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Sm.tl.spatial expression

Short Description

sm.tl.spatial_expression: The function allows users to compute a neighbourhood weighted matrix based on the expression values.

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.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_expression(adata, x_coordinate='X_centroid', y_coordinate='Y_centroid', method='radius', radius=30, knn=10, imageid='imageid', use_raw=True, log=True, subset=None, label='spatial_expression', output_dir=None)

Parameters:

Name Type Description Default
adata

AnnData object loaded into memory or path to 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'
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
use_raw

boolian, optional
Argument to denote whether to use the raw data or scaled data after applying sm.pp.rescale.

True
log

boolian, optional
If True, the log of raw data is used. Set use_raw = True for this to take effect.

True
label

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

'spatial_expression'
output_dir

string, optional
Path to output directory.

None

Returns:

Type Description
adata

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

# Running the knn method
adata = sm.tl.spatial_expression (adata, x_coordinate='X_centroid',
                                  y_coordinate='Y_centroid',
                                  method='knn', knn=10, imageid='imageid', 
                                  use_raw=True,subset=None,
                                  label='spatial_expression_knn')
```
Source code in scimap/tools/_spatial_expression.py
def spatial_expression (adata, 
                        x_coordinate='X_centroid',
                        y_coordinate='Y_centroid',
                        method='radius', radius=30, 
                        knn=10, imageid='imageid', 
                        use_raw=True, log=True, subset=None,
                        label='spatial_expression',
                        output_dir=None):
    """
Parameters:
    adata : AnnData object loaded into memory or path to AnnData object.

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

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

    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.

    use_raw : boolian, optional  
        Argument to denote whether to use the raw data or scaled data after applying `sm.pp.rescale`.

    log : boolian, optional  
        If `True`, the log of raw data is used. Set use_raw = `True` for this to take effect. 

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

    output_dir : string, optional  
        Path to output directory.

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


 Example:
    ```python
    # Running the radius method
    adata = sm.tl.spatial_expression (adata, x_coordinate='X_centroid',
                                      y_coordinate='Y_centroid',
                                      method='radius', radius=30, 
                                      imageid='imageid', 
                                      use_raw=True,subset=None,
                                      label='spatial_expression_radius')

    # Running the knn method
    adata = sm.tl.spatial_expression (adata, x_coordinate='X_centroid',
                                      y_coordinate='Y_centroid',
                                      method='knn', knn=10, imageid='imageid', 
                                      use_raw=True,subset=None,
                                      label='spatial_expression_knn')
    ```
    """

    # Load the andata object    
    if isinstance(adata, str):
        imid = str(adata.rsplit('/', 1)[-1])
        adata = anndata.read(adata)
    else:
        adata = adata


    # Error statements
    if use_raw is False:
        if all(adata.X[0] < 1) is False:
            raise ValueError('Please run `sm.pp.rescale` first if you wish to use `use_raw = False`')


    def spatial_expression_internal (adata_subset, x_coordinate, y_coordinate,log,
                                     method, radius, knn, imageid, use_raw, subset,label):

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

        # 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, leaf_size= 2)
            dist, ind = tree.query(data, k=knn, return_distance= True)


        # b) Local radius method
        if method == 'radius':
            print("Identifying neighbours within " + str(radius) + " pixels of every cell")
            kdt = BallTree(data, metric='euclidean')
            ind, dist = kdt.query_radius(data, r=radius, return_distance= True)

        # Normalize range (0-1) and account for total number of cells 
        d = scipy.sparse.lil_matrix((len(data), len(data)))
        for row, (columns, values) in enumerate(zip(ind, dist)):
            # Drop self-distance element.
            idx = columns != row
            columns = columns[idx]
            values = values[idx]
            if len(values) == 1:
                values = [1.0]
            elif len(values) > 1:
                # Normalize distances.
                values = (values.max() - values) / (values.max() - values.min())
                values /= values.sum()
            # Assign row to matrix.
            d[row, columns] = values

        # convert to csr sparse matrix
        wn_matrix_sparse = d.tocsr()


        # Calculation of spatial lag
        if use_raw==True:
            if log is True:
                spatial_lag = pd.DataFrame(wn_matrix_sparse * np.log1p(adata_subset.raw.X), columns = adata_subset.var.index, index=adata_subset.obs.index)
            else:
                spatial_lag = pd.DataFrame(wn_matrix_sparse * adata_subset.raw.X, columns = adata_subset.var.index, index=adata_subset.obs.index)
        else:
            spatial_lag = pd.DataFrame(wn_matrix_sparse * adata_subset.X, columns = adata_subset.var.index, index=adata_subset.obs.index)

        # return value
        return spatial_lag

    # 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_expression_internal = lambda x: spatial_expression_internal(adata_subset=x, 
                                                                x_coordinate=x_coordinate, 
                                                                y_coordinate=y_coordinate, 
                                                                method=method, radius=radius, 
                                                                knn=knn, imageid=imageid, 
                                                                use_raw=use_raw, subset=subset,
                                                                log=log,
                                                                label=label) 
    all_data = list(map(r_spatial_expression_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

    # Save data if requested
    if output_dir is not None:
        output_dir = pathlib.Path(output_dir)
        output_dir.mkdir(exist_ok=True, parents=True)
        adata.write(output_dir / imid)
    else:    
        # Return data
        return adata
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