| Previous changeset 2:ce22e846c5e4 (2024-06-10) Next changeset 4:88fca6e905be (2024-07-30) |
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Commit message:
planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit 49210b00535415865694ddbec16238d8cf5e6bb0 |
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modified:
main_macros.xml scimap_spatial.py |
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| diff -r ce22e846c5e4 -r 3a441d347a46 main_macros.xml --- a/main_macros.xml Mon Jun 10 18:44:25 2024 +0000 +++ b/main_macros.xml Wed Jun 26 15:27:07 2024 +0000 |
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| @@ -1,6 +1,6 @@ <macros> <token name="@TOOL_VERSION@">2.1.0</token> - <token name="@VERSION_SUFFIX@">0</token> + <token name="@VERSION_SUFFIX@">1</token> <token name="@PROFILE@">20.01</token> <xml name="scimap_requirements"> |
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| diff -r ce22e846c5e4 -r 3a441d347a46 scimap_spatial.py --- a/scimap_spatial.py Mon Jun 10 18:44:25 2024 +0000 +++ b/scimap_spatial.py Wed Jun 26 15:27:07 2024 +0000 |
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| @@ -2,6 +2,7 @@ import json import warnings +import pandas as pd import scimap as sm from anndata import read_h5ad @@ -28,6 +29,8 @@ tool_func = getattr(sm.tl, tool) options = params['analyses']['options'] + + # tool specific pre-processing if tool == 'cluster': options['method'] = params['analyses']['method'] subset_genes = options.pop('subset_genes') @@ -38,15 +41,42 @@ if sub_cluster_group: options['sub_cluster_group'] = \ [x.strip() for x in sub_cluster_group.split(',')] + elif tool == 'spatial_lda': + max_weight_assignment = options.pop('max_weight_assignment') for k, v in options.items(): if v == '': options[k] = None + # tool execution tool_func(adata, **options) + # spatial LDA post-processing if tool == 'spatial_lda': - adata.uns.pop('spatial_lda_model') + + if max_weight_assignment: + # assign cell to a motif based on maximum weight + adata.uns['spatial_lda']['neighborhood_motif'] = \ + adata.uns['spatial_lda'].idxmax(axis=1) + + # merge motif assignment into adata.obs + adata.obs = pd.merge( + adata.obs, + adata.uns['spatial_lda']['neighborhood_motif'], + left_index=True, + right_index=True + ) + + # write out LDA results as tabular files + # so they're accessible to Galaxy users + adata.uns['spatial_lda'].reset_index().to_csv( + 'lda_weights.txt', sep='\t', index=False) + adata.uns['spatial_lda_probability'].T.reset_index( + names='motif').to_csv( + 'lda_probabilities.txt', sep='\t', index=False) + + if 'spatial_lda_model' in adata.uns: + adata.uns.pop('spatial_lda_model') adata.write(output) |