Mercurial > repos > goeckslab > scimap_mcmicro_to_anndata

annotate scimap_phenotyping.py @ 4:49c178651194 draft default tip

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planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit c39cea5ae2ebf61b3d51d687fd9d2930c907b72d
author goeckslab
date Tue, 30 Jul 2024 18:20:49 +0000
parents 4c767e1a9e7c
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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
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2 import warnings
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4 import pandas as pd
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5 import scimap as sm
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6 from anndata import read_h5ad
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8
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9 def main(
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10 adata,
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11 output,
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12 log,
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13 gating_workflow,
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14 gating_workflow_ext,
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15 manual_gates=None,
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16 manual_gates_ext=None,
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17 random_state=0
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18 ):
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19 """
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20 Parameter
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21 ---------
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22 adata : str
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23 File path to the input AnnData.
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24 output : str
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25 File path to the output AnnData.
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26 log: bool
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27 Boolean whether to log the input data prior to rescaling
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28 gating_workflow : str
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29 File path to the gating workflow.
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30 gating_workflow_ext : str
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31 Datatype for gating workflow, either 'csv' or 'tabular'.
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32 manual_gates : str
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33 File path to the munual gating.
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34 manual_gates_ext : str
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35 Datatype for munual gate, either 'csv' or 'tabular'.
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36 random_state: int
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37 The seed used by the random number generator for GMM in sm.pp.rescale
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38 """
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39 warnings.simplefilter('ignore')
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40
2a3152751ca8 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/scimap commit b19cb55dfb751cccc857b95a432890299bfeebb5
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41 adata = read_h5ad(adata)
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42 # Rescale data
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43 if manual_gates:
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44 sep = ',' if manual_gates_ext == 'csv' else '\t'
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45 manual_gates = pd.read_csv(manual_gates, sep=sep)
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46
2
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47 adata = sm.pp.rescale(
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48 adata,
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49 gate=manual_gates,
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50 log=log,
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51 random_state=random_state
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52 )
0
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53
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54 # Phenotype cells
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55 # Load the gating workflow
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56 sep = ',' if gating_workflow_ext == 'csv' else '\t'
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57 phenotype = pd.read_csv(gating_workflow, sep=sep)
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58 adata = sm.tl.phenotype_cells(
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59 adata,
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60 phenotype=phenotype,
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61 label="phenotype"
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62 )
0
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63
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64 # Summary of the phenotyping
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65 print(adata.obs['phenotype'].value_counts())
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66
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67 adata.write(output)
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68
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69
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70 if __name__ == '__main__':
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71 aparser = argparse.ArgumentParser()
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72 aparser.add_argument("-a", "--adata", dest="adata", required=True)
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73 aparser.add_argument("-o", "--output", dest="output", required=True)
2
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74 aparser.add_argument("-l", "--log", dest="log", action="store_true")
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75 aparser.add_argument(
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76 "-g",
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77 "--gating_workflow",
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78 dest="gating_workflow",
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79 required=True
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80 )
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81 aparser.add_argument(
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82 "-s",
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83 "--gating_workflow_ext",
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84 dest="gating_workflow_ext",
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85 required=True
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86 )
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87 aparser.add_argument(
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88 "-m",
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89 "--manual_gates",
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90 dest="manual_gates",
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91 required=False
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92 )
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93 aparser.add_argument(
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94 "-S",
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95 "--manual_gates_ext",
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96 dest="manual_gates_ext",
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97 required=False
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98 )
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99 aparser.add_argument(
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100 "--random_state",
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101 dest="random_state",
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102 type=int,
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103 required=False
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104 )
0
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105
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106 args = aparser.parse_args()
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107
2
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108 if args.log:
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109 print("\n adata.raw.X will be log1p transformed \n")
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110
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111 main(
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112 adata=args.adata,
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113 output=args.output,
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114 log=args.log,
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115 gating_workflow=args.gating_workflow,
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116 gating_workflow_ext=args.gating_workflow_ext,
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117 manual_gates=args.manual_gates,
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118 manual_gates_ext=args.manual_gates_ext,
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119 random_state=args.random_state
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120 )