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261 | 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
|