# HG changeset patch
# User goeckslab
# Date 1662657773 0
# Node ID 6df8d6e421520275b0622c1ebb7bf36c5574ed1a
planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1
diff -r 000000000000 -r 6df8d6e42152 gate_finder.py
--- /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)
diff -r 000000000000 -r 6df8d6e42152 gate_finder.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/gate_finder.xml Thu Sep 08 17:22:53 2022 +0000
@@ -0,0 +1,63 @@
+
+ displays a series of gating outcomes using Vitessce
+
+ main_macros.xml
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+ echo "@VERSION@"
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+ 10.31219/osf.io/y8thv
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diff -r 000000000000 -r 6df8d6e42152 index.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/index.html Thu Sep 08 17:22:53 2022 +0000
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+ Vitessce
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Vitessce is loading...
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