adding function for mode="nmf" #67
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tangram/mapping_utils.py
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| cluster_label (String): field in `adata.obs` used for aggregating values | ||
| nmf_coeffs (pd.DataFrame): NMF `H` matrix (factor by sample) | ||
| sample_name (String): name of the sample to be mapped as labeled in `nmf_coeffs` | ||
| scale (bool): Optional. Whether weight input single cell by # of cells in cluster. Default is True. |
| @@ -210,6 +258,10 @@ def map_cells_to_space( | |||
| adata_sc = adata_to_cluster_expression( | |||
| adata_sc, cluster_label, scale, add_density=True | |||
| ) | |||
| if mode == "nmf": | |||
| adata_sc = adata_to_nmf_factor_expression( | |||
| adata_sc, cluster_label, nmf_coeffs, sample_name, add_density=True | |||
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Does it make sense to assume that you have an "adata_sc"? For NMF mapping, you're just starting with nmf_factors and nmf_coeffs. I would recommend passing both of those, and building the adata in the new function.
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One usually runs tg.pp_adatas() before tg.map_cells_to_space() (which calls adata_to_nmf_factor_expression()). So, I think we can assume that we have adata_sc. Is that what you were wondering about?
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This implements
option 1i.e. mapping NMF W*H on spatial data.@hejinhuang could you please review it?