Sm.tl.spatial pscore
Short Description
sm.tl.spatial_pscore: A scoring system to evaluate user defined proximity between cell types.
The function generates two scores and saved at adata.uns:
A) Proximity Density: Total number of interactions identified divided by the total number of
cells of the cell-types that were used for interaction analysis.
B) Proximity Volume: Total number of interactions identified divided by the total number of all cells in the data.
The interaction sites are also recorded and saved in adata.obs
Functions
spatial_pscore(adata, proximity, score_by='imageid', x_coordinate='X_centroid', y_coordinate='Y_centroid', phenotype='phenotype', method='radius', radius=20, knn=3, imageid='imageid', subset=None, label='spatial_pscore')
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
adata |
AnnData object |
required | |
proximity |
list |
required | |
score_by |
string, optional |
'imageid' |
|
x_coordinate |
float, required |
'X_centroid' |
|
y_coordinate |
float, required |
'Y_centroid' |
|
phenotype |
string, required |
'phenotype' |
|
method |
string, optional |
'radius' |
|
radius |
int, optional |
20 |
|
knn |
int, optional |
3 |
|
imageid |
string, optional |
'imageid' |
|
subset |
string, optional |
None |
|
label |
string, optional |
'spatial_pscore' |
Returns:
| Type | Description |
|---|---|
adata |
AnnData object |
Examples:
1 2 3 4 5 | |
Source code in scimap/tools/_spatial_pscore.py
def spatial_pscore (adata,proximity, score_by='imageid', x_coordinate='X_centroid',y_coordinate='Y_centroid',
phenotype='phenotype',method='radius',radius=20,knn=3,
imageid='imageid',subset=None, label='spatial_pscore'):
"""
Parameters:
adata : AnnData object
proximity : list
Pass a list of cell-types for which the proximity score needs to calculated. e.g. ['CellType-A', 'CellType-B']
score_by : string, optional
If the scores need to compared across region's of interest, the column name containing the ROI's
should be passed. By default the score is calculated across the entire image.
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.obs` and `adata.uns`.
Returns:
adata : AnnData object
Updated AnnData object with the results stored in `adata.obs ['spatial_pscore']` and `adata.uns ['spatial_pscore']`.
Example:
```python
# Calculate the score for proximity between `Tumor CD30+` cells and `M2 Macrophages`
adata = sm.tl.spatial_pscore (adata,proximity= ['Tumor CD30+', 'M2 Macrophages'], score_by = 'ImageId',
x_coordinate='X_position',y_coordinate='Y_position',
phenotype='phenotype',method='radius',radius=20,knn=3,
imageid='ImageId',subset=None, label='spatial_pscore')
```
"""
# Start
def spatial_pscore_internal (adata_subset,proximity,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_ind = neighbours.copy() # 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
neighbours_ind = neighbours.copy() # neighbour DF
# Map phenotype
phenomap = dict(zip(list(range(len(ind))), data['phenotype'])) # Used for mapping
phenomap_ind = dict(zip(list(range(len(ind))), data.index)) # Used for mapping cell_nme
# Loop through (all functionized methods were very slow)
for i in neighbours.columns:
neighbours[i] = neighbours[i].dropna().map(phenomap, na_action='ignore')
# do the same index and cell name
for i in neighbours_ind.columns:
neighbours_ind[i] = neighbours_ind[i].dropna().map(phenomap_ind, na_action='ignore')
# Idetify all the neighbourhoods that contains the user defined proximity phenotypes
for i in proximity:
print (str('Finding neighbourhoods with ') + str(i))
nn = neighbours[neighbours.isin([i])].dropna(how='all').index
neighbours = neighbours.loc[nn]
# Identify all the cells that was part of the neighbourhood in this analysis
neighbours_ind = neighbours_ind.loc[neighbours.index]
neighbours_ind_unique = pd.unique(neighbours_ind.values.ravel())
# subset the neighbourhood cells to include only the cells in the user defined list
cleaned_neighbours_ind_unique = [x for x in neighbours_ind_unique if str(x) != 'nan']
d = data.loc[cleaned_neighbours_ind_unique]
d = d[d['phenotype'].isin(proximity)].index
# return neighbours for score and image_neighbours for plotting on image
return {'neighbours': neighbours.index, 'image_neighbours': d }
# 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_pscore_internal = lambda x: spatial_pscore_internal(adata_subset=x,proximity=proximity,
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_pscore_internal, adata_list)) # Apply function
# Merge all the results into a single dataframe
proximity_site_cells = np.concatenate([d['image_neighbours'] for d in all_data], axis=0)
# Add it to the AnnData Object
adata.obs[label] = np.where(adata.obs.index.isin(proximity_site_cells), '_'.join(proximity), "other")
##### SCORING #####
proximity_neigh = np.concatenate([d['neighbours'] for d in all_data], axis=0)
wh_d = adata.obs
wh_d[label] = np.where(wh_d.index.isin(proximity_neigh), '_'.join(proximity), "other")
# Define a scoring system
name = '_'.join(proximity)
whole_data = wh_d[[score_by, label, phenotype]]
# proximity volume
p_v = whole_data.groupby([score_by, label]).size().unstack().fillna(0)
p_v ['All Cells'] = p_v[name] + p_v["other"]
p_v['Proximity Volume'] = p_v[name] / p_v['All Cells']
p_v = p_v.fillna(0) # replace NA
p_v = p_v.replace([np.inf, -np.inf], 0)
p_v = p_v.drop(columns = 'other')
# subset the phenotypes of interest
w_d = whole_data[whole_data[phenotype].isin(proximity)]
# proximity density
p_d = w_d.groupby([score_by, label]).size().unstack().fillna(0)
p_d ['Celltype of interest'] = p_d[name] + p_d["other"]
p_d['Proximity Density'] = p_d[name] / p_d['Celltype of interest']
p_d = p_d.fillna(0) # replace NA
p_d = p_d.replace([np.inf, -np.inf], 0)
p_d = p_d.drop(columns = ['other', name])
# Merge Promimity volumne and density
proximity_score = pd.merge(p_v, p_d, left_index=True, right_index=True)
# Add it to the anndata object
adata.uns[label] = proximity_score
# Print
print("Please check:\nadata.obs['" + str(label) + "'] &\nadata.uns['"+ str(label) + "'] for results")
# Return
return adata