Mercurial > repos > ebi-gxa > decoupler_pathway_inference

annotate decoupler_pathway_inference.py @ 3:c6787c2aee46 draft

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planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit eea5c13f9e6e070a2359c59400773b01f9cd7567
author ebi-gxa
date Mon, 15 Jul 2024 10:56:37 +0000
parents 77d680b36e23
children 9864fd2cc1f0
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1 # import the necessary packages
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2 import argparse
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3
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4 import anndata as ad
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5 import decoupler as dc
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6 import pandas as pd
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8 # define arguments for the script
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9 parser = argparse.ArgumentParser()
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11 # add AnnData input file option
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12 parser.add_argument(
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13 "-i", "--input_anndata", help="AnnData input file", required=True
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14 )
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16 # add network input file option
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17 parser.add_argument(
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18 "-n", "--input_network", help="Network input file", required=True
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19 )
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21 # output file prefix
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22 parser.add_argument(
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23 "-o",
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24 "--output",
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25 help="output files prefix",
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26 default=None,
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27 )
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29 # path to save Activities AnnData file
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30 parser.add_argument(
3
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31 "-a",
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32 "--activities_path",
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33 help="Path to save Activities AnnData file",
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34 default=None,
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35 )
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36
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37 # Column name in net with source nodes
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38 parser.add_argument(
3
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39 "-s",
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40 "--source",
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41 help="Column name in net with source nodes.",
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42 default="source",
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43 )
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45 # Column name in net with target nodes
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46 parser.add_argument(
3
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47 "-t",
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48 "--target",
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49 help="Column name in net with target nodes.",
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50 default="target",
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51 )
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52
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53 # Column name in net with weights.
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54 parser.add_argument(
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55 "-w", "--weight", help="Column name in net with weights.", default="weight"
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56 )
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57
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58 # add boolean argument for use_raw
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59 parser.add_argument(
3
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60 "--use_raw",
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61 action="store_true",
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62 default=False,
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63 help="Whether to use the raw part of the AnnData object",
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64 )
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65
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66 # add argument for min_cells
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67 parser.add_argument(
3
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68 "--min_n",
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69 help="Minimum of targets per source. If less, sources are removed.",
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70 default=5,
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71 type=int,
0
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72 )
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73
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74 # add activity inference method option
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75 parser.add_argument(
3
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76 "-m",
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77 "--method",
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78 help="Activity inference method",
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79 default="mlm",
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80 required=True,
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81 )
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82 args = parser.parse_args()
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83
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84 # check that either -o or --output is specified
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85 if args.output is None:
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86 raise ValueError("Please specify either -o or --output")
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87
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88 # read in the AnnData input file
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89 adata = ad.read_h5ad(args.input_anndata)
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90
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91 # read in the input file network input file
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92 network = pd.read_csv(args.input_network, sep="\t")
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93
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94 if (
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95 args.source not in network.columns
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96 or args.target not in network.columns
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97 or args.weight not in network.columns
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98 ):
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99 raise ValueError(
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100 "Source, target, and weight columns are not present in the network"
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101 )
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102
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103
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104 print(type(args.min_n))
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105
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106 if args.method == "mlm":
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107 dc.run_mlm(
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108 mat=adata,
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109 net=network,
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110 source=args.source,
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111 target=args.target,
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112 weight=args.weight,
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113 verbose=True,
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114 min_n=args.min_n,
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115 use_raw=args.use_raw,
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116 )
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117
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118 if args.output is not None:
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119 # write adata.obsm[mlm_key] and adata.obsm[mlm_pvals_key] to the
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120 # output network files
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121 combined_df = pd.concat(
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122 [adata.obsm["mlm_estimate"], adata.obsm["mlm_pvals"]], axis=1
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123 )
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124
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125 # Save the combined dataframe to a file
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126 combined_df.to_csv(args.output + ".tsv", sep="\t")
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127
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128 # if args.activities_path is specified, generate the activities AnnData
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129 # and save the AnnData object to the specified path
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130 if args.activities_path is not None:
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131 acts = dc.get_acts(adata, obsm_key="mlm_estimate")
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132 acts.write_h5ad(args.activities_path)
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133
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134 elif args.method == "ulm":
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135 dc.run_ulm(
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136 mat=adata,
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137 net=network,
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138 source=args.source,
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139 target=args.target,
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140 weight=args.weight,
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141 verbose=True,
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142 min_n=args.min_n,
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143 use_raw=args.use_raw,
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144 )
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145
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146 if args.output is not None:
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147 # write adata.obsm[mlm_key] and adata.obsm[mlm_pvals_key] to the
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148 # output network files
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149 combined_df = pd.concat(
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150 [adata.obsm["ulm_estimate"], adata.obsm["ulm_pvals"]], axis=1
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151 )
0
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152
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153 # Save the combined dataframe to a file
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154 combined_df.to_csv(args.output + ".tsv", sep="\t")
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155
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156 # if args.activities_path is specified, generate the activities AnnData
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157 # and save the AnnData object to the specified path
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158 if args.activities_path is not None:
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159 acts = dc.get_acts(adata, obsm_key="ulm_estimate")
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160 acts.write_h5ad(args.activities_path)