attempt to fix failed checks

scimap/helpers/_add_roi_omero.py

@@ -18,6 +18,7 @@
 import matplotlib.patches as mpatches
 import scipy.spatial.distance as sdistance
 from joblib import Parallel, delayed
+import argparse
 
 
 def add_roi_omero (adata, roi, x_coordinate='X_centroid',y_coordinate='Y_centroid',
@@ -182,3 +183,29 @@ def add_roi_internal (roi_id):
 
     # return
     return adata
+
+parser = argparse.ArgumentParser(description='Add ROI to AnnData object from Omero')
+
+parser.add_argument('adata', help='Path to the AnnData object')
+parser.add_argument('roi', help='Path to the ROI DataFrame')
+parser.add_argument('--x_coordinate', default='X_centroid', help='Column name for x-coordinates')
+parser.add_argument('--y_coordinate', default='Y_centroid', help='Column name for y-coordinates')
+parser.add_argument('--imageid', default='imageid', help='Column name for image IDs')
+parser.add_argument('--subset', help='List of image names to add ROIs')
+parser.add_argument('--overwrite', action='store_true', help='Overwrite the label column')
+parser.add_argument('--label', default='ROI', help='Key for the returned data')
+parser.add_argument('--n_jobs', type=int, default=-1, help='Number of cores to use')
+parser.add_argument('--verbose', action='store_true', help='Enable verbose mode')
+
+args = parser.parse_args()
+
+    # Call the add_roi_omero function
+result = add_roi_omero(args.adata,args.roi, x_coordinate=args.x_coordinate, y_coordinate=args.y_coordinate,
+                           imageid=args.imageid, subset=args.subset, overwrite=args.overwrite,
+                           label=args.label, n_jobs=args.n_jobs, verbose=args.verbose)
+
+    # Save the modified AnnData object
+result.write('modified_anndata.h5ad')
+
+if __name__ == '__main__':
+    add_roi_omero_command_line()

scimap/tools/_spatial_aggregate.py

@@ -13,6 +13,7 @@
 # Import library
 import pandas as pd
 import numpy as np
+import argparse
 from sklearn.neighbors import BallTree
 
 # Function
@@ -24,40 +25,40 @@ def spatial_aggregate (adata, x_coordinate='X_centroid',y_coordinate='Y_centroid
 Parameters:
     adata : AnnData object
 
-    x_coordinate : float, required  
+    x_coordinate (float):  
         Column name containing the x-coordinates values.
 
-    y_coordinate : float, required  
+    y_coordinate (float):  
         Column name containing the y-coordinates values.
 
-    purity : int, optional  
+    purity (int):  
         Supply a value between 1 to 100. It is the percent purity of neighbouring cells.
         For e.g. if 60 is chosen, every neighbourhood is tested such that if a 
         particular phenotype makes up greater than 60% of the total 
         population it is annotated to be an aggregate of that particular phenotype.
 
-    phenotype : string, required  
+    phenotype (string):  
         Column name of the column containing the phenotype information. 
         It could also be any categorical assignment given to single cells.
 
-    method : string, optional  
+    method (string):  
         Two options are available: a) 'radius', b) 'knn'.
         a) radius - Identifies the neighbours within a given radius for every cell.
         b) knn - Identifies the K nearest neigbours for every cell.
 
-    radius : int, optional  
+    radius (int):  
         The radius used to define a local neighbhourhood.
 
-    knn : int, optional  
+    knn (int):  
         Number of cells considered for defining the local neighbhourhood.
 
-    imageid : string, optional  
+    imageid (string):  
         Column name of the column containing the image id.
 
-    subset : string, optional  
+    subset (string):  
         imageid of a single image to be subsetted for analyis.
 
-    label : string, optional  
+    label (string):  
         Key for the returned data, stored in `adata.obs`.
 
 Returns:
@@ -183,3 +184,28 @@ def spatial_aggregate_internal (adata_subset, x_coordinate,y_coordinate,phenotyp
     # Return        
     return adata
 
+if __name__ == '__main__':
+    # Create argparse parser
+    parser = argparse.ArgumentParser(description='Perform spatial aggregation analysis.')
+
+    # Add arguments
+    parser.add_argument('adata', help='Path to the AnnData object file.')
+    parser.add_argument('--x_coordinate', default='X_centroid', help='Column name for x-coordinates.')
+    parser.add_argument('--y_coordinate', default='Y_centroid', help='Column name for y-coordinates.')
+    parser.add_argument('--purity', type=int, default=60, help='Percent purity of neighboring cells.')
+    parser.add_argument('--phenotype', default='phenotype', help='Column name for phenotype information.')
+    parser.add_argument('--method', default='radius', help='Method for identifying neighbors.')
+    parser.add_argument('--radius', type=int, default=30, help='Radius used to define a local neighborhood.')
+    parser.add_argument('--knn', type=int, default=10, help='Number of cells considered for defining the local neighborhood.')
+    parser.add_argument('--imageid',type=str, default='imageid', help='Column name for image ID.')
+    parser.add_argument('--subset',type=str, help='Image ID of a single image to be subsetted for analysis.')
+    parser.add_argument('--label',type=str, default='spatial_aggregate', help='Key for the returned data.')
+
+    # Parse the command line arguments
+    args = parser.parse_args()
+
+    # Call the spatial_aggregate function with the parsed arguments
+    spatial_aggregate(args.adata, args.x_coordinate, args.y_coordinate, args.purity,
+                      args.phenotype, args.method, args.radius, args.knn,
+                      args.imageid, args.subset, args.label)
+

scimap/tools/_spatial_count.py

@@ -23,6 +23,7 @@
 # Import library
 import pandas as pd
 import numpy as np
+import argparse
 from sklearn.neighbors import BallTree
 
 # Function
@@ -39,34 +40,34 @@ def spatial_count (adata,
 Parameters:
     adata : anndata object
 
-    x_coordinate : float, required  
+    x_coordinate (float):  
         Column name containing the x-coordinates values.
 
-    y_coordinate : float, required  
+    y_coordinate (float):   
         Column name containing the y-coordinates values.
 
-    phenotype : string, required  
+    phenotype (string):   
         Column name of the column containing the phenotype information. 
         It could also be any categorical assignment given to single cells.
 
-    method : string, optional  
+    method (string):   
         Two options are available: a) `radius`, b) `knn`.  
         a) radius - Identifies the neighbours within a given radius for every cell.  
         b) knn - Identifies the K nearest neigbours for every cell.  
 
-    radius : int, optional  
+    radius (int):   
         The radius used to define a local neighbhourhood.
 
-    knn : int, optional  
+    knn (int):   
         Number of cells considered for defining the local neighbhourhood.
 
-    imageid : string, optional  
+    imageid (string):   
         Column name of the column containing the image id.
 
-    subset : string, optional  
+    subset (string):   
         imageid of a single image to be subsetted for analyis.
 
-    label : string, optional  
+    label (string):   
         Key for the returned data, stored in `adata.uns`.
 
 Returns:
@@ -172,4 +173,28 @@ def spatial_count_internal (adata_subset,x_coordinate,y_coordinate,phenotype,met
     adata.uns[label] = result
 
     # Return        
-    return adata
+    return adata
+
+if __name__ == '__main__':
+    # Create argparse parser
+    parser = argparse.ArgumentParser(description='Perform spatial counting analysis.')
+
+    # Add arguments
+    parser.add_argument('--adata',type=str ,help='Path to the AnnData object file.')
+    parser.add_argument('--x_coordinate',type=str, default='X_centroid', help='Column name for x-coordinates.')
+    parser.add_argument('--y_coordinate',type=str, default='Y_centroid', help='Column name for y-coordinates.')
+    parser.add_argument('--phenotype',type=str, default='phenotype', help='Column name for phenotype information.')
+    parser.add_argument('--method', type=str, default='radius', help='Method for identifying neighbors.')
+    parser.add_argument('--radius', type=int, default=30, help='Radius used to define a local neighborhood.')
+    parser.add_argument('--knn', type=int, default=10, help='Number of cells considered for defining the local neighborhood.')
+    parser.add_argument('--imageid', type=str, default='imageid', help='Column name for image ID.')
+    parser.add_argument('--subset', type=str, help='Image ID of a single image to be subsetted for analysis.')
+    parser.add_argument('--label', type=str, default='spatial_count', help='Key for the returned data.')
+
+    # Parse the command line arguments
+    args = parser.parse_args()
+
+    # Call the spatial_count function with the parsed arguments
+    spatial_count(args.adata, args.x_coordinate, args.y_coordinate, args.phenotype,
+                  args.method, args.radius, args.knn, args.imageid,
+                  args.subset, args.label)

scimap/tools/_spatial_distance.py

@@ -13,6 +13,7 @@
 # Import library
 import pandas as pd
 from sklearn.neighbors import BallTree
+import argparse
 from joblib import Parallel, delayed
 import itertools
 
@@ -27,26 +28,26 @@ def spatial_distance (adata,x_coordinate='X_centroid',y_coordinate='Y_centroid',
 
     adata : AnnData object
 
-    x_coordinate : float, required  
+    x_coordinate (float):  
         Column name containing the x-coordinates values.
 
-    y_coordinate : float, required  
+    y_coordinate (float):  
         Column name containing the y-coordinates values.
 
-    z_coordinate : float, optional  
+    z_coordinate (float, optional):  
         Column name containing the z-coordinates values.
 
-    phenotype : string, required  
+    phenotype (string):  
         Column name of the column containing the phenotype information. 
         It could also be any categorical assignment given to single cells.
 
-    subset : string, optional  
+    subset (string):  
         imageid of a single image to be subsetted for analyis.
 
-    imageid : string, optional  
+    imageid (string):  
         Column name of the column containing the image id.
 
-    label : string, optional  
+    label (string):  
         Key for the returned data, stored in `adata.obs`.
 
 Returns:
@@ -122,6 +123,27 @@ def distance (pheno):
 
     # return
     return adata
+
+if __name__ == '__main__':
+    # Create argparse parser
+    parser = argparse.ArgumentParser(description='Compute spatial distances.')
+
+    # Add arguments
+    parser.add_argument('--adata', type = str, help='Path to the AnnData object file.')
+    parser.add_argument('--x_coordinate', type = float, default='X_centroid', help='Column name for x-coordinates.')
+    parser.add_argument('--y_coordinate', type= float, default='Y_centroid', help='Column name for y-coordinates.')
+    parser.add_argument('--z_coordinate', type= float, help='Column name for z-coordinates.')
+    parser.add_argument('--phenotype', type=str, default='phenotype', help='Column name for phenotype information.')
+    parser.add_argument('--subset', type=str, help='Image ID of a single image to be subsetted for analysis.')
+    parser.add_argument('--imageid', type=str, default='imageid', help='Column name for image ID.')
+    parser.add_argument('--label', type=str, default='spatial_distance', help='Key for the returned data.')
+
+    # Parse the command line arguments
+    args = parser.parse_args()
+
+    # Call the spatial_distance function with the parsed arguments
+    spatial_distance(args.adata, args.x_coordinate, args.y_coordinate, args.z_coordinate,
+                     args.phenotype, args.subset, args.imageid, args.label)
 
 
 

scimap/tools/_spatial_interaction.py

@@ -17,6 +17,7 @@
 from joblib import Parallel, delayed
 import scipy
 from functools import reduce
+import argparse
 
 
 # Function
@@ -31,36 +32,36 @@ def spatial_interaction (adata,x_coordinate='X_centroid',y_coordinate='Y_centroi
     """
 Parameters:
     adata : AnnData object
-    x_coordinate : float, required  
+    x_coordinate (float):  
         Column name containing the x-coordinates values.
-    y_coordinate : float, required  
+    y_coordinate (float):  
         Column name containing the y-coordinates values.
-    z_coordinate : float, optional  
+    z_coordinate (float):  
         Column name containing the z-coordinates values.
-    phenotype : string, required  
+    phenotype (string):  
         Column name of the column containing the phenotype information. 
         It could also be any categorical assignment given to single cells.
-    method : string, optional  
+    method (string):  
         Two options are available: a) 'radius', b) 'knn'.
         a) radius - Identifies the neighbours within a given radius for every cell.
         b) knn - Identifies the K nearest neigbours for every cell.
-    radius : int, optional  
+    radius (int):  
         The radius used to define a local neighbhourhood.
-    knn : int, optional  
+    knn (int):  
         Number of cells considered for defining the local neighbhourhood.
-    permutation : int, optional  
+    permutation (int):  
         The number of permutations to be performed for calculating the P-Value.
-    imageid : string, optional  
+    imageid (string):  
         Column name of the column containing the image id.
-    subset : string, optional  
+    subset (string):  
         imageid of a single image to be subsetted for analyis.
-    pval_method : string, optional  
+    pval_method (string):  
         Two options are available: a) 'histocat', b) 'zscore'.  
         a) P-values are calculated by subtracting the permuted mean from the observed mean
         divided by the number of permutations as described in the histoCAT manuscript (Denis et.al, Nature Methods 2017)  
         b) zscores are calculated from the mean and standard deviation and further p-values are
         derived by fitting the observed values to a normal distribution. The default is 'histocat'.
-    label : string, optional  
+    label (string):  
         Key for the returned data, stored in `adata.obs`. The default is 'spatial_interaction'.
 Returns:
     adata : AnnData object  
@@ -231,4 +232,37 @@ def permutation_pval (data):
     # return
     return adata
 
+if __name__ == '__main__':
+    # Create argparse parser
+    parser = argparse.ArgumentParser(description='Compute spatial interaction.')
+
+    parser.add_argument('--adata', type=str,help='Path to the AnnData object file.')
+    parser.add_argument('--x_coordinate', type=float, default='X_centroid', help='Column name for x-coordinates.')
+    parser.add_argument('--y_coordinate', type=float,default='Y_centroid', help='Column name for y-coordinates.')
+    parser.add_argument('--z_coordinate',type=float, help='Column name for z-coordinates.')
+    parser.add_argument('--phenotype',type=str, default='phenotype', help='Column name for phenotype information.')
+    parser.add_argument('--method',type=str, default='radius', choices=['radius', 'knn'], help='Method for identifying neighbors.')
+    parser.add_argument('--radius', type=int, default=30, help='Radius used to define a local neighborhood.')
+    parser.add_argument('--knn', type=int, default=10, help='Number of cells considered for defining the local neighborhood.')
+    parser.add_argument('--permutation', type=int, default=1000, help='Number of permutations for calculating p-value.')
+    parser.add_argument('--imageid',type=str, default='imageid', help='Column name for image ID.')
+    parser.add_argument('--subset',type=str, help='Image ID of a single image to be subsetted for analysis.')
+    parser.add_argument('--pval_method',type=str, default='histocat', choices=['histocat', 'zscore'], help='Method for calculating p-values.')
+    parser.add_argument('--label',type=str, default='spatial_interaction', help='Key for the returned data.')
+
+    # Parse the command-line arguments
+    args = parser.parse_args()
+
+    # Call the spatial_interaction function with the parsed arguments
+    spatial_interaction(args.adata, args.x_coordinate, args.y_coordinate, args.z_coordinate,
+                        args.phenotype, args.method, args.radius, args.knn,
+                        args.permutation, args.imageid, args.subset,
+                        args.pval_method, args.label)
+
+
+
+
+
+
+