Sm.pl.image viewer
Short Description
sm.pl.image_viewer: The function allows users to open OME-TIFF images inside
Napari and overlay any any categorical column such as cluster annotation or phenotypes.
Function
image_viewer(image_path, adata, overlay=None, flip_y=True, overlay_category=None, markers=None, channel_names='default', x_coordinate='X_centroid', y_coordinate='Y_centroid', point_size=10, point_color=None, subset=None, imageid='imageid', seg_mask=None, **kwargs)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image_path |
string |
required | |
seg_mask |
string |
None |
|
adata |
AnnData Object |
required | |
flip_y |
bool, optional |
True |
|
overlay |
string, optional |
None |
|
overlay_category |
list, optional |
None |
|
markers |
list, optional |
None |
|
channel_names |
list, optional |
'default' |
|
x_coordinate |
string, optional |
'X_centroid' |
|
y_coordinate |
string, optional |
'Y_centroid' |
|
point_size |
int, optional |
10 |
|
imageid |
string, optional |
'imageid' |
|
subset |
string, optional |
None |
Returns:
| Type | Description |
|---|---|
Napari Viewer. |
Examples:
1 2 3 4 | |
Source code in scimap/plotting/_image_viewer.py
def image_viewer (image_path, adata, overlay=None, flip_y=True,
overlay_category=None,markers=None,channel_names='default',
x_coordinate='X_centroid',y_coordinate='Y_centroid',point_size=10,
point_color=None,subset=None,imageid='imageid',seg_mask=None,**kwargs):
"""
Parameters:
image_path : string
Location to the image file (TIFF, OME.TIFF, ZARR supported)
seg_mask: string
Location to the segmentation mask file.
adata : AnnData Object
flip_y : bool, optional
Flip the Y-axis if needed. Some algorithms output the XY with the Y-coordinates flipped.
If the image overlays do not align to the cells, try again by setting this to `False`.
overlay : string, optional
Name of the column with any categorical data such as phenotypes or clusters.
overlay_category : list, optional
If only specfic categories within the overlay column is needed, pass their names as a list.
If None, all categories will be used.
markers : list, optional
Markers to be included. If none, all markers will be displayed.
channel_names : list, optional
List of channels in the image in the exact order as image. The default is `adata.uns['all_markers']`
x_coordinate : string, optional
X axis coordinate column name in AnnData object.
y_coordinate : string, optional
Y axis coordinate column name in AnnData object.
point_size : int, optional
point size in the napari plot.
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. Only useful when multiple images are being analyzed together.
**kwargs
Other arguments that can be passed to napari viewer
Returns:
Napari Viewer.
Example:
```python
image_path = '/Users/aj/Desktop/ptcl_tma/image.ome.tif'
sm.pl.image_viewer (image_path, adata, overlay='phenotype',overlay_category=None,
markers=['CD31', "CD3D","DNA11",'CD19','CD45','CD163','FOXP3'],
point_size=7,point_color='white')
```
"""
#TODO
# - ADD Subset markers for ZARR ssection
# - Ability to use ZARR metadata if available
# adding option to load just the image without an adata object
if adata is None:
channel_names = None
else:
# All operations on the AnnData object is performed first
# Plot only the Image that is requested
if subset is not None:
adata = adata[adata.obs[imageid] == subset]
# Recover the channel names from adata
if channel_names == 'default':
channel_names = adata.uns['all_markers']
else:
channel_names = channel_names
# Index of the marker of interest and corresponding names
if markers is None:
idx = list(range(len(channel_names)))
channel_names = channel_names
else:
idx = []
for i in markers:
idx.append(list(channel_names).index(i))
channel_names = markers
# Load the segmentation mask
if seg_mask is not None:
seg_m = tiff.imread(seg_mask)
if (len(seg_m.shape) > 2) and (seg_m.shape[0] > 1):
seg_m = seg_m[0]
# Operations on the OME TIFF image is performed next
# check the format of image
if os.path.isfile(image_path) is True:
image = tiff.TiffFile(image_path, is_ome=False) #is_ome=False
z = zarr.open(image.aszarr(), mode='r') # convert image to Zarr array
# Identify the number of pyramids
n_levels = len(image.series[0].levels) # pyramid
# If and if not pyramids are available
if n_levels > 1:
pyramid = [da.from_zarr(z[i]) for i in range(n_levels)]
multiscale = True
else:
pyramid = da.from_zarr(z)
multiscale = False
# subset channels of interest
if markers is not None:
if n_levels > 1:
for i in range(n_levels-1):
pyramid[i] = pyramid[i][idx, :, :]
n_channels = pyramid[0].shape[0] # identify the number of channels
else:
pyramid = pyramid[idx, :, :]
n_channels = pyramid.shape[0] # identify the number of channels
else:
if n_levels > 1:
n_channels = pyramid[0].shape[0]
else:
n_channels = pyramid.shape[0]
# check if channel names have been passed to all channels
if channel_names is not None:
assert n_channels == len(channel_names), (
f'number of channel names ({len(channel_names)}) must '
f'match number of channels ({n_channels})'
)
# Load the viewer
viewer = napari.view_image(
pyramid, multiscale=multiscale, channel_axis=0,
visible=False,
name = None if channel_names is None else channel_names,
#**kwargs
)
# Operations on the ZARR image
# check the format of image
if os.path.isfile(image_path) is False:
#print(image_path)
viewer = napari.Viewer()
viewer.open(image_path, multiscale=True,
visible=False,
name = None if channel_names is None else channel_names)
# Add the seg mask
if seg_mask is not None:
viewer.add_labels(seg_m, name='segmentation mask', visible=False)
# Add phenotype layer function
def add_phenotype_layer (adata, overlay, phenotype_layer,x,y,viewer,point_size,point_color):
coordinates = adata[adata.obs[overlay] == phenotype_layer]
# Flip Y AXIS if needed
if flip_y is True:
coordinates = pd.DataFrame({'y': coordinates.obs[y],'x': coordinates.obs[x]})
else:
coordinates = pd.DataFrame({'x': coordinates.obs[x],'y': coordinates.obs[y]})
#points = coordinates.values.tolist()
points = coordinates.values
if point_color is None:
r = lambda: random.randint(0,255) # random color generator
point_color = '#%02X%02X%02X' % (r(),r(),r()) # random color generator
viewer.add_points(points, size=point_size,face_color=point_color,visible=False,name=phenotype_layer)
if overlay is not None:
# categories under investigation
if overlay_category is None:
available_phenotypes = list(adata.obs[overlay].unique())
else:
available_phenotypes = overlay_category
# Run the function on all phenotypes
for i in available_phenotypes:
add_phenotype_layer (adata=adata, overlay=overlay,
phenotype_layer=i, x=x_coordinate, y=y_coordinate, viewer=viewer,
point_size=point_size,point_color=point_color)