Mercurial > repos > goeckslab > vitessce_spatial

annotate vitessce_spatial.py @ 4:068da7f7cd83 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 19:47:16 +0000
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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
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3 import warnings
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068da7f7cd83 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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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 scanpy as sc
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 vitessce import (
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10 AnnDataWrapper,
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11 Component as cm,
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12 MultiImageWrapper,
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13 OmeTiffWrapper,
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14 VitessceConfig,
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15 )
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16 from vitessce.data_utils import (
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17 optimize_adata,
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18 VAR_CHUNK_SIZE,
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19 )
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21
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22 def main(inputs, output, image, offsets=None, anndata=None, masks=None):
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23 """
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24 Parameter
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25 ---------
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26 inputs : str
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27 File path to galaxy inputs config file.
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28 output : str
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29 Output folder for saving web content.
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30 image : str
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31 File path to the OME Tiff image.
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32 anndata : str
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33 File path to anndata containing phenotyping info.
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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34 masks : str
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35 File path to the image masks.
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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36 """
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37 warnings.simplefilter('ignore')
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38
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39 with open(inputs, 'r') as param_handler:
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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40 params = json.load(param_handler)
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41
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42 # initialize vitessce config and add OME-TIFF image, and masks if specified
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43 vc = VitessceConfig(schema_version="1.0.17", name=None, description=None)
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44 dataset = vc.add_dataset()
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45
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46 # FIXME: grab offsets file for faster display. NEED TO TEST
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47 image_wrappers = [OmeTiffWrapper(img_path=image, offsets_path=offsets, name='OMETIFF')]
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48 if masks:
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49 image_wrappers.append(
9f60ef2d586e planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
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50 OmeTiffWrapper(img_path=masks, name='MASKS', is_bitmask=True)
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51 )
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52 dataset.add_object(MultiImageWrapper(image_wrappers))
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53
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54 # set relative view sizes (w,h), full window dims are 12x12
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55 # if no anndata file, image and layer view can take up whole window
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56 if not anndata:
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57 spatial_dims = (9, 12)
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58 lc_dims = (3, 12)
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59 else:
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60 spatial_dims = (6, 6)
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61 lc_dims = (3, 6)
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62
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63 # add views for the images, and the layer/channels selector
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64 spatial = vc.add_view(
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65 view_type=cm.SPATIAL,
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66 dataset=dataset,
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67 w=spatial_dims[0],
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68 h=spatial_dims[1])
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69
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70 lc = vc.add_view(
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71 view_type=cm.LAYER_CONTROLLER,
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72 dataset=dataset,
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73 w=lc_dims[0],
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74 h=lc_dims[1])
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75
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76 # if no anndata file, export the config with these minimal components
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77 if not anndata:
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78 vc.layout(lc | spatial)
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79 config_dict = vc.export(
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80 to='files',
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81 base_url='http://localhost',
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82 out_dir=output)
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83 with open(Path(output).joinpath('config.json'), 'w') as f:
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84 json.dump(config_dict, f, indent=4)
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85 return
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86
4
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87 # read anndata file, compute embeddings
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88 adata = read_h5ad(anndata)
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89
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90 params = params['do_phenotyping']
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91 embedding = params['scatterplot_embeddings']['embedding']
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92 embedding_options = params['scatterplot_embeddings']['options']
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93 if embedding == 'umap':
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94 sc.pp.neighbors(adata, **embedding_options)
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95 sc.tl.umap(adata)
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96 mappings_obsm = 'X_umap'
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97 mappings_obsm_name = "UMAP"
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98 elif embedding == 'tsne':
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99 sc.tl.tsne(adata, **embedding_options)
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100 mappings_obsm = 'X_tsne'
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101 mappings_obsm_name = "tSNE"
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102 else:
0
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103 sc.tl.pca(adata, **embedding_options)
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104 mappings_obsm = 'X_pca'
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105 mappings_obsm_name = "PCA"
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106
4
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107 # Add spatial coords to obsm, although uncertain if this is needed
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108 # FIXME: provide options for alternative coordinate colnames
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109 adata.obsm['spatial'] = adata.obs[['X_centroid', 'Y_centroid']].values
0
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110
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111 # parse list of obs columns to use as cell type labels
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112 cell_set_obs = params['phenotype_factory']['phenotypes']
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113 if not isinstance(cell_set_obs, list):
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114 cell_set_obs = [x.strip() for x in cell_set_obs.split(',')]
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115
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116 # write anndata out as zarr hierarchy
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117 zarr_filepath = join("data", "adata.zarr")
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118 if not isdir(zarr_filepath):
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119 adata = optimize_adata(
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120 adata,
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121 obs_cols=cell_set_obs,
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122 obsm_keys=[mappings_obsm, 'spatial'],
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123 optimize_X=True
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124 )
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125 adata.write_zarr(
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126 zarr_filepath,
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127 chunks=[adata.shape[0], VAR_CHUNK_SIZE]
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128 )
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129
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130 # create a nicer label for the cell types to be displayed on the dashboard
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131 cell_set_obs_names = [obj[0].upper() + obj[1:] for obj in cell_set_obs]
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132
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133 # add anndata zarr to vitessce config
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134 dataset.add_object(
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135 AnnDataWrapper(
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136 adata_path=zarr_filepath,
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137 obs_feature_matrix_path="X", # FIXME: provide rep options
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138 obs_set_paths=['obs/' + x for x in cell_set_obs],
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139 obs_set_names=cell_set_obs_names,
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140 obs_locations_path='spatial',
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141 obs_embedding_paths=['obsm/' + mappings_obsm],
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142 obs_embedding_names=[mappings_obsm_name]
0
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143 )
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144 )
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145
4
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146 # add views
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147 cellsets = vc.add_view(
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148 view_type=cm.OBS_SETS,
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149 dataset=dataset,
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150 w=3,
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151 h=3)
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152
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153 scatterplot = vc.add_view(
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154 view_type=cm.SCATTERPLOT,
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155 dataset=dataset,
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156 mapping=mappings_obsm_name,
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157 w=3,
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158 h=6)
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159
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160 heatmap = vc.add_view(
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161 view_type=cm.HEATMAP,
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162 dataset=dataset,
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163 w=3,
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164 h=3)
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165
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166 genes = vc.add_view(
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167 view_type=cm.FEATURE_LIST,
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168 dataset=dataset,
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169 w=3,
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170 h=3)
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171
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172 cell_set_sizes = vc.add_view(
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173 view_type=cm.OBS_SET_SIZES,
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174 dataset=dataset,
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175 w=3,
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176 h=3)
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177
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178 cell_set_expression = vc.add_view(
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179 view_type=cm.OBS_SET_FEATURE_VALUE_DISTRIBUTION,
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180 dataset=dataset,
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181 w=3,
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182 h=6)
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183
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184 # define the dashboard layout
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185 vc.layout(
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186 (cellsets / genes / cell_set_expression)
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187 | (lc / scatterplot)
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188 | (cell_set_sizes / heatmap / spatial)
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189 )
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190
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191 # export the config file
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192 config_dict = vc.export(
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193 to='files',
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194 base_url='http://localhost',
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195 out_dir=output)
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196
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197 with open(Path(output).joinpath('config.json'), 'w') as f:
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198 json.dump(config_dict, f, indent=4)
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199
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200
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201 if __name__ == '__main__':
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202 aparser = argparse.ArgumentParser()
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203 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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204 aparser.add_argument("-e", "--output", dest="output", required=True)
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205 aparser.add_argument("-g", "--image", dest="image", required=True)
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206 aparser.add_argument("-f", "--offsets", dest="offsets", required=False)
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207 aparser.add_argument("-a", "--anndata", dest="anndata", required=False)
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208 aparser.add_argument("-m", "--masks", dest="masks", required=False)
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209
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210 args = aparser.parse_args()
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211
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212 main(args.inputs, args.output, args.image, args.offsets, args.anndata, args.masks)