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‎docs/notebooks/spatial_data_merfish.ipynb‎

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},
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/mkeller/research/dbmi/vitessce/vitessce-python/.venv/lib/python3.10/site-packages/dask/dataframe/__init__.py:31: FutureWarning: The legacy Dask DataFrame implementation is deprecated and will be removed in a future version. Set the configuration option `dataframe.query-planning` to `True` or None to enable the new Dask Dataframe implementation and silence this warning.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"from spatialdata import read_zarr\n",
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"from os.path import join"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"spatialdata_filepath = join(\"..\", \"..\", \"..\", \"tissue-map-tools\", \"examples\", \"moffitt_multiscale.sdata.zarr\")"
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"sdata_url = \"https://data-2.vitessce.io/data/moffitt/merfish_mouse_ileum.sdata.zarr\""
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"text": [
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"version mismatch: detected: RasterFormatV02, requested: FormatV04\n",
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"/Users/mkeller/research/dbmi/vitessce/vitessce-python/.venv/lib/python3.10/site-packages/zarr/creation.py:614: UserWarning: ignoring keyword argument 'read_only'\n",
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" compressor, fill_value = _kwargs_compat(compressor, fill_value, kwargs)\n",
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"/Users/mkeller/research/dbmi/vitessce/vitessce-python/.venv/lib/python3.10/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.\n",
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" warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n"
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]
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}
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],
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"source": [
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"sdata = read_zarr(spatialdata_filepath)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"data": {
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"text/plain": [
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"SpatialData object, with associated Zarr store: /Users/mkeller/research/dbmi/vitessce/tissue-map-tools/examples/moffitt_multiscale.sdata.zarr\n",
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"├── Images\n",
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"│ └── 'stains': DataTree[czyx] (2, 9, 9392, 5721), (2, 4, 4696, 2860), (2, 2, 2348, 1430)\n",
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"├── Labels\n",
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"│ ├── 'dapi_labels': DataTree[zyx] (9, 9392, 5721), (4, 4696, 2860), (2, 2348, 1430)\n",
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"│ └── 'membrane_labels': DataTree[zyx] (9, 9392, 5721), (4, 4696, 2860), (2, 2348, 1430)\n",
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"├── Points\n",
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"│ ├── 'cells_centroids_cellpose': DataFrame with shape: (<Delayed>, 3) (3D points)\n",
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"│ ├── 'molecule_baysor': DataFrame with shape: (<Delayed>, 21) (3D points)\n",
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"│ └── 'molecules': DataFrame with shape: (<Delayed>, 12) (3D points)\n",
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"├── Shapes\n",
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"│ ├── 'cells_baysor': GeoDataFrame shape: (33222, 6) (2D shapes)\n",
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"│ ├── 'cells_circles_baysor': GeoDataFrame shape: (5749, 2) (2D shapes)\n",
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"│ ├── 'cells_layer_1_baysor': GeoDataFrame shape: (3797, 3) (2D shapes)\n",
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"│ ├── 'cells_layer_2_baysor': GeoDataFrame shape: (4037, 3) (2D shapes)\n",
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"│ ├── 'cells_layer_3_baysor': GeoDataFrame shape: (4298, 3) (2D shapes)\n",
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"│ ├── 'cells_layer_4_baysor': GeoDataFrame shape: (4446, 3) (2D shapes)\n",
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"│ ├── 'cells_layer_5_baysor': GeoDataFrame shape: (4362, 3) (2D shapes)\n",
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"│ ├── 'cells_layer_6_baysor': GeoDataFrame shape: (4008, 3) (2D shapes)\n",
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"│ ├── 'cells_layer_7_baysor': GeoDataFrame shape: (3394, 3) (2D shapes)\n",
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"│ ├── 'cells_layer_8_baysor': GeoDataFrame shape: (2717, 3) (2D shapes)\n",
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"│ └── 'cells_layer_9_baysor': GeoDataFrame shape: (2163, 3) (2D shapes)\n",
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"└── Tables\n",
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" ├── 'gene_expression_baysor': AnnData (5800, 240)\n",
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" └── 'gene_expression_cellpose': AnnData (8439, 241)\n",
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"with coordinate systems:\n",
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" ▸ 'global', with elements:\n",
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" stains (Images), dapi_labels (Labels), membrane_labels (Labels), cells_centroids_cellpose (Points), molecule_baysor (Points), molecules (Points), cells_baysor (Shapes), cells_circles_baysor (Shapes), cells_layer_1_baysor (Shapes), cells_layer_2_baysor (Shapes), cells_layer_3_baysor (Shapes), cells_layer_4_baysor (Shapes), cells_layer_5_baysor (Shapes), cells_layer_6_baysor (Shapes), cells_layer_7_baysor (Shapes), cells_layer_8_baysor (Shapes), cells_layer_9_baysor (Shapes)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sdata"
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},
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>x</th>\n",
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" <th>y</th>\n",
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" <th>z</th>\n",
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" <th>molecule_id</th>\n",
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" <th>z_um</th>\n",
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" <th>area</th>\n",
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" <th>gene</th>\n",
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" <th>brightness</th>\n",
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" <th>x_um</th>\n",
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" <th>qc_score</th>\n",
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" <th>y_um</th>\n",
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" <th>total_magnitude</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" </tr>\n",
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" <tr>\n",
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" </tr>\n",
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" <td>501.4615</td>\n",
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" </tr>\n",
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" <td>2.5</td>\n",
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" <td>7</td>\n",
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" <td>Maoa</td>\n",
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" <td>1.960428</td>\n",
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" <td>-2929.339</td>\n",
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" <td>0.991316</td>\n",
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" <td>639.0364</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>794.0</td>\n",
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" <td>0.0</td>\n",
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" <td>5</td>\n",
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" <td>2.5</td>\n",
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" <td>6</td>\n",
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" <td>Maoa</td>\n",
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" <td>1.937280</td>\n",
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" <td>-2913.718</td>\n",
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" <td>0.983210</td>\n",
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" <td>-1270.474</td>\n",
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" <td>519.3154</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" x y z molecule_id z_um area gene brightness x_um \\\n",
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"0 1705.0 1271.0 0.0 1 2.5 4 Maoa 2.021306 -2935.386 \n",
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"1 1725.0 1922.0 0.0 2 2.5 4 Maoa 1.828640 -2933.229 \n",
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"2 1753.0 1863.0 0.0 3 2.5 5 Maoa 2.001268 -2930.104 \n",
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"3 1760.0 1865.0 0.0 4 2.5 7 Maoa 1.960428 -2929.339 \n",
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"4 1904.0 794.0 0.0 5 2.5 6 Maoa 1.937280 -2913.718 \n",
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"\n",
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" qc_score y_um total_magnitude \n",
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"0 0.954363 -1218.580 420.1126 \n",
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"1 0.908246 -1147.614 269.5874 \n",
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"2 0.977219 -1154.062 501.4615 \n",
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"3 0.991316 -1153.784 639.0364 \n",
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"4 0.983210 -1270.474 519.3154 "
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]
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},
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"execution_count": 5,
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"output_type": "execute_result"
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}
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],
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"source": [
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"sdata.points['molecules'].head()"
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"# Add data to the configuration:\n",
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"\n",
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"dataset = vc.add_dataset(name='MERFISH').add_object(SpatialDataWrapper(\n",
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" sdata_store=spatialdata_filepath,\n",
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" sdata_url=sdata_url,\n",
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" # The following paths are relative to the root of the SpatialData zarr store on-disk.\n",
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" image_path=\"images/stains\",\n",
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" coordinate_system=\"global\",\n",
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" coordination_values={\n",
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" \"fileUid\": \"stains\"\n",
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")).add_object(SpatialDataWrapper(\n",
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" sdata_store=spatialdata_filepath,\n",
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" sdata_url=sdata_url,\n",
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" # The following paths are relative to the root of the SpatialData zarr store on-disk.\n",
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" labels_path=\"labels/dapi_labels\",\n",
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")).add_object(SpatialDataWrapper(\n",
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" sdata_store=spatialdata_filepath,\n",
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" sdata_url=sdata_url,\n",
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" # The following paths are relative to the root of the SpatialData zarr store on-disk.\n",
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "65667908b55d48d583d8b0d44aaba152",
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"version_major": 2,
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"version_minor": 1
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},
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"text/plain": [
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"VitessceWidget(config={'version': '1.0.17', 'name': 'SpatialData with MERFISH data', 'description': '', 'datas…"
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"vw = vc.widget()\n",
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"vw"

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