Mercurial > repos > ebi-gxa > decoupler_pathway_inference

annotate decoupler_aucell_score.py @ 0:77d680b36e23 draft

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author ebi-gxa
date Fri, 15 Mar 2024 12:17:49 +0000
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77d680b36e23 planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit 1034a450c97dcbb77871050cf0c6d3da90dac823
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1 import argparse
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2 import os
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3 import tempfile
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4
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5 import anndata
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6 import decoupler as dc
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7 import pandas as pd
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8
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9
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10 def read_gmt(gmt_file):
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11 """
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12 Reads a GMT file into a Pandas DataFrame.
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13
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14 Parameters
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15 ----------
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16 gmt_file : str
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17 Path to the GMT file.
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19 Returns
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20 -------
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21 pd.DataFrame
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22 A DataFrame with the gene sets. Each row represents a gene set, and the columns are "gene_set_name", "gene_set_url", and "genes".
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23 >>> line = "HALLMARK_NOTCH_SIGNALING\\thttp://www.gsea-msigdb.org/gsea/msigdb/human/geneset/HALLMARK_NOTCH_SIGNALING\\tJAG1\\tNOTCH3\\tNOTCH2\\tAPH1A\\tHES1\\tCCND1\\tFZD1\\tPSEN2\\tFZD7\\tDTX1\\tDLL1\\tFZD5\\tMAML2\\tNOTCH1\\tPSENEN\\tWNT5A\\tCUL1\\tWNT2\\tDTX4\\tSAP30\\tPPARD\\tKAT2A\\tHEYL\\tSKP1\\tRBX1\\tTCF7L2\\tARRB1\\tLFNG\\tPRKCA\\tDTX2\\tST3GAL6\\tFBXW11\\n"
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24 >>> line2 = "HALLMARK_APICAL_SURFACE\\thttp://www.gsea-msigdb.org/gsea/msigdb/human/geneset/HALLMARK_APICAL_SURFACE\\tB4GALT1\\tRHCG\\tMAL\\tLYPD3\\tPKHD1\\tATP6V0A4\\tCRYBG1\\tSHROOM2\\tSRPX\\tMDGA1\\tTMEM8B\\tTHY1\\tPCSK9\\tEPHB4\\tDCBLD2\\tGHRL\\tLYN\\tGAS1\\tFLOT2\\tPLAUR\\tAKAP7\\tATP8B1\\tEFNA5\\tSLC34A3\\tAPP\\tGSTM3\\tHSPB1\\tSLC2A4\\tIL2RB\\tRTN4RL1\\tNCOA6\\tSULF2\\tADAM10\\tBRCA1\\tGATA3\\tAFAP1L2\\tIL2RG\\tCD160\\tADIPOR2\\tSLC22A12\\tNTNG1\\tSCUBE1\\tCX3CL1\\tCROCC\\n"
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25 >>> temp_dir = tempfile.gettempdir()
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26 >>> temp_gmt = os.path.join(temp_dir, "temp_file.gmt")
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27 >>> with open(temp_gmt, "w") as f:
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28 ... f.write(line)
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29 ... f.write(line2)
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30 288
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31 380
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32 >>> df = read_gmt(temp_gmt)
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33 >>> df.shape[0]
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34 2
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35 >>> df.columns == ["gene_set_name", "genes"]
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36 array([ True, True])
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37 >>> df.loc[df["gene_set_name"] == "HALLMARK_APICAL_SURFACE"].genes.tolist()[0].startswith("B4GALT1")
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38 True
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39 """
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40 # Read the GMT file into a list of lines
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41 with open(gmt_file, "r") as f:
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42 lines = f.readlines()
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43
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44 # Create a list of dictionaries, where each dictionary represents a gene set
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45 gene_sets = []
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46 for line in lines:
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47 fields = line.strip().split("\t")
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48 gene_set = {"gene_set_name": fields[0], "genes": ",".join(fields[2:])}
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49 gene_sets.append(gene_set)
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50
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51 # Convert the list of dictionaries to a DataFrame
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52 return pd.DataFrame(gene_sets)
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53
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54
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55 def score_genes_aucell(
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56 adata: anndata.AnnData, gene_list: list, score_name: str, use_raw=False
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57 ):
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58 """Score genes using Aucell.
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59
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60 Parameters
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61 ----------
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62 adata : anndata.AnnData
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63 gene_list : list
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64 score_names : str
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65 use_raw : bool, optional
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66
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67 >>> import scanpy as sc
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68 >>> import decoupler as dc
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69 >>> adata = sc.datasets.pbmc68k_reduced()
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70 >>> gene_list = adata.var[adata.var.index.str.startswith("RP")].index.tolist()
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71 >>> score_genes_aucell(adata, gene_list, "ribosomal_aucell", use_raw=False)
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72 >>> "ribosomal_aucell" in adata.obs.columns
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73 True
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74 """
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75 # make a data.frame with two columns, geneset and gene_id, geneset filled with score_names and gene_id with gene_list, one row per element
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76 geneset_df = pd.DataFrame(
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77 {
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78 "gene_id": gene_list,
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79 "geneset": score_name,
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80 }
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81 )
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82 # run decoupler's run_aucell
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83 dc.run_aucell(
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84 adata, net=geneset_df, source="geneset", target="gene_id", use_raw=use_raw
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85 )
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86 # copy .obsm['aucell_estimate'] matrix columns to adata.obs using the column names
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87 adata.obs[score_name] = adata.obsm["aucell_estimate"][score_name]
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88
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89
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90 def run_for_genelists(
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91 adata, gene_lists, score_names, use_raw=False, gene_symbols_field="gene_symbols"
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92 ):
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93 if len(gene_lists) == len(score_names):
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94 for gene_list, score_names in zip(gene_lists, score_names):
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95 genes = gene_list.split(",")
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96 ens_gene_ids = adata.var[adata.var[gene_symbols_field].isin(genes)].index
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97 score_genes_aucell(
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98 adata,
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99 ens_gene_ids,
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100 f"AUCell_{score_names}",
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101 use_raw,
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102 )
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103 else:
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104 raise ValueError(
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105 "The number of gene lists (separated by :) and score names (separated by :) must be the same"
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106 )
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107
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108
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109 if __name__ == "__main__":
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110 # Create command-line arguments parser
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111 parser = argparse.ArgumentParser(description="Score genes using Aucell")
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112 parser.add_argument(
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113 "--input_file", type=str, help="Path to input AnnData file", required=True
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114 )
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115 parser.add_argument(
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116 "--output_file", type=str, help="Path to output file", required=True
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117 )
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118 parser.add_argument("--gmt_file", type=str, help="Path to GMT file", required=False)
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119 # add argument for gene sets to score
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120 parser.add_argument(
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121 "--gene_sets_to_score",
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122 type=str,
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123 required=False,
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124 help="Optional comma separated list of gene sets to score (the need to be in the gmt file)",
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125 )
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126 # add argument for gene list (comma separated) to score
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127 parser.add_argument(
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128 "--gene_lists_to_score",
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129 type=str,
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130 required=False,
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131 help="Comma separated list of genes to score. You can have more than one set of genes, separated by colon :",
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132 )
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133 # argument for the score name when using the gene list
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134 parser.add_argument(
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135 "--score_names",
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136 type=str,
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137 required=False,
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138 help="Name of the score column when using the gene list. You can have more than one set of score names, separated by colon :. It should be the same length as the number of gene lists.",
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139 )
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140 parser.add_argument(
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141 "--gene_symbols_field",
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142 type=str,
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143 help="Name of the gene symbols field in the AnnData object",
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144 required=True,
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145 )
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146 parser.add_argument("--use_raw", action="store_true", help="Use raw data")
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147 parser.add_argument(
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148 "--write_anndata", action="store_true", help="Write the modified AnnData object"
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149 )
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150
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151 # Parse command-line arguments
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152 args = parser.parse_args()
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153
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154 # Load input AnnData object
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155 adata = anndata.read_h5ad(args.input_file)
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156
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157 if args.gmt_file is not None:
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158 # Load MSigDB file in GMT format
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159 msigdb = read_gmt(args.gmt_file)
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160
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161 gene_sets_to_score = args.gene_sets_to_score.split(",") if args.gene_sets_to_score else []
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162 # Score genes by their ensembl ids using the score_genes_aucell function
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163 for _, row in msigdb.iterrows():
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164 gene_set_name = row["gene_set_name"]
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165 if not gene_sets_to_score or gene_set_name in gene_sets_to_score:
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166 genes = row["genes"].split(",")
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167 # Convert gene symbols to ensembl ids by using the columns gene_symbols and index in adata.var specific to the gene set
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168 ens_gene_ids = adata.var[
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169 adata.var[args.gene_symbols_field].isin(genes)
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170 ].index
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171 score_genes_aucell(
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172 adata, ens_gene_ids, f"AUCell_{gene_set_name}", args.use_raw
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173 )
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174 elif args.gene_lists_to_score is not None and args.score_names is not None:
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175 gene_lists = args.gene_lists_to_score.split(":")
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176 score_names = args.score_names.split(",")
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177 run_for_genelists(
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178 adata, gene_lists, score_names, args.use_raw, args.gene_symbols_field
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179 )
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180
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181 # Save the modified AnnData object or generate a file with cells as rows and the new score_names columns
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182 if args.write_anndata:
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183 adata.write_h5ad(args.output_file)
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184 else:
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185 new_columns = [col for col in adata.obs.columns if col.startswith("AUCell_")]
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186 adata.obs[new_columns].to_csv(args.output_file, sep="\t", index=True)