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gate_finder doc strings
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@ajitjohnson
ajitjohnson committed Mar 1, 2024
1 parent 172d5f6 commit 0eb086b
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28 changes: 14 additions & 14 deletions scimap/plotting/_foldchange.py
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Expand Up @@ -91,24 +91,24 @@ def foldchange (adata,
If True, returns the data frame used for plotting instead of the plot.
Returns:
Dataframe/ plot (pandas, matplotlib):
If `return_data` is True, returns a pandas DataFrame used for plotting. Otherwise, displays the plot.
Dataframe; plot (pandas, matplotlib):
If `return_data` is True, returns a pandas DataFrame used for plotting. Otherwise, displays the plot.
Example:
```python
```python
# Generate a heatmap of fold changes with custom settings
sm.pl.foldchange(adata, label='foldchange', method='heatmap', cmap='coolwarm', log=True,
p_val=0.05, nonsig_color='lightgrey', xticks_rotation=45)
# Generate a heatmap of fold changes with custom settings
sm.pl.foldchange(adata, label='foldchange', method='heatmap', cmap='coolwarm', log=True,
p_val=0.05, nonsig_color='lightgrey', xticks_rotation=45)
# Create a parallel coordinates plot to visualize fold changes across groups
sm.pl.foldchange(adata, label='foldchange', method='parallel_coordinates', log=True,
parallel_coordinates_color=['red', 'blue', 'green'], invert_axis=True)
# Return the data frame used for fold change visualization
df_foldchange = sm.pl.foldchange(adata, label='foldchange', return_data=True)
# Create a parallel coordinates plot to visualize fold changes across groups
sm.pl.foldchange(adata, label='foldchange', method='parallel_coordinates', log=True,
parallel_coordinates_color=['red', 'blue', 'green'], invert_axis=True)
```
# Return the data frame used for fold change visualization
df_foldchange = sm.pl.foldchange(adata, label='foldchange', return_data=True)
```
"""

# set color for heatmap
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106 changes: 61 additions & 45 deletions scimap/plotting/_gate_finder.py
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Expand Up @@ -4,8 +4,11 @@
# @author: Ajit Johnson Nirmal
"""
!!! abstract "Short Description"
`sm.pl.gate_finder`: The function opens the OME-TIFF image inside Napari and overlays points to help with the
identifying manual gates for each marker. Use the `sm.pp.rescale` function to apply the identified gates to your data.
`sm.pl.gate_finder`: This function leverages Napari to display OME-TIFF images,
overlaying points that assist in manually determining gating thresholds for specific markers.
By visualizing marker expression spatially, users can more accurately define gates.
Subsequently, the identified gating parameters can be applied to the dataset using `sm.pp.rescale`,
enabling precise control over data segmentation and analysis based on marker expression levels.
## Function
"""
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**kwargs,
):
"""
Parameters:
Parameters:
image_path (str):
Location to the image file.
Path to the high-resolution image file (supports formats like TIFF, OME.TIFF).
adata : Ann Data Object
adata (anndata.AnnData):
The annotated data matrix.
marker_of_interest (str):
Marker for which gate is to be defined e.g. 'CD45'.
The target marker for which the gating threshold is to be determined.
layer (str):
The layer in adata.layers that contains the expression data to gate.
If None, adata.X is used. use `raw` to use the data stored in `adata.raw.X`
layer (str, optional):
Specifies the layer in `adata` containing expression data. Defaults to 'raw' for `adata.raw.X`.
log (bool):
Log transform the data before gating.
log (bool, optional):
Applies log transformation to expression data if set to True. Defaults to True.
from_gate (int):
Start value gate of interest.
from_gate (int, optional):
Starting gate threshold value for the marker of interest. Defaults to 6.
to_gate (int):
End value of the gate of interest.
to_gate (int, optional):
Ending gate threshold value for the marker of interest. Defaults to 8.
flip_y (bool):
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`.
increment (float, optional):
Incremental step size between `from_gate` and `to_gate`. Defaults to 0.1.
increment (float):
Increments between the start and end values.
markers (list, optional):
A list of additional markers to include in visualization for context.
markers (str):
Additional markers to be included in the plot for evaluation.
channel_names (list or str, optional):
Names of the channels in the image, in order. Defaults to 'default', using `adata.uns['all_markers']`.
channel_names (list):
List of channels in the image in the exact order as image. The default is `adata.uns['all_markers']`
flip_y (bool, optional):
Inverts the Y-axis to match image coordinates if set to True. Defaults to True.
x_coordinate (str):
X axis coordinate column name in AnnData object.
x_coordinate, y_coordinate (str, optional):
Columns in `adata.obs` specifying cell coordinates. Defaults are 'X_centroid' and 'Y_centroid'.
y_coordinate (str):
Y axis coordinate column name in AnnData object.
point_size (int, optional):
Size of points in the visualization. Defaults to 10.
point_size (int):
point size in the napari plot.
imageid (str, optional):
Column in `adata.obs` identifying images for datasets with multiple images. Defaults to 'imageid'.
imageid (str):
Column name of the column containing the image id.
subset (str, optional):
Specific image identifier for targeted analysis, typically an image ID. Defaults to None.
subset (str):
imageid of a single image to be subsetted for analyis.
seg_mask (str, optional):
Path to a segmentation mask file to overlay. Defaults to None.
seg_mask (str):
Location to the segmentation mask file.
**kwargs:
Additional arguments passed to the visualization tool.
**kwargs
Other arguments that can be passed to napari viewer.
Returns:
Image (napari):
Displays the visualization using napari viewer.
Example:
Example:
```python
image_path = '/Users/aj/Desktop/ptcl_tma/image.ome.tif'
sm.pl.gate_finder (image_path, adata, marker_of_interest='CD45',
from_gate = 6, to_gate = 8, increment = 0.1,
markers=['DNA10'], channel_names = 'default',
x_coordinate='X_position',y_coordinate='Y_position',point_size=10,
subset= '77', seg_mask=None)
# Visualize gating thresholds for CD45 on a specific image
sm.pl.gate_finder(
image_path='/path/to/image.ome.tif', adata=adata, marker_of_interest='CD45',
from_gate=4, to_gate=10, increment=0.2, flip_y=False, point_size=12,
subset='Sample1', seg_mask='/path/to/seg_mask.tif')
# Log-transformed gating for a marker with additional markers and custom channel names
sm.pl.gate_finder(
image_path='/path/to/image.ome.tif', adata=adata, marker_of_interest='CD3',
log=True, from_gate=3, to_gate=7, increment=0.1, markers=['CD19', 'CD4'],
channel_names=['DAPI', 'CD3', 'CD19', 'CD4'], point_size=15)
# Explore gating for multiple markers across different segments
sm.pl.gate_finder(
image_path='/path/to/image.ome.tif', adata=adata, marker_of_interest='CD8',
layer='expression', from_gate=5, to_gate=9, increment=0.05, markers=['CD8', 'PD1'],
subset='TumorRegion', seg_mask='/path/to/tumor_seg_mask.tif')
```
"""

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