Mercurial > repos > goeckslab > scimap_mcmicro_to_anndata

annotate scimap_spatial.py @ 3:8c55377d7f06 draft

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planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit 49210b00535415865694ddbec16238d8cf5e6bb0
author goeckslab
date Wed, 26 Jun 2024 15:27:13 +0000
parents 8ca435ec19be
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2a3152751ca8 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit b19cb55dfb751cccc857b95a432890299bfeebb5
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diff changeset
1 import argparse
2a3152751ca8 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit b19cb55dfb751cccc857b95a432890299bfeebb5
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2 import json
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3 import warnings
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4
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5 import pandas as pd
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2a3152751ca8 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit b19cb55dfb751cccc857b95a432890299bfeebb5
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6 import scimap as sm
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7 from anndata import read_h5ad
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10 def main(inputs, anndata, output):
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11 """
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12 Parameter
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13 ---------
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14 inputs : str
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15 File path to galaxy tool parameter.
2a3152751ca8 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit b19cb55dfb751cccc857b95a432890299bfeebb5
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16 anndata : str
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17 File path to anndata containing phenotyping info.
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18 output : str
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19 File path to output.
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20 """
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21 warnings.simplefilter('ignore')
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23 with open(inputs, 'r') as param_handler:
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24 params = json.load(param_handler)
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26 adata = read_h5ad(anndata)
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28 tool = params['analyses']['selected_tool']
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29 tool_func = getattr(sm.tl, tool)
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31 options = params['analyses']['options']
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33 # tool specific pre-processing
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34 if tool == 'cluster':
2a3152751ca8 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit b19cb55dfb751cccc857b95a432890299bfeebb5
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35 options['method'] = params['analyses']['method']
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36 subset_genes = options.pop('subset_genes')
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37 if subset_genes:
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38 options['subset_genes'] = \
2a3152751ca8 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit b19cb55dfb751cccc857b95a432890299bfeebb5
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39 [x.strip() for x in subset_genes.split(',')]
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40 sub_cluster_group = options.pop('sub_cluster_group')
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41 if sub_cluster_group:
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42 options['sub_cluster_group'] = \
2a3152751ca8 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit b19cb55dfb751cccc857b95a432890299bfeebb5
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43 [x.strip() for x in sub_cluster_group.split(',')]
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44 elif tool == 'spatial_lda':
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45 max_weight_assignment = options.pop('max_weight_assignment')
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47 for k, v in options.items():
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48 if v == '':
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49 options[k] = None
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50
3
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51 # tool execution
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52 tool_func(adata, **options)
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53
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54 # spatial LDA post-processing
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55 if tool == 'spatial_lda':
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57 if max_weight_assignment:
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58 # assign cell to a motif based on maximum weight
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59 adata.uns['spatial_lda']['neighborhood_motif'] = \
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60 adata.uns['spatial_lda'].idxmax(axis=1)
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62 # merge motif assignment into adata.obs
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63 adata.obs = pd.merge(
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64 adata.obs,
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65 adata.uns['spatial_lda']['neighborhood_motif'],
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66 left_index=True,
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67 right_index=True
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68 )
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69
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70 # write out LDA results as tabular files
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71 # so they're accessible to Galaxy users
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72 adata.uns['spatial_lda'].reset_index().to_csv(
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73 'lda_weights.txt', sep='\t', index=False)
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74 adata.uns['spatial_lda_probability'].T.reset_index(
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75 names='motif').to_csv(
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76 'lda_probabilities.txt', sep='\t', index=False)
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77
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78 if 'spatial_lda_model' in adata.uns:
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79 adata.uns.pop('spatial_lda_model')
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80
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81 adata.write(output)
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82
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83
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84 if __name__ == '__main__':
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85 aparser = argparse.ArgumentParser()
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86 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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87 aparser.add_argument("-e", "--output", dest="output", required=True)
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88 aparser.add_argument("-a", "--anndata", dest="anndata", required=True)
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89
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90 args = aparser.parse_args()
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91
2a3152751ca8 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit b19cb55dfb751cccc857b95a432890299bfeebb5
goeckslab
parents:
diff changeset
92 main(args.inputs, args.anndata, args.output)