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spatial_aggregate

Short Description

sm.tl.spatial_aggregate: This function identifies spatial clusters of phenotypically similar cells within specified regions. By adjusting the purity parameter, users can specify the minimum percentage of similarity required among cells within a defined radius or nearest neighbors, enabling precise delineation of cellular aggregates.

Function

spatial_aggregate(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid', z_coordinate=None, purity=60, phenotype='phenotype', method='radius', radius=30, knn=10, imageid='imageid', subset=None, verbose=True, label='spatial_aggregate')

Parameters:

Name Type Description Default
adata AnnData

The annotated data matrix of shape (n_obs, n_vars), where rows correspond to cells and columns to genes, used for spatial analysis.

 required
x_coordinate (str, required)

The column name in adata containing the x-coordinates of cells.

 'X_centroid'
y_coordinate (str, required)

The column name in adata containing the y-coordinates of cells.

 'Y_centroid'
z_coordinate (str, required)

The column name in adata containing the z-coordinates of cells.

 None
purity int

The minimum percentage (between 1 and 100) of cells with a similar phenotype required in a neighborhood for it to be considered a cluster.

60
phenotype (str, required)

The column name in adata representing cell phenotype information or any other categorical classification of cells.

 'phenotype'
method str

The neighborhood definition method: 'radius' for radial distance-based neighborhoods or 'knn' for k-nearest neighbors-based neighborhoods.

'radius'
radius int

The radius used to define a neighborhood around each cell, applicable when method='radius'. Measured in the same units as x and y coordinates.

 30
knn int

The number of nearest neighbors to consider for defining a neighborhood around each cell, applicable when method='knn'.

 10
imageid str

The column name in adata containing identifiers for different images, allowing for analysis within specific images.

 'imageid'
subset str

The identifier of a specific image to restrict the analysis to. If provided, analysis will only be performed on this subset.

 None
label str

The key under which to store the results in adata.obs, allowing for customized labeling of the output.

 'spatial_aggregate'
verbose bool
True

Returns:

Name Type Description
adata AnnData

The input AnnData object updated with the results stored under adata.obs[label], where label is the specified output label.
Example 
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# Analyze spatial aggregation using the radius method
adata = sm.tl.spatial_aggregate(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid',
                                phenotype='phenotype', method='radius', radius=30, purity=60,
                                imageid='imageid', subset=None, label='spatial_aggregate_radius')

# Analyze spatial aggregation using the knn method
adata = sm.tl.spatial_aggregate(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid',
                                phenotype='phenotype', method='knn', knn=10, purity=60,
                                imageid='imageid', subset=None, label='spatial_aggregate_knn')

# Subset analysis to a specific image using the radius method
adata = sm.tl.spatial_aggregate(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid',
                                phenotype='phenotype', method='radius', radius=30, purity=60,
                                imageid='imageid', subset='image_01', label='spatial_aggregate_image_01')
Source code in scimap/tools/spatial_aggregate.py 
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def spatial_aggregate (adata, 
                       x_coordinate='X_centroid',
                       y_coordinate='Y_centroid',
                       z_coordinate= None,
                       purity = 60, 
                       phenotype='phenotype', 
                       method='radius', 
                       radius=30, 
                       knn=10, 
                       imageid='imageid',
                       subset=None,
                       verbose=True,
                       label='spatial_aggregate'):
    """

Parameters:
        adata (anndata.AnnData):  
            The annotated data matrix of shape (n_obs, n_vars), where rows correspond to cells and columns to genes, used for spatial analysis.

        x_coordinate (str, required):  
            The column name in `adata` containing the x-coordinates of cells.

        y_coordinate (str, required):  
            The column name in `adata` containing the y-coordinates of cells.

        z_coordinate (str, required):  
            The column name in `adata` containing the z-coordinates of cells.

        purity (int, optional):  
            The minimum percentage (between 1 and 100) of cells with a similar phenotype required in a neighborhood for it to be considered a cluster. 

        phenotype (str, required):  
            The column name in `adata` representing cell phenotype information or any other categorical classification of cells.

        method (str, optional):  
            The neighborhood definition method: 'radius' for radial distance-based neighborhoods or 'knn' for k-nearest neighbors-based neighborhoods. 

        radius (int, optional):  
            The radius used to define a neighborhood around each cell, applicable when `method='radius'`. Measured in the same units as x and y coordinates.

        knn (int, optional):  
            The number of nearest neighbors to consider for defining a neighborhood around each cell, applicable when `method='knn'`.

        imageid (str, optional):  
            The column name in `adata` containing identifiers for different images, allowing for analysis within specific images.

        subset (str, optional):  
            The identifier of a specific image to restrict the analysis to. If provided, analysis will only be performed on this subset.

        label (str, optional):  
            The key under which to store the results in `adata.obs`, allowing for customized labeling of the output.

        verbose (bool):  
        If set to `True`, the function will print detailed messages about its progress and the steps being executed.

Returns:
        adata (anndata.AnnData):    
            The input AnnData object updated with the results stored under `adata.obs[label]`, where `label` is the specified output label.

Example:
    ```python

    # Analyze spatial aggregation using the radius method
    adata = sm.tl.spatial_aggregate(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid',
                                    phenotype='phenotype', method='radius', radius=30, purity=60,
                                    imageid='imageid', subset=None, label='spatial_aggregate_radius')

    # Analyze spatial aggregation using the knn method
    adata = sm.tl.spatial_aggregate(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid',
                                    phenotype='phenotype', method='knn', knn=10, purity=60,
                                    imageid='imageid', subset=None, label='spatial_aggregate_knn')

    # Subset analysis to a specific image using the radius method
    adata = sm.tl.spatial_aggregate(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid',
                                    phenotype='phenotype', method='radius', radius=30, purity=60,
                                    imageid='imageid', subset='image_01', label='spatial_aggregate_image_01')

    ```
    """

    # Error statements
    #if purity < 51:
    #    raise ValueError('purity should be set to a value greater than 50')

    def spatial_aggregate_internal (adata_subset, x_coordinate,y_coordinate,z_coordinate,phenotype,purity,
                                    method,radius,knn,imageid,subset,label):


        # Create a DataFrame with the necessary inforamtion
        if z_coordinate is not None:
            if verbose:
                print("Including Z -axis")
            data = pd.DataFrame({'x': adata_subset.obs[x_coordinate], 'y': adata_subset.obs[y_coordinate], 'z': adata_subset.obs[z_coordinate], 'phenotype': adata_subset.obs[phenotype]})
        else:
            data = pd.DataFrame({'x': adata_subset.obs[x_coordinate], 'y': adata_subset.obs[y_coordinate], 'phenotype': adata_subset.obs[phenotype]})

        #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':
            if verbose:
                print("Identifying the " + str(knn) + " nearest neighbours for every cell")
            if z_coordinate is not None:
                tree = BallTree(data[['x','y','z']], leaf_size= 2)
                ind = tree.query(data[['x','y','z']], k=knn, return_distance= False)
            else:
                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':
            if verbose:
                print("Identifying neighbours within " + str(radius) + " pixels of every cell")
            if z_coordinate is not None:
                kdt = BallTree(data[['x','y','z']], metric='euclidean') 
                ind = kdt.query_radius(data[['x','y','z']], r=radius, return_distance=False)
            else:
                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


# =============================================================================
#         if method == 'knn':
#             if verbose:
#                 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':
#             if verbose:
#                 print("Identifying neighbours within " + str(radius) + " pixels of every cell")
#             kdt = BallTree(data[['x','y']], leaf_size= 2) 
#             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'])

        # Count the neighbours
        k = n.groupby(['neighbourhood','neighbour_phenotype']).size().unstack().fillna(0)
        k = k.div(k.sum(axis=1), axis=0)

        # Iteratte over all rows and find the column which passes the purity test
        #def col_name_mapper (row_data, purity):
        #    p = row_data[row_data >= purity/100]
        #    #phenotype_name = 'non-significant' if len(p.index) == 0 else p.index[0]
        #    phenotype_name = 'non-significant' if len(p.index) == 0 else p.idxmax()
        #    return phenotype_name
        # Apply the iteration function
        #aggregate_pheno = pd.DataFrame(k.apply(lambda x: col_name_mapper(row_data=x,purity=purity), axis=1))


        # Within the spatial_aggregate_internal function
        # Create an empty DataFrame to hold the results
        aggregate_pheno = pd.DataFrame(index=k.index, columns=[0])

        # Iterate over rows in DataFrame k
        for idx, row in k.iterrows():
            filtered_row = row[row >= purity / 100]  # Apply purity threshold
            if not filtered_row.empty:  # Check if the filtered row is not empty
                # If not empty, find the index of the maximum value
                max_idx = filtered_row.idxmax()
            else:
                # If empty, set to 'non-significant'
                max_idx = 'non-significant'
            # Store the result
            aggregate_pheno.at[idx, 0] = max_idx


        #aggregate_pheno = pd.DataFrame(k[k>=purity/100].idxmax(axis=1).fillna('non-significant'))
        aggregate_pheno.columns = ['spatial_aggregate']

        # Return 
        return aggregate_pheno


    # 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_aggregate_internal = lambda x: spatial_aggregate_internal(adata_subset=x,
                                                                          x_coordinate=x_coordinate,
                                                                          y_coordinate=y_coordinate,
                                                                          z_coordinate=z_coordinate,
                                                                          phenotype=phenotype,
                                                                          method=method,
                                                                          radius=radius,knn=knn,
                                                                          imageid=imageid,subset=subset,
                                                                          purity=purity,
                                                                          label=label) 
    all_data = list(map(r_spatial_aggregate_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.reindex(adata.obs.index)
    result = result.fillna('non-significant')

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

    # Return        
    return adata