AttributeError: 'SpectralEmbedding' object has no attribute 'transform'

[jipeifeng]

Hi: Sorry for bother you again. When mapping the new data set to the reference, error happened.

Command lines:

1. Generating reference stream.select_variable_genes(wt) stream.dimension_reduction(wt, n_jobs=4) stream.seed_elastic_principal_graph(wt) stream.elastic_principal_graph(wt,incr_n_nodes=10, epg_alpha=0.03, epg_trimmingradius=0.1) stream.optimize_branching(wt) stream.prune_elastic_principal_graph(wt) stream.shift_branching(wt) stream.extend_elastic_principal_graph(wt)

This works well.

2. Mapping step ko = sc.read_h5ad('ko.h5ad') stream.set_workdir(ko, './stream') stream.remove_mt_genes(ko) ko.obs.rename(columns={'type':'label'}, inplace=True) stream.add_cell_colors(ko) sc.pp.subsample(ko, n_obs=10000) stream.map_new_data(wt, ko, method='se') Error:

---

AttributeError Traceback (most recent call last) in ----> 1 stream.map_new_data(wt, ko, method='se')

/miniconda3/envs/stream/lib/python3.7/site-packages/stream/core.py in map_new_data(adata, adata_new, feature, method, use_radius) 4830 if(method == 'se'): 4831 trans = adata.uns['trans_se'] -> 4832 adata_new.obsm['X_se_mapping'] = trans.transform(input_data) 4833 adata_new.obsm['X_dr'] = adata_new.obsm['X_se_mapping'].copy() 4834 if(method == 'mlle'):

AttributeError: 'SpectralEmbedding' object has no attribute 'transform'

Thanks for you help!

Best,

Peifeng

[jipeifeng]

I believe that the issue should be caused by a bug. I have looked into the code in core.py and found that the 'SpectralEmbedding' only have 'fit' and 'fit_transform' function. But, which function should I choose? PS: I have chose fit_transform to run, it works.

[huidongchen]

Thanks for the important feedback! I will try to fix it in the next update.

[huidongchen]

Hi Peifeng,

I was looking into the issue and just realized that the spectral embedding method se currently doesn't have the method transform supported in scikit-learn yet. Unless we write the projection function ourselves (this may take a long time), currently in STREAM, to map new data, you might have to use other available methods (e.g. mlle or umap. but you need to use the same method during the dimension reduction step)

[huidongchen]

I believe that the issue should be caused by a bug. I have looked into the code in core.py and found that the 'SpectralEmbedding' only have 'fit' and 'fit_transform' function. But, which function should I choose? PS: I have chose fit_transform to run, it works.

fit_transform will generate a new embedding space instead of projecting new points into the same embedding space used for the reference data. The result (generate a new embedding space for new points) might be similar to using transform (projecting new points to the same reference space) but like I said, it's not a mapping process any more. so you might need to be cautious about the final result.

[jipeifeng]

Ok, got it ! I'll use available methods. Thank for the reply.

[ccshao]

We found the "se" projection provides better trajectories than the "mlle", which is influenced by extream points when the data is small. Hope to see the "se" mapping strategy available soon.