annotate gate_finder.py @ 3:7cc457aa78b1 draft default tip

planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit af71ccb3b89c9735c6f985a3e8ffe22cd14c0e04
author goeckslab
date Thu, 30 May 2024 17:24:44 +0000
parents 9f60ef2d586e
children
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9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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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
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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4 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 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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7 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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8 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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9 from sklearn.mixture import GaussianMixture
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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10 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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11 from vitessce import (
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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12 AnnDataWrapper,
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13 Component as cm,
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14 MultiImageWrapper,
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15 OmeTiffWrapper,
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16 VitessceConfig,
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17 )
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20 # Generate binarized phenotype for a gate
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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21 def get_gate_phenotype(g, d):
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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22 dd = d.copy()
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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23 dd = np.where(dd < g, 0, dd)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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24 np.warnings.filterwarnings('ignore')
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25 dd = np.where(dd >= g, 1, dd)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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26 return dd
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29 def get_gmm_phenotype(data):
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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30 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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31 high = np.percentile(data, 99.99)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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32 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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33
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34 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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35 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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36 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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37
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38 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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39 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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40
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41 scaler = MinMaxScaler(feature_range=(0, 1))
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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42 data_norm = scaler.fit_transform(data_log)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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43
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44 gmm = GaussianMixture(n_components=2)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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45 gmm.fit(data_norm)
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46 gate = np.mean(gmm.means_)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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47
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48 return get_gate_phenotype(gate, np.ravel(data_norm))
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49
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50
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51 def main(inputs, output, image, anndata, masks=None):
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52 """
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53 Parameter
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54 ---------
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55 inputs : str
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56 File path to galaxy tool parameter.
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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57 output : str
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58 Output folder for saving web content.
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59 image : str
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60 File path to the OME Tiff image.
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61 anndata : str
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62 File path to anndata containing phenotyping info.
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63 masks : str
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64 File path to the image masks.
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65 """
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66 warnings.simplefilter('ignore')
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67
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68 with open(inputs, 'r') as param_handler:
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69 params = json.load(param_handler)
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70
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71 marker = params['marker'].strip()
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72 from_gate = params['from_gate']
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73 to_gate = params['to_gate']
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74 increment = params['increment']
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75 x_coordinate = params['x_coordinate'].strip() or 'X_centroid'
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76 y_coordinate = params['y_coordinate'].strip() or 'Y_centroid'
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77
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78 adata = read_h5ad(anndata)
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79
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80 # If no raw data is available make a copy
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81 if adata.raw is None:
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82 adata.raw = adata
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83
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84 # Copy of the raw data if it exisits
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85 if adata.raw is not None:
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86 adata.X = adata.raw.X
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87
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88 data = pd.DataFrame(
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89 adata.X,
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90 columns=adata.var.index,
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91 index=adata.obs.index
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92 )
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93 marker_values = data[[marker]].values
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94 marker_values_log = np.log1p(marker_values)
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95
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96 # Identify the list of increments
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97 gate_names = []
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98 for num in np.arange(from_gate, to_gate, increment):
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99 num = round(num, 3)
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100 key = marker + '--' + str(num)
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101 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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102 gate_names.append(key)
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103
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104 adata.obs['GMM_auto'] = get_gmm_phenotype(marker_values)
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105 gate_names.append('GMM_auto')
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106
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107 adata.obsm['XY_coordinate'] = adata.obs[[x_coordinate, y_coordinate]].values
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108
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109 vc = VitessceConfig(name=None, description=None)
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110 dataset = vc.add_dataset()
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111 image_wrappers = [OmeTiffWrapper(img_path=image, name='OMETIFF')]
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112 if masks:
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113 image_wrappers.append(
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114 OmeTiffWrapper(img_path=masks, name='MASKS', is_bitmask=True)
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115 )
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116 dataset.add_object(MultiImageWrapper(image_wrappers))
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117
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118 dataset.add_object(
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119 AnnDataWrapper(
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120 adata,
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121 spatial_centroid_obsm='XY_coordinate',
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122 cell_set_obs=gate_names,
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123 cell_set_obs_names=[obj[0].upper() + obj[1:] for obj in gate_names],
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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124 expression_matrix="X"
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125 )
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126 )
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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127 spatial = vc.add_view(dataset, cm.SPATIAL)
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128 cellsets = vc.add_view(dataset, cm.CELL_SETS)
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129 status = vc.add_view(dataset, cm.STATUS)
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130 lc = vc.add_view(dataset, cm.LAYER_CONTROLLER)
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131 genes = vc.add_view(dataset, cm.GENES)
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132 cell_set_sizes = vc.add_view(dataset, cm.CELL_SET_SIZES)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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133 cell_set_expression = vc.add_view(dataset, cm.CELL_SET_EXPRESSION)
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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134
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135 vc.layout(
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136 (status / genes / cell_set_expression)
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137 | (cellsets / cell_set_sizes / lc)
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138 | (spatial)
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139 )
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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140 config_dict = vc.export(to='files', base_url='http://localhost', out_dir=output)
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141
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142 with open(Path(output).joinpath('config.json'), 'w') as f:
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143 json.dump(config_dict, f, indent=4)
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144
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145
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146 if __name__ == '__main__':
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147 aparser = argparse.ArgumentParser()
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148 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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149 aparser.add_argument("-e", "--output", dest="output", required=True)
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150 aparser.add_argument("-g", "--image", dest="image", required=True)
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151 aparser.add_argument("-a", "--anndata", dest="anndata", required=True)
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152 aparser.add_argument("-m", "--masks", dest="masks", required=False)
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153
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154 args = aparser.parse_args()
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155
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156 main(args.inputs, args.output, args.image, args.anndata, args.masks)