Mercurial > repos > ebi-gxa > decoupler_pathway_inference

annotate decoupler_aucell_score.py @ 2:82b7cd3e1bbd draft default tip

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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 e9b06a8fb73a
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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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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 import numba as nb
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10
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11 def read_gmt_long(gmt_file):
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12 """
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13 Reads a GMT file and produce a Pandas DataFrame in long format, ready to be passed to the AUCell method.
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15 Parameters
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16 ----------
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17 gmt_file : str
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18 Path to the GMT file.
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20 Returns
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21 -------
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22 pd.DataFrame
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23 A DataFrame with the gene sets. Each row represents a gene set to gene assignment, and the columns are "gene_set_name" and "genes".
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24 >>> 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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25 >>> 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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26 >>> temp_dir = tempfile.gettempdir()
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27 >>> temp_gmt = os.path.join(temp_dir, "temp_file.gmt")
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28 >>> with open(temp_gmt, "w") as f:
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29 ... f.write(line)
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30 ... f.write(line2)
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31 288
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32 380
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33 >>> df = read_gmt_long(temp_gmt)
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34 >>> df.shape[0]
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35 76
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36 >>> len(df.loc[df["gene_set"] == "HALLMARK_APICAL_SURFACE"].gene.tolist())
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37 44
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38 """
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39 # Create a list of dictionaries, where each dictionary represents a gene set
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40 gene_sets = {}
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41
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42 # Read the GMT file into a list of lines
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43 with open(gmt_file, "r") as f:
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44 while True:
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45 line = f.readline()
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46 if not line:
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47 break
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48 fields = line.strip().split("\t")
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49 gene_sets[fields[0]]= fields[2:]
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50
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51 return pd.concat(pd.DataFrame({'gene_set':k, 'gene':v}) for k, v in gene_sets.items())
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53
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54 def score_genes_aucell_mt(adata: anndata.AnnData, gene_set_gene: pd.DataFrame, use_raw=False, min_n_genes=5, var_gene_symbols_field=None):
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55 """Score genes using Aucell.
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57 Parameters
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58 ----------
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59 adata : anndata.AnnData
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60 gene_set_gene: pd.DataFrame with columns gene_set and gene
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61 use_raw : bool, optional, False by default.
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62 min_n_genes : int, optional, 5 by default.
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63 var_gene_symbols_field : str, optional, None by default. The field in var where gene symbols are stored
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64
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65 >>> import scanpy as sc
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66 >>> import decoupler as dc
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67 >>> adata = sc.datasets.pbmc68k_reduced()
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68 >>> r_gene_list = adata.var[adata.var.index.str.startswith("RP")].index.tolist()
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69 >>> m_gene_list = adata.var[adata.var.index.str.startswith("M")].index.tolist()
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70 >>> gene_set = {}
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71 >>> gene_set["m"] = m_gene_list
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72 >>> gene_set["r"] = r_gene_list
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73 >>> gene_set_df = pd.concat(pd.DataFrame({'gene_set':k, 'gene':v}) for k, v in gene_set.items())
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74 >>> score_genes_aucell_mt(adata, gene_set_df, use_raw=False)
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75 >>> "AUCell_m" in adata.obs.columns
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76 True
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77 >>> "AUCell_r" in adata.obs.columns
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78 True
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79 """
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80
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81 # if var_gene_symbols_fiels is provided, transform gene_set_gene df so that gene contains gene ids instead of gene symbols
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82 if var_gene_symbols_field:
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83 # merge the index of var to gene_set_gene df based on var_gene_symbols_field
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84 var_id_symbols = adata.var[[var_gene_symbols_field]]
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85 var_id_symbols['gene_id'] = var_id_symbols.index
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86
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87 gene_set_gene = gene_set_gene.merge(var_id_symbols, left_on='gene', right_on=var_gene_symbols_field, how='left')
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88 # this will still produce some empty gene_ids (genes in the gene_set_gene df that are not in the var df), fill those
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89 # with the original gene symbol from the gene_set to avoid deforming the AUCell calculation
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90 gene_set_gene['gene_id'] = gene_set_gene['gene_id'].fillna(gene_set_gene['gene'])
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91 gene_set_gene['gene'] = gene_set_gene['gene_id']
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92
0
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93 # run decoupler's run_aucell
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94 dc.run_aucell(
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95 adata, net=gene_set_gene, source="gene_set", target="gene", use_raw=use_raw, min_n=min_n_genes
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96 )
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97 for gs in gene_set_gene.gene_set.unique():
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98 if gs in adata.obsm['aucell_estimate'].keys():
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99 adata.obs[f"AUCell_{gs}"] = adata.obsm["aucell_estimate"][gs]
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100
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101
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102 def run_for_genelists(
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103 adata, gene_lists, score_names, use_raw=False, gene_symbols_field=None, min_n_genes=5
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104 ):
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105 if len(gene_lists) == len(score_names):
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106 for gene_list, score_names in zip(gene_lists, score_names):
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107 genes = gene_list.split(",")
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108 gene_sets = {}
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109 gene_sets[score_names] = genes
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110 gene_set_gene_df = pd.concat(pd.DataFrame({'gene_set':k, 'gene':v}) for k, v in gene_sets.items())
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111
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112 score_genes_aucell_mt(
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113 adata,
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114 gene_set_gene_df,
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115 use_raw,
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116 min_n_genes,
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117 var_gene_symbols_field=gene_symbols_field
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118 )
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119 else:
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120 raise ValueError(
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121 "The number of gene lists (separated by :) and score names (separated by :) must be the same"
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122 )
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123
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124
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125 if __name__ == "__main__":
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126 # Create command-line arguments parser
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127 parser = argparse.ArgumentParser(description="Score genes using Aucell")
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128 parser.add_argument(
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129 "--input_file", type=str, help="Path to input AnnData file", required=True
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130 )
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131 parser.add_argument(
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132 "--output_file", type=str, help="Path to output file", required=True
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133 )
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134 parser.add_argument("--gmt_file", type=str, help="Path to GMT file", required=False)
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135 # add argument for gene sets to score
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136 parser.add_argument(
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137 "--gene_sets_to_score",
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138 type=str,
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139 required=False,
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140 help="Optional comma separated list of gene sets to score (the need to be in the gmt file)",
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141 )
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142 # add argument for gene list (comma separated) to score
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143 parser.add_argument(
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144 "--gene_lists_to_score",
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145 type=str,
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146 required=False,
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147 help="Comma separated list of genes to score. You can have more than one set of genes, separated by colon :",
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148 )
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149 # argument for the score name when using the gene list
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150 parser.add_argument(
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151 "--score_names",
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152 type=str,
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153 required=False,
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154 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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155 )
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156 parser.add_argument(
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157 "--gene_symbols_field",
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158 type=str,
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159 help="Name of the gene symbols field in the AnnData object",
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160 required=True,
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161 )
1
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162 # argument for min_n Minimum of targets per source. If less, sources are removed.
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163 parser.add_argument(
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164 "--min_n",
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165 type=int,
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166 required=False,
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167 default=5,
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168 help="Minimum of targets per source. If less, sources are removed.",
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169 )
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170 parser.add_argument("--use_raw", action="store_true", help="Use raw data")
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171 parser.add_argument(
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172 "--write_anndata", action="store_true", help="Write the modified AnnData object"
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173 )
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174 # argument for number of max concurrent processes
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175 parser.add_argument("--max_threads", type=int, required=False, default=1, help="Number of max concurrent threads")
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176
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177
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178 # Parse command-line arguments
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179 args = parser.parse_args()
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180
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181 nb.set_num_threads(n=args.max_threads)
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182
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183 # Load input AnnData object
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184 adata = anndata.read_h5ad(args.input_file)
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185
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186 if args.gmt_file is not None:
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187 # Load MSigDB file in GMT format
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188 # msigdb = read_gmt(args.gmt_file)
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189 msigdb = read_gmt_long(args.gmt_file)
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190
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191 gene_sets_to_score = (
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192 args.gene_sets_to_score.split(",") if args.gene_sets_to_score else []
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193 )
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194 if gene_sets_to_score:
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195 # we limit the GMT file read to the genesets specified in the gene_sets_to_score argument
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196 msigdb = msigdb[msigdb["gene_set"].isin(gene_sets_to_score)]
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197
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198 score_genes_aucell_mt(adata, msigdb, args.use_raw, args.min_n, var_gene_symbols_field=args.gene_symbols_field)
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199 elif args.gene_lists_to_score is not None and args.score_names is not None:
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200 gene_lists = args.gene_lists_to_score.split(":")
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201 score_names = args.score_names.split(",")
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202 run_for_genelists(
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203 adata, gene_lists, score_names, args.use_raw, args.gene_symbols_field, args.min_n
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204 )
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205
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206 # Save the modified AnnData object or generate a file with cells as rows and the new score_names columns
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207 if args.write_anndata:
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208 adata.write_h5ad(args.output_file)
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209 else:
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210 new_columns = [col for col in adata.obs.columns if col.startswith("AUCell_")]
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211 adata.obs[new_columns].to_csv(args.output_file, sep="\t", index=True)