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update spatial_detection_scores to use bin_size instead of n_bins for… #29

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update spatial_detection_scores to use bin_size instead of n_bins for… #29

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a935581
update spatial_detection_scores to use bin_size instead of n_bins for…
shachafl Dec 11, 2024
9ae708b
reformat using black
shachafl Dec 11, 2024
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24 changes: 16 additions & 8 deletions spatial_compare/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@ def spatial_detection_scores(
query_name: str = "query data",
comparison_column="transcript_counts",
category="supercluster_name",
n_bins=50,
bin_size=100,
in_place=True,
non_spatial=False,
):
Expand All @@ -68,7 +68,7 @@ def spatial_detection_scores(
plot_stuff (bool, optional): Whether to plot the results. Defaults to True.
query_name (str, optional): The name of the query data. Defaults to "query data".
category (str, optional): The category column to compare. Defaults to "supercluster_name".
n_bins (int, optional): The number of bins for spatial grouping. Defaults to 50.
bin_size (int, optional): Bin size in microns for spatial grouping. Defaults to 100.
in_place (bool, optional): Whether to modify the query data in place. Defaults to True.
non_spatial (bool, optional): Whether to compare to an ungrouped mean/std. Defaults to False.

Expand Down Expand Up @@ -122,10 +122,18 @@ def spatial_detection_scores(
(s2.loc[s2[category] == c, [comparison_column]] / means[c]).values
)

# determine number of bins on each axis for grouping the data spatially
nx_bins = np.ceil((s2.x_centroid.max() - s2.x_centroid.min()) / bin_size).astype(
int
)
ny_bins = np.ceil((s2.y_centroid.max() - s2.y_centroid.min()) / bin_size).astype(
int
)

s2["xy_bucket"] = list(
zip(
pd.cut(s2.x_centroid, n_bins, labels=list(range(n_bins))),
pd.cut(s2.y_centroid, n_bins, labels=list(range(n_bins))),
pd.cut(s2.x_centroid, nx_bins, labels=list(range(nx_bins))),
pd.cut(s2.y_centroid, ny_bins, labels=list(range(ny_bins))),
)
)

Expand All @@ -137,10 +145,10 @@ def spatial_detection_scores(
log_ratio = s2.groupby("xy_bucket").log_10_detection_ratio.mean()
n_cells = s2.groupby("xy_bucket").x_centroid.count()

bin_image_z_score = np.zeros([n_bins, n_bins])
bin_image_difference = np.zeros([n_bins, n_bins])
bin_image_ratio = np.zeros([n_bins, n_bins])
bin_image_counts = np.zeros([n_bins, n_bins])
bin_image_z_score = np.zeros([nx_bins, ny_bins])
bin_image_difference = np.zeros([nx_bins, ny_bins])
bin_image_ratio = np.zeros([nx_bins, ny_bins])
bin_image_counts = np.zeros([nx_bins, ny_bins])

extent = [np.min(binx), np.max(binx), np.min(biny), np.max(biny)]
for coord in binx.index:
Expand Down
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