Mercurial > repos > goeckslab > vitessce_spatial

annotate gate_finder.py @ 7:cab38f254824 draft default tip

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planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 52576a03742abfab8a623c06e22624f1d8423e3d
author goeckslab
date Mon, 21 Jul 2025 16:47:46 +0000
parents 97be83fc3677
children
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9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
goeckslab
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1 import argparse
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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2 import json
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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3 import warnings
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4 from os.path import isdir, join
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5 from pathlib import Path
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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6
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7 import numpy as np
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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8 import pandas as pd
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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9 from anndata import read_h5ad
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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10 from sklearn.mixture import GaussianMixture
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11 from sklearn.preprocessing import MinMaxScaler
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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12 from vitessce import (
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13 AnnDataWrapper,
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14 Component as cm,
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15 MultiImageWrapper,
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16 OmeTiffWrapper,
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17 VitessceConfig,
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18 )
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19 from vitessce.data_utils import (
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20 optimize_adata,
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21 VAR_CHUNK_SIZE,
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22 )
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25 # Generate binarized phenotype for a gate
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26 def get_gate_phenotype(g, d):
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27 dd = d.copy()
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28 dd = np.where(dd < g, 0, dd)
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29 warnings.filterwarnings('ignore')
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30 dd = np.where(dd >= g, 1, dd)
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31 return dd
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34 def get_gmm_phenotype(data):
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35 low = np.percentile(data, 0.01)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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36 high = np.percentile(data, 99.99)
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37 data = np.clip(data, low, high)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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39 sum = np.sum(data)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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40 median = np.median(data)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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41 data_med = data / sum * median
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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43 data_log = np.log1p(data_med)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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44 data_log = data_log.reshape(-1, 1)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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46 scaler = MinMaxScaler(feature_range=(0, 1))
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47 data_norm = scaler.fit_transform(data_log)
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49 gmm = GaussianMixture(n_components=2)
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50 gmm.fit(data_norm)
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51 gate = np.mean(gmm.means_)
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53 return get_gate_phenotype(gate, np.ravel(data_norm))
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55
5
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56 def main(inputs, output, image, anndata, offsets=None, masks=None, config_path=None):
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57 """
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58 Parameter
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59 ---------
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60 inputs : str
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61 File path to galaxy tool parameter.
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62 output : str
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63 Output folder for saving web content.
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64 image : str
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65 File path to the OME Tiff image.
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66 anndata : str
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67 File path to anndata containing phenotyping info.
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68 masks : str
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69 File path to the image masks.
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70 config_path : str
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71 File path to the config containing galaxy_url and dataset_id.
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72 """
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73 warnings.simplefilter('ignore')
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74
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75 with open(inputs, 'r') as param_handler:
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76 params = json.load(param_handler)
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77
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78 with open(config_path) as conf_fh:
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79 config = json.load(conf_fh)
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80
0
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81 marker = params['marker'].strip()
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82 from_gate = params['from_gate']
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83 to_gate = params['to_gate']
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84 increment = params['increment']
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85 x_coordinate = params['x_coordinate'].strip() or 'X_centroid'
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86 y_coordinate = params['y_coordinate'].strip() or 'Y_centroid'
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87
5
97be83fc3677 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 90f081e58b5ee41bba3a7e6f01d97b5bd9392759
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88 galaxy_url = config["galaxy_url"]
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89 dataset_id = config["dataset_id"]
97be83fc3677 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 90f081e58b5ee41bba3a7e6f01d97b5bd9392759
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90
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91 # Build the prefix that Vitessce should use
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92 display_prefix = (f"{galaxy_url}/api/datasets/{dataset_id}/display?filename=")
97be83fc3677 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 90f081e58b5ee41bba3a7e6f01d97b5bd9392759
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93
0
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94 adata = read_h5ad(anndata)
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95
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96 # If no raw data is available make a copy
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97 if adata.raw is None:
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98 adata.raw = adata
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99
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100 # Copy of the raw data if it exisits
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101 if adata.raw is not None:
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102 adata.X = adata.raw.X
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103
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104 data = pd.DataFrame(
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105 adata.X,
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106 columns=adata.var.index,
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107 index=adata.obs.index
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108 )
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109 marker_values = data[[marker]].values
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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110 marker_values_log = np.log1p(marker_values)
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111
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112 # Identify the list of increments
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113 gate_names = []
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114 for num in np.arange(from_gate, to_gate, increment):
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115 num = round(num, 3)
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116 key = marker + '--' + str(num)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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117 adata.obs[key] = get_gate_phenotype(num, marker_values_log)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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118 gate_names.append(key)
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119
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120 adata.obs['GMM_auto'] = get_gmm_phenotype(marker_values)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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121 gate_names.append('GMM_auto')
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122
4
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123 adata.obsm['spatial'] = adata.obs[[x_coordinate, y_coordinate]].values
0
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124
4
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125 # initialize vitessce config and add OME-TIFF image
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126 vc = VitessceConfig(schema_version="1.0.17", name=None, description=None)
0
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127 dataset = vc.add_dataset()
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128 image_wrappers = [OmeTiffWrapper(img_path=image, offsets_path=offsets, name='OMETIFF')]
0
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129 if masks:
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130 image_wrappers.append(
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131 OmeTiffWrapper(img_path=masks, name='MASKS', is_bitmask=True)
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132 )
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133 dataset.add_object(MultiImageWrapper(image_wrappers))
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134
4
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135 # write anndata out as zarr hierarchy
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136 zarr_filepath = join("data", "adata.zarr")
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137 if not isdir(zarr_filepath):
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138 adata = optimize_adata(
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139 adata,
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140 obs_cols=gate_names,
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141 obsm_keys=['spatial'],
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142 optimize_X=True
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143 )
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144 adata.write_zarr(
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145 zarr_filepath,
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146 chunks=[adata.shape[0], VAR_CHUNK_SIZE]
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147 )
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148
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149 # add anndata zarr to vitessce config
0
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150 dataset.add_object(
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151 AnnDataWrapper(
4
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152 adata_path=zarr_filepath,
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153 obs_feature_matrix_path="X", # FIXME: provide rep options
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154 obs_set_paths=['obs/' + x for x in gate_names],
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155 obs_set_names=gate_names,
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156 obs_locations_path='spatial'
0
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157 )
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158 )
4
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159
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160 # add views
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161 spatial = vc.add_view(
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162 view_type=cm.SPATIAL,
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163 dataset=dataset,
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164 w=6,
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165 h=12)
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166
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167 cellsets = vc.add_view(
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168 view_type=cm.OBS_SETS,
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169 dataset=dataset,
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170 w=3,
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171 h=6)
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172
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173 lc = vc.add_view(
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174 view_type=cm.LAYER_CONTROLLER,
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175 dataset=dataset,
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176 w=3,
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177 h=9)
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178
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179 genes = vc.add_view(
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180 view_type=cm.FEATURE_LIST,
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181 dataset=dataset,
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182 w=3,
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183 h=3)
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184
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185 cell_set_sizes = vc.add_view(
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186 view_type=cm.OBS_SET_SIZES,
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187 dataset=dataset,
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188 w=3,
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189 h=3)
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190
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191 cell_set_expression = vc.add_view(
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192 view_type=cm.OBS_SET_FEATURE_VALUE_DISTRIBUTION,
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193 dataset=dataset,
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194 w=3,
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195 h=3)
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196
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197 # define the dashboard layout
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198 vc.layout(
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199 (cellsets / genes / cell_set_expression)
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200 | (cell_set_sizes / lc)
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201 | (spatial)
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202 )
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203
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204 # export config file
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205 config_dict = vc.export(to='files', base_url=display_prefix, out_dir=output)
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206
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207 with open(Path(output).joinpath('config.json'), 'w') as f:
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208 json.dump(config_dict, f, indent=4)
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209
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210
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211 if __name__ == '__main__':
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212 aparser = argparse.ArgumentParser()
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213 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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214 aparser.add_argument("-e", "--output", dest="output", required=True)
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215 aparser.add_argument("-g", "--image", dest="image", required=True)
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216 aparser.add_argument("-a", "--anndata", dest="anndata", required=True)
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217 aparser.add_argument("-f", "--offsets", dest="offsets", required=False)
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218 aparser.add_argument("-m", "--masks", dest="masks", required=False)
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219 aparser.add_argument("--galaxy_config", dest="config_path", required=True)
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220
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221 args = aparser.parse_args()
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222
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223 main(args.inputs, args.output, args.image, args.anndata, args.offsets, args.masks, args.config_path)