Skip to content

Sm.tl.spatial count

Short Description

The sm.tl.spatial_count function allows users to compute a neighbourhood matrix using any categorical variable (e.g. cell-types) as input.

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 neighbourhood matrix is saved with adata.uns.

This can be further clustered to identify similar neighbourhoods. Use the [spatial_cluster] function to further group the neighbourhoods into Reccurent Cellular Neighbourhoods (RCNs)

Function

spatial_count(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid', phenotype='phenotype', method='radius', radius=30, knn=10, imageid='imageid', subset=None, label='spatial_count')

Parameters:

Name Type Description Default
adata

anndata object

required
x_coordinate

float, required
Column name containing the x-coordinates values.

'X_centroid'
y_coordinate

float, required
Column name containing the y-coordinates values.

'Y_centroid'
phenotype

string, required
Column name of the column containing the phenotype information. It could also be any categorical assignment given to single cells.

'phenotype'
method

string, optional
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'
radius

int, optional
The radius used to define a local neighbhourhood.

30
knn

int, optional
Number of cells considered for defining the local neighbhourhood.

10
imageid

string, optional
Column name of the column containing the image id.

'imageid'
subset

string, optional
imageid of a single image to be subsetted for analyis.

None
label

string, optional
Key for the returned data, stored in adata.uns.

'spatial_count'

Returns:

Type Description
adata

anndata object
Updated AnnData object with the results stored in adata.uns ['spatial_count'].

Examples:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
# Running the radius method
adata = sm.tl.spatial_count (adata,x_coordinate='X_centroid',
                             y_coordinate='Y_centroid',
                             phenotype='phenotype',
                             method='radius',radius=30,
                             imageid='imageid',subset=None,
                             label='spatial_count_radius')

# Running the knn method
adata = sm.tl.spatial_count (adata,x_coordinate='X_centroid',
                             y_coordinate='Y_centroid',
                             phenotype='phenotype',method='knn',
                             knn=10, imageid='imageid',
                             subset=None,label='spatial_count_knn')
Source code in scimap/tools/_spatial_count.py
def spatial_count (adata,
                   x_coordinate='X_centroid',
                   y_coordinate='Y_centroid',
                   phenotype='phenotype',
                   method='radius',
                   radius=30,knn=10,
                   imageid='imageid',
                   subset=None,
                   label='spatial_count'):
    """
Parameters:
    adata : anndata object

    x_coordinate : float, required  
        Column name containing the x-coordinates values.

    y_coordinate : float, required  
        Column name containing the y-coordinates values.

    phenotype : string, required  
        Column name of the column containing the phenotype information. 
        It could also be any categorical assignment given to single cells.

    method : string, optional  
        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 radius used to define a local neighbhourhood.

    knn : int, optional  
        Number of cells considered for defining the local neighbhourhood.

    imageid : string, optional  
        Column name of the column containing the image id.

    subset : string, optional  
        imageid of a single image to be subsetted for analyis.

    label : string, optional  
        Key for the returned data, stored in `adata.uns`.

Returns:
    adata : anndata object  
        Updated AnnData object with the results stored in `adata.uns ['spatial_count']`.

Example:
    ```python
    # Running the radius method
    adata = sm.tl.spatial_count (adata,x_coordinate='X_centroid',
                                 y_coordinate='Y_centroid',
                                 phenotype='phenotype',
                                 method='radius',radius=30,
                                 imageid='imageid',subset=None,
                                 label='spatial_count_radius')

    # Running the knn method
    adata = sm.tl.spatial_count (adata,x_coordinate='X_centroid',
                                 y_coordinate='Y_centroid',
                                 phenotype='phenotype',method='knn',
                                 knn=10, imageid='imageid',
                                 subset=None,label='spatial_count_knn')
    ```
    """

    def spatial_count_internal (adata_subset,x_coordinate,y_coordinate,phenotype,method,radius,knn,
                                imageid,subset,label):

        # Create a DataFrame with the necessary inforamtion
        data = pd.DataFrame({'x': adata_subset.obs[x_coordinate], 'y': adata_subset.obs[y_coordinate], 'phenotype': adata_subset.obs[phenotype]})

        # Identify neighbourhoods based on the method used
        # a) KNN method
        if method == 'knn':
            print("Identifying the " + str(knn) + " nearest neighbours for every cell")
            tree = BallTree(data[['x','y']], leaf_size= 2)
            ind = tree.query(data[['x','y']], k=knn, return_distance= False)
            neighbours = pd.DataFrame(ind.tolist(), index = data.index) # neighbour DF
            neighbours.drop(0, axis=1, inplace=True) # Remove self neighbour

        # b) Local radius method
        if method == 'radius':
            print("Identifying neighbours within " + str(radius) + " pixels of every cell")
            kdt = BallTree(data[['x','y']], metric='euclidean') 
            ind = kdt.query_radius(data[['x','y']], r=radius, return_distance=False)
            for i in range(0, len(ind)): ind[i] = np.delete(ind[i], np.argwhere(ind[i] == i))#remove self
            neighbours = pd.DataFrame(ind.tolist(), index = data.index) # neighbour DF

        # Map phenotype
        phenomap = dict(zip(list(range(len(ind))), data['phenotype'])) # Used for mapping

        # Loop through (all functionized methods were very slow)
        for i in neighbours.columns:
            neighbours[i] = neighbours[i].dropna().map(phenomap, na_action='ignore')

        # Drop NA
        #n_dropped = neighbours.dropna(how='all')

        # Collapse all the neighbours into a single column
        n = pd.DataFrame(neighbours.stack(), columns = ["neighbour_phenotype"])
        n.index = n.index.get_level_values(0) # Drop the multi index
        n = pd.DataFrame(n)
        n['order'] = list(range(len(n)))

        # Merge with real phenotype
        n_m = n.merge(data['phenotype'], how='inner', left_index=True, right_index=True)
        n_m['neighbourhood'] = n_m.index
        n = n_m.sort_values(by=['order'])

        # Normalize based on total cell count
        k = n.groupby(['neighbourhood','neighbour_phenotype']).size().unstack().fillna(0)
        k = k.div(k.sum(axis=1), axis=0)

        # return the normalized neighbour occurance count
        return k

    # Subset a particular image if needed
    if subset is not None:
        adata_list = [adata[adata.obs[imageid] == subset]]
    else:
        adata_list = [adata[adata.obs[imageid] == i] for i in adata.obs[imageid].unique()]

    # Apply function to all images and create a master dataframe
    # Create lamda function 
    r_spatial_count_internal = lambda x: spatial_count_internal(adata_subset=x,x_coordinate=x_coordinate,
                                                   y_coordinate=y_coordinate,phenotype=phenotype,
                                                   method=method,radius=radius,knn=knn,
                                                   imageid=imageid,subset=subset,label=label) 
    all_data = list(map(r_spatial_count_internal, adata_list)) # Apply function 


    # Merge all the results into a single dataframe    
    result = []
    for i in range(len(all_data)):
        result.append(all_data[i])
    result = pd.concat(result, join='outer')  

    # Reindex the cells
    result = result.fillna(0)
    result = result.reindex(adata.obs.index)

    # Add to adata
    adata.uns[label] = result

    # Return        
    return adata
Back to top