Mercurial > repos > goeckslab > gate_finder

annotate gate_finder.py @ 4:5f4a4dd06bc9 draft default tip

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planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit bc4c0bb6784a55399241f99a29b176541a164a18
author goeckslab
date Thu, 20 Feb 2025 19:47:28 +0000
parents 6df8d6e42152
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6df8d6e42152 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
goeckslab
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1 import argparse
6df8d6e42152 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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2 import json
6df8d6e42152 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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5f4a4dd06bc9 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit bc4c0bb6784a55399241f99a29b176541a164a18
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4 from os.path import isdir, join
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6df8d6e42152 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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5 from pathlib import Path
6df8d6e42152 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
6df8d6e42152 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
6df8d6e42152 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
6df8d6e42152 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
6df8d6e42152 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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11 from sklearn.preprocessing import MinMaxScaler
6df8d6e42152 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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12 from vitessce import (
6df8d6e42152 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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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()
6df8d6e42152 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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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)
6df8d6e42152 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)
6df8d6e42152 planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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37 data = np.clip(data, low, high)
6df8d6e42152 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)
6df8d6e42152 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)
6df8d6e42152 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
6df8d6e42152 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)
6df8d6e42152 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)
6df8d6e42152 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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48
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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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52
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53 return get_gate_phenotype(gate, np.ravel(data_norm))
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54
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55
4
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56 def main(inputs, output, image, anndata, offsets=None, masks=None):
0
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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 """
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71 warnings.simplefilter('ignore')
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72
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73 with open(inputs, 'r') as param_handler:
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74 params = json.load(param_handler)
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75
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76 marker = params['marker'].strip()
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77 from_gate = params['from_gate']
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78 to_gate = params['to_gate']
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79 increment = params['increment']
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80 x_coordinate = params['x_coordinate'].strip() or 'X_centroid'
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81 y_coordinate = params['y_coordinate'].strip() or 'Y_centroid'
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82
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83 adata = read_h5ad(anndata)
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84
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85 # If no raw data is available make a copy
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86 if adata.raw is None:
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87 adata.raw = adata
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88
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89 # Copy of the raw data if it exisits
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90 if adata.raw is not None:
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91 adata.X = adata.raw.X
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92
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93 data = pd.DataFrame(
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94 adata.X,
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95 columns=adata.var.index,
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96 index=adata.obs.index
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97 )
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98 marker_values = data[[marker]].values
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99 marker_values_log = np.log1p(marker_values)
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100
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101 # Identify the list of increments
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102 gate_names = []
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103 for num in np.arange(from_gate, to_gate, increment):
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104 num = round(num, 3)
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105 key = marker + '--' + str(num)
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106 adata.obs[key] = get_gate_phenotype(num, marker_values_log)
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107 gate_names.append(key)
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108
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109 adata.obs['GMM_auto'] = get_gmm_phenotype(marker_values)
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110 gate_names.append('GMM_auto')
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111
4
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112 adata.obsm['spatial'] = adata.obs[[x_coordinate, y_coordinate]].values
0
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113
4
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114 # initialize vitessce config and add OME-TIFF image
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115 vc = VitessceConfig(schema_version="1.0.17", name=None, description=None)
0
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116 dataset = vc.add_dataset()
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117 image_wrappers = [OmeTiffWrapper(img_path=image, offsets_path=offsets, name='OMETIFF')]
0
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118 if masks:
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119 image_wrappers.append(
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120 OmeTiffWrapper(img_path=masks, name='MASKS', is_bitmask=True)
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121 )
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122 dataset.add_object(MultiImageWrapper(image_wrappers))
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123
4
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124 # write anndata out as zarr hierarchy
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125 zarr_filepath = join("data", "adata.zarr")
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126 if not isdir(zarr_filepath):
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127 adata = optimize_adata(
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128 adata,
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129 obs_cols=gate_names,
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130 obsm_keys=['spatial'],
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131 optimize_X=True
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132 )
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133 adata.write_zarr(
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134 zarr_filepath,
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135 chunks=[adata.shape[0], VAR_CHUNK_SIZE]
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136 )
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137
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138 # add anndata zarr to vitessce config
0
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139 dataset.add_object(
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140 AnnDataWrapper(
4
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141 adata_path=zarr_filepath,
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142 obs_feature_matrix_path="X", # FIXME: provide rep options
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143 obs_set_paths=['obs/' + x for x in gate_names],
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144 obs_set_names=gate_names,
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145 obs_locations_path='spatial'
0
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146 )
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147 )
4
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148
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149 # add views
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150 spatial = vc.add_view(
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151 view_type=cm.SPATIAL,
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152 dataset=dataset,
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153 w=6,
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154 h=12)
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155
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156 cellsets = vc.add_view(
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157 view_type=cm.OBS_SETS,
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158 dataset=dataset,
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159 w=3,
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160 h=6)
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161
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162 lc = vc.add_view(
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163 view_type=cm.LAYER_CONTROLLER,
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164 dataset=dataset,
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165 w=3,
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166 h=9)
0
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167
4
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168 genes = vc.add_view(
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169 view_type=cm.FEATURE_LIST,
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170 dataset=dataset,
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171 w=3,
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172 h=3)
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173
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174 cell_set_sizes = vc.add_view(
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175 view_type=cm.OBS_SET_SIZES,
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176 dataset=dataset,
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177 w=3,
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178 h=3)
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179
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180 cell_set_expression = vc.add_view(
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181 view_type=cm.OBS_SET_FEATURE_VALUE_DISTRIBUTION,
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182 dataset=dataset,
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183 w=3,
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184 h=3)
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185
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186 # define the dashboard layout
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187 vc.layout(
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188 (cellsets / genes / cell_set_expression)
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189 | (cell_set_sizes / lc)
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190 | (spatial)
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191 )
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192
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193 # export config file
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194 config_dict = vc.export(to='files', base_url='http://localhost', out_dir=output)
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195
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196 with open(Path(output).joinpath('config.json'), 'w') as f:
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197 json.dump(config_dict, f, indent=4)
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198
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199
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200 if __name__ == '__main__':
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201 aparser = argparse.ArgumentParser()
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202 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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203 aparser.add_argument("-e", "--output", dest="output", required=True)
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204 aparser.add_argument("-g", "--image", dest="image", required=True)
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205 aparser.add_argument("-a", "--anndata", dest="anndata", required=True)
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206 aparser.add_argument("-f", "--offsets", dest="offsets", required=False)
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207 aparser.add_argument("-m", "--masks", dest="masks", required=False)
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208
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209 args = aparser.parse_args()
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210
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211 main(args.inputs, args.output, args.image, args.anndata, args.offsets, args.masks)