Mercurial > repos > ebi-gxa > decoupler_pathway_inference

annotate decoupler_aucell_score.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 82b7cd3e1bbd
children
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1 import argparse
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3 import anndata
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4 import decoupler as dc
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5 import numba as nb
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6 import pandas as pd
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8
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9 def read_gmt_long(gmt_file):
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10 r"""
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11 Reads a GMT file and produce a Pandas DataFrame in long format, ready to
c6787c2aee46 planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit eea5c13f9e6e070a2359c59400773b01f9cd7567
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12 be passed to the AUCell method.
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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 to gene
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23 assignment, and the columns are "gene_set_name" and "genes".
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24 >>> import os
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25 >>> import tempfile
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26 >>> line = "HALLMARK_NOTCH_SIGNALING\
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27 ... \thttp://www.gsea-msigdb.org/\
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28 ... gsea/msigdb/human/geneset/HALLMARK_NOTCH_SIGNALING\
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29 ... \tJAG1\tNOTCH3\tNOTCH2\tAPH1A\tHES1\tCCND1\
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30 ... \tFZD1\tPSEN2\tFZD7\tDTX1\tDLL1\tFZD5\tMAML2\
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31 ... \tNOTCH1\tPSENEN\tWNT5A\tCUL1\tWNT2\tDTX4\
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32 ... \tSAP30\tPPARD\tKAT2A\tHEYL\tSKP1\tRBX1\tTCF7L2\
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33 ... \tARRB1\tLFNG\tPRKCA\tDTX2\tST3GAL6\tFBXW11\n"
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34 >>> line2 = "HALLMARK_APICAL_SURFACE\
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35 ... \thttp://www.gsea-msigdb.org/\
c6787c2aee46 planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit eea5c13f9e6e070a2359c59400773b01f9cd7567
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36 ... gsea/msigdb/human/geneset/HALLMARK_APICAL_SURFACE\
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37 ... \tB4GALT1\tRHCG\tMAL\tLYPD3\tPKHD1\tATP6V0A4\
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38 ... \tCRYBG1\tSHROOM2\tSRPX\tMDGA1\tTMEM8B\tTHY1\
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39 ... \tPCSK9\tEPHB4\tDCBLD2\tGHRL\tLYN\tGAS1\tFLOT2\
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40 ... \tPLAUR\tAKAP7\tATP8B1\tEFNA5\tSLC34A3\tAPP\
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41 ... \tGSTM3\tHSPB1\tSLC2A4\tIL2RB\tRTN4RL1\tNCOA6\
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42 ... \tSULF2\tADAM10\tBRCA1\tGATA3\tAFAP1L2\tIL2RG\
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43 ... \tCD160\tADIPOR2\tSLC22A12\tNTNG1\tSCUBE1\tCX3CL1\
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44 ... \tCROCC\n"
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45 >>> temp_dir = tempfile.gettempdir()
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46 >>> temp_gmt = os.path.join(temp_dir, "temp_file.gmt")
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47 >>> with open(temp_gmt, "w") as f:
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48 ... f.write(line)
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49 ... f.write(line2)
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50 288
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51 380
2
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52 >>> df = read_gmt_long(temp_gmt)
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53 >>> df.shape[0]
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54 76
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55 >>> len(df.loc[df["gene_set"] == "HALLMARK_APICAL_SURFACE"].gene.tolist())
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56 44
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57 """
3
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58 # Create a list of dictionaries, where each dictionary represents a
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59 # gene set
2
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60 gene_sets = {}
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61
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62 # Read the GMT file into a list of lines
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63 with open(gmt_file, "r") as f:
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64 while True:
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65 line = f.readline()
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66 if not line:
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67 break
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68 fields = line.strip().split("\t")
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69 gene_sets[fields[0]] = fields[2:]
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70
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71 return pd.concat(
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72 pd.DataFrame({"gene_set": k, "gene": v}) for k, v in gene_sets.items()
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73 )
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74
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75
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76 def score_genes_aucell_mt(
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77 adata: anndata.AnnData,
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78 gene_set_gene: pd.DataFrame,
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79 use_raw=False,
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80 min_n_genes=5,
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81 var_gene_symbols_field=None,
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82 ):
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83 """Score genes using Aucell.
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84
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85 Parameters
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86 ----------
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87 adata : anndata.AnnData
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88 gene_set_gene: pd.DataFrame with columns gene_set and gene
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89 use_raw : bool, optional, False by default.
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90 min_n_genes : int, optional, 5 by default.
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91 var_gene_symbols_field : str, optional, None by default. The field in var
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92 where gene symbols are stored
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93
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94 >>> import scanpy as sc
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95 >>> import decoupler as dc
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96 >>> adata = sc.datasets.pbmc68k_reduced()
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97 >>> r_gene_list = adata.var[
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98 ... adata.var.index.str.startswith("RP")].index.tolist()
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99 >>> m_gene_list = adata.var[
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100 ... adata.var.index.str.startswith("M")].index.tolist()
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101 >>> gene_set = {}
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102 >>> gene_set["m"] = m_gene_list
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103 >>> gene_set["r"] = r_gene_list
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104 >>> gene_set_df = pd.concat(
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105 ... pd.DataFrame(
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106 ... {'gene_set':k, 'gene':v}
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107 ... ) for k, v in gene_set.items())
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108 >>> score_genes_aucell_mt(adata, gene_set_df, use_raw=False)
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109 >>> "AUCell_m" in adata.obs.columns
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110 True
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111 >>> "AUCell_r" in adata.obs.columns
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112 True
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113 """
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114
3
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115 # if var_gene_symbols_fiels is provided, transform gene_set_gene df so
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116 # that gene contains gene ids instead of gene symbols
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117 if var_gene_symbols_field:
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118 # merge the index of var to gene_set_gene df based on
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119 # var_gene_symbols_field
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120 var_id_symbols = adata.var[[var_gene_symbols_field]]
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121 var_id_symbols["gene_id"] = var_id_symbols.index
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122
3
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123 gene_set_gene = gene_set_gene.merge(
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124 var_id_symbols,
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125 left_on="gene",
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126 right_on=var_gene_symbols_field,
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127 how="left",
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128 )
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129 # this will still produce some empty gene_ids (genes in the
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130 # gene_set_gene df that are not in the var df), fill those
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131 # with the original gene symbol from the gene_set to avoid
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132 # deforming the AUCell calculation
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133 gene_set_gene["gene_id"] = gene_set_gene["gene_id"].fillna(
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134 gene_set_gene["gene"]
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135 )
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136 gene_set_gene["gene"] = gene_set_gene["gene_id"]
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137
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138 # run decoupler's run_aucell
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139 dc.run_aucell(
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140 adata,
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141 net=gene_set_gene,
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142 source="gene_set",
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143 target="gene",
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144 use_raw=use_raw,
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145 min_n=min_n_genes,
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146 )
2
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147 for gs in gene_set_gene.gene_set.unique():
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148 if gs in adata.obsm["aucell_estimate"].keys():
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149 adata.obs[f"AUCell_{gs}"] = adata.obsm["aucell_estimate"][gs]
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150
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151
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152 def run_for_genelists(
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153 adata,
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154 gene_lists,
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155 score_names,
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156 use_raw=False,
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157 gene_symbols_field=None,
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158 min_n_genes=5,
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159 ):
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160 if len(gene_lists) == len(score_names):
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161 for gene_list, score_names in zip(gene_lists, score_names):
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162 genes = gene_list.split(",")
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163 gene_sets = {}
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164 gene_sets[score_names] = genes
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165 gene_set_gene_df = pd.concat(
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166 pd.DataFrame({"gene_set": k, "gene": v})
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167 for k, v in gene_sets.items()
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168 )
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169
2
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170 score_genes_aucell_mt(
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171 adata,
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172 gene_set_gene_df,
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173 use_raw,
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174 min_n_genes,
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175 var_gene_symbols_field=gene_symbols_field,
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176 )
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177 else:
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178 raise ValueError(
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179 "The number of gene lists (separated by :) and score names \
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180 (separated by :) must be the same"
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181 )
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182
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183
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184 if __name__ == "__main__":
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185 # Create command-line arguments parser
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186 parser = argparse.ArgumentParser(description="Score genes using Aucell")
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187 parser.add_argument(
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188 "--input_file",
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189 type=str,
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190 help="Path to input AnnData file",
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191 required=True,
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192 )
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193 parser.add_argument(
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194 "--output_file", type=str, help="Path to output file", required=True
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195 )
3
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196 parser.add_argument(
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197 "--gmt_file", type=str, help="Path to GMT file", required=False
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198 )
0
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199 # add argument for gene sets to score
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200 parser.add_argument(
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201 "--gene_sets_to_score",
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202 type=str,
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203 required=False,
3
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204 help="Optional comma separated list of gene sets to score \
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205 (the need to be in the gmt file)",
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206 )
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207 # add argument for gene list (comma separated) to score
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208 parser.add_argument(
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209 "--gene_lists_to_score",
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210 type=str,
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211 required=False,
3
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212 help="Comma separated list of genes to score. You can have more \
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213 than one set of genes, separated by colon :",
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214 )
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215 # argument for the score name when using the gene list
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216 parser.add_argument(
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217 "--score_names",
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218 type=str,
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219 required=False,
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220 help="Name of the score column when using the gene list. You can \
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221 have more than one set of score names, separated by colon :. \
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222 It should be the same length as the number of gene lists.",
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223 )
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224 parser.add_argument(
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225 "--gene_symbols_field",
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226 type=str,
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227 help="Name of the gene symbols field in the AnnData object",
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228 required=True,
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229 )
3
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230 # argument for min_n Minimum of targets per source. If less, sources
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231 # are removed.
1
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232 parser.add_argument(
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233 "--min_n",
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234 type=int,
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235 required=False,
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236 default=5,
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237 help="Minimum of targets per source. If less, sources are removed.",
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238 )
0
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239 parser.add_argument("--use_raw", action="store_true", help="Use raw data")
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240 parser.add_argument(
3
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241 "--write_anndata",
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242 action="store_true",
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243 help="Write the modified AnnData object",
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244 )
2
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245 # argument for number of max concurrent processes
3
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246 parser.add_argument(
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247 "--max_threads",
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248 type=int,
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249 required=False,
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250 default=1,
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251 help="Number of max concurrent threads",
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252 )
0
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253
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254 # Parse command-line arguments
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255 args = parser.parse_args()
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256
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257 nb.set_num_threads(n=args.max_threads)
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258
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259 # Load input AnnData object
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260 adata = anndata.read_h5ad(args.input_file)
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261
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262 if args.gmt_file is not None:
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263 # Load MSigDB file in GMT format
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264 # msigdb = read_gmt(args.gmt_file)
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265 msigdb = read_gmt_long(args.gmt_file)
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266
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267 gene_sets_to_score = (
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268 args.gene_sets_to_score.split(",")
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269 if args.gene_sets_to_score
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270 else []
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271 )
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272 if gene_sets_to_score:
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273 # we limit the GMT file read to the genesets specified in the
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274 # gene_sets_to_score argument
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275 msigdb = msigdb[msigdb["gene_set"].isin(gene_sets_to_score)]
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276
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277 score_genes_aucell_mt(
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278 adata,
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279 msigdb,
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280 args.use_raw,
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281 args.min_n,
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282 var_gene_symbols_field=args.gene_symbols_field,
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283 )
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284 elif args.gene_lists_to_score is not None and args.score_names is not None:
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285 gene_lists = args.gene_lists_to_score.split(":")
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286 score_names = args.score_names.split(",")
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287 run_for_genelists(
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288 adata,
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289 gene_lists,
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290 score_names,
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291 args.use_raw,
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292 args.gene_symbols_field,
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293 args.min_n,
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294 )
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295
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296 # Save the modified AnnData object or generate a file with cells as rows
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297 # and the new score_names columns
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298 if args.write_anndata:
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299 adata.write_h5ad(args.output_file)
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300 else:
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301 new_columns = [
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302 col for col in adata.obs.columns if col.startswith("AUCell_")
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303 ]
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304 adata.obs[new_columns].to_csv(args.output_file, sep="\t", index=True)