Open ivirshup opened 2 years ago
sgkit
does something similar to the "Partial-AnnDatas returned from functions", and controls whether a partial or updated object is returned with the merge
kwarg
Just noting that the scirpy use-case is obsolete in favor of MuData after https://github.com/scverse/scirpy/pull/356/ gets merged.
This would be very nice to have, we produce a lot of heavy objects redundantly due to parametrisation of runs.
I would like to add a
anndata.merge
function, with similar functionality toxarray.merge
.Example
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, thenad.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 IRAnnData
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.
266
441, though the desired API is more like
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.