with axes aligned). As the updates and reindexing are orthogonal.
I would like to add a
anndata.mergefunction, with similar functionality toxarray.merge.Example
Use cases
Partial-AnnDatas returned from functions
Many
scanpyfunction take an anndata object, produce a number of elements, and add them back to the original anndata object. We could instead produce a new object which only holds the new elements, thenad.mergethe results together. By itself, this is the exact same thing, but this refactoring would allow a few new uses.Instead of updating the original, we could keep the results seperate. This would be useful for generating multiple parameterizations, or having a lightweight object to pass to further objects – as opposed to mutating or copying the whole original object.
Seperating parts of analyses
We could want to keep elements from annotation or analysis seperate until we need them. We could avoid keeping the large arrays in
layersfor a velocity analysis, until we actually want them.scirpy
Scirpy has a function for doing this specifically with immense receptor data:
scirpy.pp.merge_with_ir. This would be a more general case. The IRAnnDatahere is a bit like the "partial-AnnDatas" discussed above.(please let me know if this isn't the case @grst)
Previous discussion
This has been suggested and discussed a number of places.
ad.merge(adata, {"obs": df}), i.e. other objects to merge can just be mappings.Requirements
This would require full support for
adata.X = None#467Implimenting this would fit well with an
anndata.align(#531) function (e.g. pass multiple anndata objects, return them with axes aligned). As the updates and reindexing are orthogonal.