annotate decoupler_pseudobulk.py @ 0:77d680b36e23 draft

planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit 1034a450c97dcbb77871050cf0c6d3da90dac823
author ebi-gxa
date Fri, 15 Mar 2024 12:17:49 +0000
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children c6787c2aee46
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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
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3 import anndata
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4 import decoupler
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5 import pandas as pd
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6
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8 def get_pseudobulk(
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9 adata,
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10 sample_col,
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11 groups_col,
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12 layer=None,
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13 mode="sum",
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14 min_cells=10,
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15 min_counts=1000,
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16 use_raw=False,
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17 ):
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18 """
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19 >>> import scanpy as sc
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20 >>> adata = sc.datasets.pbmc68k_reduced()
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21 >>> adata.X = abs(adata.X).astype(int)
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22 >>> pseudobulk = get_pseudobulk(adata, "bulk_labels", "louvain")
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23 """
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24
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25 return decoupler.get_pseudobulk(
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26 adata,
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27 sample_col=sample_col,
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28 groups_col=groups_col,
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29 layer=layer,
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30 mode=mode,
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31 use_raw=use_raw,
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32 min_cells=min_cells,
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33 min_counts=min_counts,
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34 )
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35
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36
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37 def prepend_c_to_index(index_value):
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38 if index_value and index_value[0].isdigit():
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39 return "C" + index_value
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40 return index_value
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41
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42
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43 # write results for loading into DESeq2
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44 def write_DESeq2_inputs(pdata, layer=None, output_dir="", factor_fields=None):
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45 """
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46 >>> import scanpy as sc
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47 >>> adata = sc.datasets.pbmc68k_reduced()
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48 >>> adata.X = abs(adata.X).astype(int)
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49 >>> pseudobulk = get_pseudobulk(adata, "bulk_labels", "louvain")
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50 >>> write_DESeq2_inputs(pseudobulk)
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51 """
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52 # add / to output_dir if is not empty or if it doesn't end with /
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53 if output_dir != "" and not output_dir.endswith("/"):
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54 output_dir = output_dir + "/"
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55 obs_for_deseq = pdata.obs.copy()
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56 # replace any index starting with digits to start with C instead.
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57 obs_for_deseq.rename(index=prepend_c_to_index, inplace=True)
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58 # avoid dash that is read as point on R colnames.
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59 obs_for_deseq.index = obs_for_deseq.index.str.replace("-", "_")
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60 obs_for_deseq.index = obs_for_deseq.index.str.replace(" ", "_")
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61 col_metadata_file = f"{output_dir}col_metadata.tsv"
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62 # write obs to a col_metadata file
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63 if factor_fields:
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64 # only output the index plus the columns in factor_fields in that order
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65 obs_for_deseq[factor_fields].to_csv(col_metadata_file, sep="\t", index=True)
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66 else:
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67 obs_for_deseq.to_csv(col_metadata_file, sep="\t", index=True)
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68 # write var to a gene_metadata file
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69 pdata.var.to_csv(f"{output_dir}gene_metadata.tsv", sep="\t", index=True)
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70 # write the counts matrix of a specified layer to file
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71 if layer is None:
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72 # write the X numpy matrix transposed to file
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73 df = pd.DataFrame(pdata.X.T, index=pdata.var.index, columns=obs_for_deseq.index)
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74 else:
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75 df = pd.DataFrame(
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76 pdata.layers[layer].T, index=pdata.var.index, columns=obs_for_deseq.index
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77 )
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78 df.to_csv(f"{output_dir}counts_matrix.tsv", sep="\t", index_label="")
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79
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80
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81 def plot_pseudobulk_samples(
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82 pseudobulk_data,
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83 groupby,
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84 figsize=(10, 10),
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85 save_path=None,
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86 ):
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87 """
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88 >>> import scanpy as sc
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89 >>> adata = sc.datasets.pbmc68k_reduced()
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90 >>> adata.X = abs(adata.X).astype(int)
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91 >>> pseudobulk = get_pseudobulk(adata, "bulk_labels", "louvain")
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92 >>> plot_pseudobulk_samples(pseudobulk, groupby=["bulk_labels", "louvain"], figsize=(10, 10))
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93 """
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94 fig = decoupler.plot_psbulk_samples(
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95 pseudobulk_data, groupby=groupby, figsize=figsize, return_fig=True
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96 )
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97 if save_path:
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98 fig.savefig(f"{save_path}/pseudobulk_samples.png")
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99 else:
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100 fig.show()
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101
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102
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103 def plot_filter_by_expr(
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104 pseudobulk_data, group, min_count=None, min_total_count=None, save_path=None
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105 ):
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106 """
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107 >>> import scanpy as sc
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108 >>> adata = sc.datasets.pbmc68k_reduced()
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109 >>> adata.X = abs(adata.X).astype(int)
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110 >>> pseudobulk = get_pseudobulk(adata, "bulk_labels", "louvain")
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111 >>> plot_filter_by_expr(pseudobulk, group="bulk_labels", min_count=10, min_total_count=200)
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112 """
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113 fig = decoupler.plot_filter_by_expr(
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114 pseudobulk_data,
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115 group=group,
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116 min_count=min_count,
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117 min_total_count=min_total_count,
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118 return_fig=True,
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119 )
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120 if save_path:
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121 fig.savefig(f"{save_path}/filter_by_expr.png")
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122 else:
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123 fig.show()
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124
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125
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126 def filter_by_expr(pdata, min_count=None, min_total_count=None):
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127 """
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128 >>> import scanpy as sc
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129 >>> adata = sc.datasets.pbmc68k_reduced()
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130 >>> adata.X = abs(adata.X).astype(int)
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131 >>> pseudobulk = get_pseudobulk(adata, "bulk_labels", "louvain")
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132 >>> pdata_filt = filter_by_expr(pseudobulk, min_count=10, min_total_count=200)
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133 """
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134 genes = decoupler.filter_by_expr(
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135 pdata, min_count=min_count, min_total_count=min_total_count
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136 )
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137 return pdata[:, genes].copy()
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138
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139
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140 def check_fields(fields, adata, obs=True, context=None):
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141 """
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142 >>> import scanpy as sc
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143 >>> adata = sc.datasets.pbmc68k_reduced()
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144 >>> check_fields(["bulk_labels", "louvain"], adata, obs=True)
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145 """
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146
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147 legend = ""
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148 if context:
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149 legend = f", passed in {context},"
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150 if obs:
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151 if not set(fields).issubset(set(adata.obs.columns)):
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152 raise ValueError(
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153 f"Some of the following fields {legend} are not present in adata.obs: {fields}. Possible fields are: {list(set(adata.obs.columns))}"
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154 )
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155 else:
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156 if not set(fields).issubset(set(adata.var.columns)):
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157 raise ValueError(
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158 f"Some of the following fields {legend} are not present in adata.var: {fields}. Possible fields are: {list(set(adata.var.columns))}"
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159 )
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160
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161
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162 def main(args):
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163 # Load AnnData object from file
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164 adata = anndata.read_h5ad(args.adata_file)
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165
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166 # Merge adata.obs fields specified in args.adata_obs_fields_to_merge
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167 if args.adata_obs_fields_to_merge:
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168 # first split potential groups by ":" and iterate over them
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169 for group in args.adata_obs_fields_to_merge.split(":"):
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170 fields = group.split(",")
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171 check_fields(fields, adata)
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172 adata = merge_adata_obs_fields(fields, adata)
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173
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174 check_fields([args.groupby, args.sample_key], adata)
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175
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176 factor_fields = None
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177 if args.factor_fields:
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178 factor_fields = args.factor_fields.split(",")
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179 check_fields(factor_fields, adata)
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180
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181 print(f"Using mode: {args.mode}")
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182 # Perform pseudobulk analysis
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183 pseudobulk_data = get_pseudobulk(
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184 adata,
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185 sample_col=args.sample_key,
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186 groups_col=args.groupby,
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187 layer=args.layer,
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188 mode=args.mode,
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189 use_raw=args.use_raw,
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190 min_cells=args.min_cells,
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191 min_counts=args.min_counts,
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192 )
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193
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194 # Plot pseudobulk samples
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195 plot_pseudobulk_samples(
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196 pseudobulk_data,
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197 args.groupby,
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198 save_path=args.save_path,
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199 figsize=args.plot_samples_figsize,
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200 )
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201
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202 plot_filter_by_expr(
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203 pseudobulk_data,
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204 group=args.groupby,
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205 min_count=args.min_counts,
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206 min_total_count=args.min_total_counts,
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207 save_path=args.save_path,
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208 )
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209
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210 # Filter by expression if enabled
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211 if args.filter_expr:
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212 filtered_adata = filter_by_expr(
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213 pseudobulk_data,
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214 min_count=args.min_counts,
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215 min_total_count=args.min_total_counts,
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216 )
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217
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218 pseudobulk_data = filtered_adata
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219
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220 # Save the pseudobulk data
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221 if args.anndata_output_path:
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222 pseudobulk_data.write_h5ad(args.anndata_output_path, compression="gzip")
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223
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224 write_DESeq2_inputs(
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225 pseudobulk_data, output_dir=args.deseq2_output_path, factor_fields=factor_fields
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226 )
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227
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228
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229 def merge_adata_obs_fields(obs_fields_to_merge, adata):
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230 """
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231 Merge adata.obs fields specified in args.adata_obs_fields_to_merge
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232
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233 Parameters
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234 ----------
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235 obs_fields_to_merge : str
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236 Fields in adata.obs to merge, comma separated
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237 adata : anndata.AnnData
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238 The AnnData object
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239
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240 Returns
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241 -------
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242 anndata.AnnData
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243 The merged AnnData object
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244
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245 docstring tests:
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246 >>> import scanpy as sc
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247 >>> ad = sc.datasets.pbmc68k_reduced()
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248 >>> ad = merge_adata_obs_fields(["bulk_labels","louvain"], ad)
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249 >>> ad.obs.columns
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250 Index(['bulk_labels', 'n_genes', 'percent_mito', 'n_counts', 'S_score',
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251 'G2M_score', 'phase', 'louvain', 'bulk_labels_louvain'],
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252 dtype='object')
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253 """
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254 field_name = "_".join(obs_fields_to_merge)
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255 for field in obs_fields_to_merge:
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256 if field not in adata.obs.columns:
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257 raise ValueError(f"The '{field}' column is not present in adata.obs.")
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258 if field_name not in adata.obs.columns:
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259 adata.obs[field_name] = adata.obs[field].astype(str)
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260 else:
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261 adata.obs[field_name] = (
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262 adata.obs[field_name] + "_" + adata.obs[field].astype(str)
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263 )
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264 return adata
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265
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266
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267 if __name__ == "__main__":
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268 # Create argument parser
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269 parser = argparse.ArgumentParser(
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270 description="Perform pseudobulk analysis on an AnnData object"
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271 )
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272
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273 # Add arguments
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274 parser.add_argument("adata_file", type=str, help="Path to the AnnData file")
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275 parser.add_argument(
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276 "-m",
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277 "--adata_obs_fields_to_merge",
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278 type=str,
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279 help="Fields in adata.obs to merge, comma separated. You can have more than one set of fields, separated by semi-colon ;",
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280 )
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281 parser.add_argument(
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282 "--groupby",
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283 type=str,
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284 required=True,
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285 help="The column in adata.obs that defines the groups",
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286 )
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287 parser.add_argument(
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288 "--sample_key",
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289 required=True,
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290 type=str,
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291 help="The column in adata.obs that defines the samples",
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292 )
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293 # add argument for layer
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294 parser.add_argument(
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295 "--layer",
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296 type=str,
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297 default=None,
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298 help="The name of the layer of the AnnData object to use",
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299 )
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300 # add argument for mode
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301 parser.add_argument(
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302 "--mode",
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303 type=str,
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304 default="sum",
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305 help="The mode for Decoupler pseudobulk analysis",
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306 choices=["sum", "mean", "median"],
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307 )
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308 # add boolean argument for use_raw
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309 parser.add_argument(
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310 "--use_raw",
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311 action="store_true",
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312 default=False,
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313 help="Whether to use the raw part of the AnnData object",
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314 )
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315 # add argument for min_cells
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316 parser.add_argument(
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317 "--min_cells",
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318 type=int,
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319 default=10,
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320 help="Minimum number of cells for pseudobulk analysis",
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321 )
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322 parser.add_argument(
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323 "--save_path", type=str, help="Path to save the plot (optional)"
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324 )
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325 parser.add_argument(
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326 "--min_counts",
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327 type=int,
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328 help="Minimum count threshold for filtering by expression",
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329 )
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330 parser.add_argument(
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331 "--min_total_counts",
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332 type=int,
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333 help="Minimum total count threshold for filtering by expression",
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334 )
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335 parser.add_argument(
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336 "--anndata_output_path",
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337 type=str,
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338 help="Path to save the filtered AnnData object or pseudobulk data",
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339 )
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340 parser.add_argument(
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341 "--filter_expr", action="store_true", help="Enable filtering by expression"
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342 )
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343 parser.add_argument(
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344 "--factor_fields",
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345 type=str,
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346 help="Comma separated list of fields for the factors",
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347 )
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348 parser.add_argument(
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349 "--deseq2_output_path",
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350 type=str,
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351 help="Path to save the DESeq2 inputs",
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352 required=True,
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353 )
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354 parser.add_argument(
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355 "--plot_samples_figsize",
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356 type=int,
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357 default=[10, 10],
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358 nargs=2,
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359 help="Size of the samples plot as a tuple (two arguments)",
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360 )
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361 parser.add_argument("--plot_filtering_figsize", type=int, default=[10, 10], nargs=2)
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362
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363 # Parse the command line arguments
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364 args = parser.parse_args()
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365
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366 # Call the main function
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367 main(args)