diff gate_finder.py @ 0:6df8d6e42152 draft

planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/gate_finder.py	Thu Sep 08 17:22:53 2022 +0000
@@ -0,0 +1,156 @@
+import argparse
+import json
+import warnings
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from anndata import read_h5ad
+from sklearn.mixture import GaussianMixture
+from sklearn.preprocessing import MinMaxScaler
+from vitessce import (
+    AnnDataWrapper,
+    Component as cm,
+    MultiImageWrapper,
+    OmeTiffWrapper,
+    VitessceConfig,
+)
+
+
+# Generate binarized phenotype for a gate
+def get_gate_phenotype(g, d):
+    dd = d.copy()
+    dd = np.where(dd < g, 0, dd)
+    np.warnings.filterwarnings('ignore')
+    dd = np.where(dd >= g, 1, dd)
+    return dd
+
+
+def get_gmm_phenotype(data):
+    low = np.percentile(data, 0.01)
+    high = np.percentile(data, 99.99)
+    data = np.clip(data, low, high)
+
+    sum = np.sum(data)
+    median = np.median(data)
+    data_med = data / sum * median
+
+    data_log = np.log1p(data_med)
+    data_log = data_log.reshape(-1, 1)
+
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    data_norm = scaler.fit_transform(data_log)
+
+    gmm = GaussianMixture(n_components=2)
+    gmm.fit(data_norm)
+    gate = np.mean(gmm.means_)
+
+    return get_gate_phenotype(gate, np.ravel(data_norm))
+
+
+def main(inputs, output, image, anndata, masks=None):
+    """
+    Parameter
+    ---------
+    inputs : str
+        File path to galaxy tool parameter.
+    output : str
+        Output folder for saving web content.
+    image : str
+        File path to the OME Tiff image.
+    anndata : str
+        File path to anndata containing phenotyping info.
+    masks : str
+        File path to the image masks.
+    """
+    warnings.simplefilter('ignore')
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    marker = params['marker'].strip()
+    from_gate = params['from_gate']
+    to_gate = params['to_gate']
+    increment = params['increment']
+    x_coordinate = params['x_coordinate'].strip() or 'X_centroid'
+    y_coordinate = params['y_coordinate'].strip() or 'Y_centroid'
+
+    adata = read_h5ad(anndata)
+
+    # If no raw data is available make a copy
+    if adata.raw is None:
+        adata.raw = adata
+
+    # Copy of the raw data if it exisits
+    if adata.raw is not None:
+        adata.X = adata.raw.X
+
+    data = pd.DataFrame(
+        adata.X,
+        columns=adata.var.index,
+        index=adata.obs.index
+    )
+    marker_values = data[[marker]].values
+    marker_values_log = np.log1p(marker_values)
+
+    # Identify the list of increments
+    gate_names = []
+    for num in np.arange(from_gate, to_gate, increment):
+        num = round(num, 3)
+        key = marker + '--' + str(num)
+        adata.obs[key] = get_gate_phenotype(num, marker_values_log)
+        gate_names.append(key)
+
+    adata.obs['GMM_auto'] = get_gmm_phenotype(marker_values)
+    gate_names.append('GMM_auto')
+
+    adata.obsm['XY_coordinate'] = adata.obs[[x_coordinate, y_coordinate]].values
+
+    vc = VitessceConfig(name=None, description=None)
+    dataset = vc.add_dataset()
+    image_wrappers = [OmeTiffWrapper(img_path=image, name='OMETIFF')]
+    if masks:
+        image_wrappers.append(
+            OmeTiffWrapper(img_path=masks, name='MASKS', is_bitmask=True)
+        )
+    dataset.add_object(MultiImageWrapper(image_wrappers))
+
+    dataset.add_object(
+        AnnDataWrapper(
+            adata,
+            spatial_centroid_obsm='XY_coordinate',
+            cell_set_obs=gate_names,
+            cell_set_obs_names=[obj[0].upper() + obj[1:] for obj in gate_names],
+            expression_matrix="X"
+        )
+    )
+    spatial = vc.add_view(dataset, cm.SPATIAL)
+    cellsets = vc.add_view(dataset, cm.CELL_SETS)
+    status = vc.add_view(dataset, cm.STATUS)
+    lc = vc.add_view(dataset, cm.LAYER_CONTROLLER)
+    genes = vc.add_view(dataset, cm.GENES)
+    cell_set_sizes = vc.add_view(dataset, cm.CELL_SET_SIZES)
+    cell_set_expression = vc.add_view(dataset, cm.CELL_SET_EXPRESSION)
+
+    vc.layout(
+        (status / genes / cell_set_expression)
+        | (cellsets / cell_set_sizes / lc)
+        | (spatial)
+    )
+    config_dict = vc.export(to='files', base_url='http://localhost', out_dir=output)
+
+    with open(Path(output).joinpath('config.json'), 'w') as f:
+        json.dump(config_dict, f, indent=4)
+
+
+if __name__ == '__main__':
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-e", "--output", dest="output", required=True)
+    aparser.add_argument("-g", "--image", dest="image", required=True)
+    aparser.add_argument("-a", "--anndata", dest="anndata", required=True)
+    aparser.add_argument("-m", "--masks", dest="masks", required=False)
+
+    args = aparser.parse_args()
+
+    main(args.inputs, args.output, args.image, args.anndata, args.masks)