scimap.tl.spatial_aggregate

Short description

The spatial_aggregate function allows users to find regions of aggregration of similar cells
The purity parameter can be used to tune the granulatity of allowed cell-type heterogenity within local neighbourhood.

The function supports two methods to define a local neighbourhood
Radius method: Can be used to identifies the neighbours within a user defined radius for every cell. KNN method: Can be used to identifies the neighbours based on K nearest neigbours for every cell

The resultant proportion matrix is saved with adata.obs.

Parameters

adata : AnnData Object

x_coordinate : float, required (The default is 'X_centroid')
Column name containing the x-coordinates values.

y_coordinate : float, required (The default is 'Y_centroid')
Column name containing the y-coordinates values.

purity : int, required (The default is 60)
Supply a value between 1 and 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. The default is 60.

method : string, optional (The default is 'radius')
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 (The default is 30)
The radius used to define a local neighbhourhood.

knn : int, optional (The default is 10)
Number of cells considered for defining the local neighbhourhood.

imageid : string, optional (The default is 'imageid')
Column name of the column containing the image id.

subset : string, optional (The default is None)
imageid of the image to be subsetted for analyis.

label : string, optional (The default is 'spatial_aggregate')
Key for the returned data, stored in adata.obs.

Returns AnnData object with the results stored in adata.obs['spatial_aggregate'].

Example

# Running the radius method
adata = sm.tl.spatial_aggregate (adata, x_coordinate='X_centroid',y_coordinate='Y_centroid',
                                 phenotype='phenotype', method='radius', radius=30,
                                 imageid='imageid',subset=None,label='spatial_aggregate_radius')
# Running the knn method
adata =  sm.tl.spatial_aggregate (adata, x_coordinate='X_centroid',y_coordinate='Y_centroid',
                                  phenotype='phenotype', method='knn', knn=10, 
                                  imageid='imageid',subset=None,label='spatial_aggregate_knn')