scimap.pl.gate_finder

Short description

The function helps to identify manual gates for each marker by overlaying the predicted positive cells on the image. For each marker from_gate, to_gate and an increment can be passed to identify predicted positive cells for multiple gates. All of these are overlayed on the image to identify the best gate visually.

Parameters

image_path : string
Location to the image file.

adata : AnnData Object

marker_of_interest : string
Marker for which gate is to be defined e.g. 'CD45'.

from_gate : int, optional (The default is 6)
Start value gate of interest.

to_gate : int, optional (The default is 8)
End value of the gate of interest.

increment : float, optional (The default is 0.1)
Increments between the start and end values.

markers : string, optional (The default is None)
Additional markers to be included in the image for evaluation.

channel_names : list, optional (The default is adata.uns['all_markers'])
List of channels in the image in the exact order as image.

x_coordinate : string, optional (The default is 'X_centroid')
X axis coordinate column name in AnnData object.

y_coordinate : string, optional (The default is 'Y_centroid')
Y axis coordinate column name in AnnData object.

point_size : int, optional (The default is 10)
point size in the napari plot.

imageid : string, optional (The default is imageid)
Column name of the column containing the image id.

subset : string, optional (The default is None)
imageid of a single image to be subsetted for analyis. Only useful when multiple images are being analyzed together.

seg_mask : string, optional (The default is None)
Location to the segmentation mask file.

**kwargs : None
Other arguments that can be passed to napari viewer.

Returns

napari image viewer loads the image with the predicted cells that are positive for the given marker and gate.

Example

image_path = '/Users/aj/Desktop/ptcl_tma/image.tif'
sm.pl.gate_finder (image_path, adata, marker_of_interest='CD45',
             from_gate = 6, to_gate = 8, increment = 0.1,
             markers=['DNA10', 'CD20'], image_id= '77', seg_mask=None)