Subsample by observations grouping

• Additional function parameters / changed functionality / changed defaults?
• New analysis tool: A simple analysis tool you have been using and are missing in sc.tools?
• New plotting function: A kind of plot you would like to seein sc.pl?
• External tools: Do you know an existing package that should go into sc.external.*?
• Other?

Related to scanpy.pp.subsample, it would be useful to have a subsampling tool that subsamples based on the key of an observations grouping. E.g., if I have an observation key ‘MyGroup’ with possible values [‘A’, ‘B’], and there are 10,000 cells of type ‘A’ and 2,000 cells of type ‘B’ and I want only max 5,000 cells of each type, then this function would subsample 5,000 cells of type ‘A’ but retain all 2,000 cells of type ‘B’.

[giovp]

I’ll reopen this cause I think it’s quite relevant still and could be very straightforward to implement with sklearn resample

also, there is an entire package for subsampling strategies which is probably quite relevant: https://github.com/scikit-learn-contrib/imbalanced-learn

line here for reference: https://github.com/theislab/scanpy/blob/48cc7b38f1f31a78902a892041902cc810ddfcd3/scanpy/preprocessing/_simple.py#L857

[LuckyMD]

Something like this should work. Note, this is not tested.

target_cells = 5000

adatas = [adata[adata.obs[cluster_key].isin(clust)] for clust in adata.obs[cluster_key].cat.categories]

for dat in adatas:
    if dat.n_obs > target_cells:
         sc.pp.subsample(dat, n_obs=target_cells)

adata_downsampled = adatas[0].concatenate(*adatas[1:])

Hope that helps.