Sm.pl.spatial interaction
Short Description
sm.pl.spatial_interaction: The function allows users to plot a heatmap to visualize spatial interaction output.
The intensity represents abundance of co-occurrence (scaled) observed and blank regions represent non-significant results.
Function
spatial_interaction(adata, spatial_interaction='spatial_interaction', summarize_plot=True, p_val=0.05, row_cluster=False, col_cluster=False, cmap='vlag', nonsig_color='grey', subset_phenotype=None, subset_neighbour_phenotype=None, binary_view=False, return_data=False, **kwargs)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
adata |
AnnData object |
required | |
spatial_interaction |
string, optional |
'spatial_interaction' |
|
summarize_plot |
bool, optional |
True |
|
p_val |
float, optional |
0.05 |
|
row_cluster |
bool, optional |
False |
|
col_cluster |
bool, optional |
False |
|
subset_phenotype |
list, optional |
None |
|
subset_neighbour_phenotype |
list, optional |
None |
|
cmap |
string, optional |
'vlag' |
|
nonsig_color |
string, optional |
'grey' |
|
binary_view |
bool, optional |
False |
|
return_data |
bool, optional |
False |
|
**kwargs |
Pass other parameters that works with |
{} |
Examples:
1 2 3 4 5 6 7 8 | |
Source code in scimap/plotting/_spatial_interaction.py
def spatial_interaction (adata, spatial_interaction='spatial_interaction',
summarize_plot=True, p_val=0.05,
row_cluster=False, col_cluster=False,
cmap = 'vlag', nonsig_color='grey',
subset_phenotype=None, subset_neighbour_phenotype=None,
binary_view=False, return_data=False, **kwargs):
"""
Parameters:
adata : AnnData object
spatial_interaction : string, optional
In order to locate the spatial_interaction data within the AnnData object please provide the output
label/columnname of `sm.tl.spatial_interaction` function.
summarize_plot : bool, optional
In the event of analyzing multiple images, this argument allows users to
plot the average cell-cell interaction across all images.
p_val : float, optional
P-value cut-off above which interactions are not considered significant.
row_cluster : bool, optional
Cluster Rows.
col_cluster : bool, optional
Cluster Columns.
subset_phenotype : list, optional
If user requires to visualize a subset of phenotypes, it can be passed here.
e.g. `subset_phenotype = ['celltype_A', 'celltype_B']`.
subset_neighbour_phenotype : list, optional
If user requires to visualize a subset of interacting phenotypes, it can be passed here.
e.g. `subset_neighbour_phenotype = ['celltype_C', 'celltype_D']`.
cmap : string, optional
Color map to use for continous variables.
Can be a name or a Colormap instance (e.g. 'magma', 'viridis').
nonsig_color : string, optional
Color for non-significant interactions (Interactions above the P-value cut-off will use this color).
binary_view : bool, optional
Removes the intensity of intreaction and plots significant interactions and avoidance in a binary format.
return_data : bool, optional
When True, return the data used for plotting.
**kwargs : key:value pairs
Pass other parameters that works with `sns.clustermap`. e.g. `linecolor='black'`
Example:
```python
# spatial_interaction heatmap for a single image
sm.pl.spatial_interaction(adata, summarize_plot=True,
row_cluster=True, linewidths=0.75, linecolor='black')
# spatial_interaction heatmap for multiple images
sns.set(font_scale=0.6)
sm.pl.spatial_interaction(adata, summarize_plot=False,
row_cluster=True, col_cluster=True, yticklabels=True)
```
"""
# set color for heatmap
#cmap_updated = copy.copy(matplotlib.cm.get_cmap(cmap))
cmap_updated = matplotlib.cm.get_cmap(cmap)
cmap_updated.set_bad(color=nonsig_color)
# Copy the interaction results from anndata object
try:
interaction_map = adata.uns[spatial_interaction].copy()
except KeyError:
raise ValueError('spatial_interaction not found- Please run sm.tl.spatial_interaction first')
# subset the data if user requests
if subset_phenotype is not None:
if isinstance(subset_phenotype, str):
subset_phenotype = [subset_phenotype]
# subset the phenotype
interaction_map = interaction_map[interaction_map['phenotype'].isin(subset_phenotype)]
if subset_neighbour_phenotype is not None:
if isinstance(subset_neighbour_phenotype, str):
subset_neighbour_phenotype = [subset_neighbour_phenotype]
# subset the phenotype
interaction_map = interaction_map[interaction_map['neighbour_phenotype'].isin(subset_neighbour_phenotype)]
# Seperate Interaction intensity from P-value
p_value = interaction_map.filter(regex='pvalue_')
p_val_df = pd.concat([interaction_map[['phenotype','neighbour_phenotype']], p_value], axis=1, join='outer')
p_val_df = p_val_df.set_index(['phenotype','neighbour_phenotype'])
interaction_map = interaction_map[interaction_map.columns.difference(p_value.columns)]
interaction_map = interaction_map.set_index(['phenotype','neighbour_phenotype'])
# Binarize the values if user requests
if binary_view == True:
interaction_map[interaction_map > 0] = 1
interaction_map[interaction_map <= 0] = -1
if summarize_plot == True:
# convert first two columns to multi-index column
#interaction_map = interaction_map.set_index(['phenotype','neighbour_phenotype'])
#p_val_df = p_val_df.set_index(['phenotype','neighbour_phenotype'])
# If multiple images are present, take the average of interactions
interaction_map['mean'] = interaction_map.mean(axis=1).values
interaction_map = interaction_map[['mean']] # keep only the mean column
interaction_map = interaction_map['mean'].unstack()
# Do the same for P-values
p_val_df['mean'] = p_val_df.mean(axis=1).values
p_val_df = p_val_df[['mean']] # keep only the mean column
# set the P-value threshold
p_val_df.loc[p_val_df[p_val_df['mean'] > p_val].index,'mean'] = np.NaN
p_val_df = p_val_df['mean'].unstack()
# change to the order passed in subset
if subset_phenotype is not None:
interaction_map = interaction_map.reindex(subset_phenotype)
p_val_df = p_val_df.reindex(subset_phenotype)
if subset_neighbour_phenotype is not None:
interaction_map = interaction_map.reindex(columns=subset_neighbour_phenotype)
p_val_df = p_val_df.reindex(columns=subset_neighbour_phenotype)
# Plotting heatmap
mask = p_val_df.isnull() # identify the NAN's for masking
im = interaction_map.fillna(0) # replace nan's with 0 so that clustering will work
# heatmap
sns.clustermap(im, cmap=cmap, row_cluster=row_cluster, col_cluster=col_cluster, mask=mask, **kwargs)
else:
if len(interaction_map.columns) <= 3:
raise ValueError('Data for only a single image is available please set summarize_plot=True and try again')
# convert first two columns to multi-index column
#interaction_map = interaction_map.set_index(['phenotype','neighbour_phenotype'])
#p_val_df = p_val_df.set_index(['phenotype','neighbour_phenotype'])
# P value threshold
p_val_df = p_val_df.apply(lambda x: np.where(x > p_val,np.nan,x))
# Remove rows that are all nan
idx = p_val_df.index[p_val_df.isnull().all(1)] # Find all nan rows
interaction_map = interaction_map.loc[interaction_map.index.difference(idx)] # clean intensity data
p_val_df = p_val_df.loc[p_val_df.index.difference(idx)] # clean p-value data
# order the plot as needed
if subset_phenotype or subset_neighbour_phenotype is not None:
interaction_map.reset_index(inplace=True)
p_val_df.reset_index(inplace=True)
if subset_phenotype is not None:
interaction_map['phenotype'] = interaction_map['phenotype'].astype('category')
interaction_map['phenotype'] = interaction_map['phenotype'].cat.reorder_categories(subset_phenotype)
interaction_map = interaction_map.sort_values('phenotype')
# Do same for Pval
p_val_df['phenotype'] = p_val_df['phenotype'].astype('category')
p_val_df['phenotype'] = p_val_df['phenotype'].cat.reorder_categories(subset_phenotype)
p_val_df = p_val_df.sort_values('phenotype')
if subset_neighbour_phenotype is not None:
interaction_map['neighbour_phenotype'] = interaction_map['neighbour_phenotype'].astype('category')
interaction_map['neighbour_phenotype'] = interaction_map['neighbour_phenotype'].cat.reorder_categories(subset_neighbour_phenotype)
interaction_map = interaction_map.sort_values('neighbour_phenotype')
# Do same for Pval
p_val_df['neighbour_phenotype'] = p_val_df['neighbour_phenotype'].astype('category')
p_val_df['neighbour_phenotype'] = p_val_df['neighbour_phenotype'].cat.reorder_categories(subset_neighbour_phenotype)
p_val_df = p_val_df.sort_values('neighbour_phenotype')
if subset_phenotype and subset_neighbour_phenotype is not None:
interaction_map = interaction_map.sort_values(['phenotype', 'neighbour_phenotype'])
p_val_df = p_val_df.sort_values(['phenotype', 'neighbour_phenotype'])
# convert the data back into multi-index
interaction_map = interaction_map.set_index(['phenotype', 'neighbour_phenotype'])
p_val_df = p_val_df.set_index(['phenotype', 'neighbour_phenotype'])
# Plotting heatmap
mask = p_val_df.isnull() # identify the NAN's for masking
im = interaction_map.fillna(0) # replace nan's with 0 so that clustering will work
mask.columns = im.columns
# covert the first two columns into index
# Plot
sns.clustermap(im, cmap=cmap, row_cluster=row_cluster, col_cluster=col_cluster, mask=mask, **kwargs)
if return_data is True:
# perpare data for export
map_data = interaction_map.copy()
p_val_data = mask.copy()
map_data.reset_index(inplace=True)
p_val_data.reset_index(inplace=True)
# remove the first two colums
map_data = map_data.drop(['phenotype','neighbour_phenotype'],axis=1)
p_val_data = p_val_data.drop(['phenotype','neighbour_phenotype'],axis=1)
p_val_data.columns = map_data.columns
# remove the mased values
final_Data = map_data.where(~p_val_data, other=np.nan)
final_Data.index = interaction_map.index
return final_Data