Mercurial > repos > ebi-gxa > decoupler_pathway_inference

annotate decoupler_pathway_inference.py @ 2:82b7cd3e1bbd draft

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planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit b01245159f9cb67101497bb974b2c13bcee019b7
author ebi-gxa
date Tue, 16 Apr 2024 11:49:19 +0000
parents 77d680b36e23
children c6787c2aee46
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1 # import the necessary packages
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2 import argparse
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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", "--output",
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24 help="output files prefix",
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25 default=None,
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26 )
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28 # path to save Activities AnnData file
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29 parser.add_argument(
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30 "-a", "--activities_path", help="Path to save Activities AnnData file", default=None
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31 )
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33 # Column name in net with source nodes
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34 parser.add_argument(
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35 "-s", "--source", help="Column name in net with source nodes.", default="source"
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36 )
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38 # Column name in net with target nodes
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39 parser.add_argument(
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40 "-t", "--target", help="Column name in net with target nodes.", default="target"
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41 )
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43 # Column name in net with weights.
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44 parser.add_argument(
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45 "-w", "--weight", help="Column name in net with weights.", default="weight"
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46 )
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47
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48 # add boolean argument for use_raw
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49 parser.add_argument(
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50 "--use_raw", action="store_true", default=False, help="Whether to use the raw part of the AnnData object"
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51 )
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53 # add argument for min_cells
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54 parser.add_argument(
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55 "--min_n", help="Minimum of targets per source. If less, sources are removed.", default=5, type=int
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56 )
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57
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58 # add activity inference method option
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59 parser.add_argument(
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60 "-m", "--method", help="Activity inference method", default="mlm", required=True
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61 )
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62 args = parser.parse_args()
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63
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64 # check that either -o or --output is specified
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65 if args.output is None:
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66 raise ValueError("Please specify either -o or --output")
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68 # read in the AnnData input file
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69 adata = ad.read_h5ad(args.input_anndata)
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70
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71 # read in the input file network input file
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72 network = pd.read_csv(args.input_network, sep='\t')
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73
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74 if (
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75 args.source not in network.columns
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76 or args.target not in network.columns
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77 or args.weight not in network.columns
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78 ):
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79 raise ValueError(
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80 "Source, target, and weight columns are not present in the network"
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81 )
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82
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83
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84 print(type(args.min_n))
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85
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86 if args.method == "mlm":
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87 dc.run_mlm(
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88 mat=adata,
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89 net=network,
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90 source=args.source,
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91 target=args.target,
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92 weight=args.weight,
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93 verbose=True,
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94 min_n=args.min_n,
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95 use_raw=args.use_raw
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96 )
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97
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98 if args.output is not None:
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99 # write adata.obsm[mlm_key] and adata.obsm[mlm_pvals_key] to the output network files
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100 combined_df = pd.concat([adata.obsm["mlm_estimate"], adata.obsm["mlm_pvals"]], axis=1)
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101
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102 # Save the combined dataframe to a file
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103 combined_df.to_csv(args.output + ".tsv", sep="\t")
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104
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105 # if args.activities_path is specified, generate the activities AnnData and save the AnnData object to the specified path
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106 if args.activities_path is not None:
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107 acts = dc.get_acts(adata, obsm_key="mlm_estimate")
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108 acts.write_h5ad(args.activities_path)
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109
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110 elif args.method == "ulm":
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111 dc.run_ulm(
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112 mat=adata,
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113 net=network,
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114 source=args.source,
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115 target=args.target,
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116 weight=args.weight,
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117 verbose=True,
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118 min_n=args.min_n,
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119 use_raw=args.use_raw
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120 )
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121
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122 if args.output is not None:
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123 # write adata.obsm[mlm_key] and adata.obsm[mlm_pvals_key] to the output network files
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124 combined_df = pd.concat([adata.obsm["ulm_estimate"], adata.obsm["ulm_pvals"]], axis=1)
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125
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126 # Save the combined dataframe to a file
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127 combined_df.to_csv(args.output + ".tsv", sep="\t")
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128
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129 # if args.activities_path is specified, generate the activities AnnData 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="ulm_estimate")
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132 acts.write_h5ad(args.activities_path)