Merge pull request #289 from BiomedicalMachineLearning/fix_bug/cci

fix: fix several bugs of CCI and zoom

stlearn/image_preprocessing/segmentation.py

@@ -158,7 +158,7 @@ def _calculate_morph_stats(tile_path):
     min_nucleus_area = 60
     im_nuclei_seg_mask = htk.segmentation.label.area_open(
         labels, min_nucleus_area
-    ).astype(np.int)
+    ).astype(np.int64)
 
     # compute nuclei properties
     objProps = skimage.measure.regionprops(im_nuclei_seg_mask)

stlearn/plotting/cci_plot_helpers.py

@@ -198,9 +198,11 @@ def rank_scatter(
                 y[ranks_],
                 alpha=alpha,
                 c=highlight_color,
-                s=None
-                if type(point_sizes) == type(None)
-                else (point_sizes[ranks_] ** point_size_exp),
+                s=(
+                    None
+                    if type(point_sizes) == type(None)
+                    else (point_sizes[ranks_] ** point_size_exp)
+                ),
                 edgecolors=color,
             )
             ranks = ranks_ if not show_all else ranks
@@ -754,7 +756,16 @@ def chordDiagram(X, ax, colors=None, width=0.1, pad=2, chordwidth=0.7, lim=1.1):
         # This draws the outter ring #
         # IdeogramArc(start=start, end=end, radius=1.0, ax=ax,
         #            color=colors[i], width=width)
-        a = Arc((0, 0), diam, diam, 0, start, end, color=colors[i], lw=10)
+        a = Arc(
+            xy=(0, 0),
+            width=diam,
+            height=diam,
+            angle=0,
+            theta1=start,
+            theta2=end,
+            color=colors[i],
+            lw=10,
+        )
         ax.add_patch(a)
         start, end = pos[(i, i)]
         # This draws the paths to itself #

stlearn/plotting/classes.py

@@ -194,7 +194,7 @@ def _crop_image(self, main_ax: _AxesSubplot, margin: float):
     def _zoom_image(self, main_ax: _AxesSubplot, zoom_coord: Optional[float]):
 
         main_ax.set_xlim(zoom_coord[0], zoom_coord[1])
-        main_ax.set_ylim(zoom_coord[2], zoom_coord[3])
+        main_ax.set_ylim(zoom_coord[3], zoom_coord[2])
 
     def _add_color_bar(self, plot, color_bar_label: str = ""):
         cb = plt.colorbar(

stlearn/tools/microenv/cci/base.py

@@ -16,7 +16,6 @@ def lr(
     neighbours: list = None,
     fast: bool = True,
 ) -> AnnData:
-
     """Calculate the proportion of known ligand-receptor co-expression among the neighbouring spots or within spots
     Parameters
     ----------
@@ -111,7 +110,7 @@ def get_lrs_scores(
     lrs: np.array   lr pairs from the database in format ['L1_R1', 'LN_RN']
     """
     if type(spot_indices) == type(None):
-        spot_indices = np.array(list(range(len(adata))), dtype=np.int_)
+        spot_indices = np.array(list(range(len(adata))), dtype=np.int32)
 
     spot_lr1s = get_spot_lrs(
         adata, lr_pairs=lrs, lr_order=True, filter_pairs=filter_pairs
@@ -208,7 +207,7 @@ def calc_neighbours(
                 distance,
             )
             if index:
-                n_index = np.array(n_index, dtype=np.int_)
+                n_index = np.array(n_index, dtype=np.int32)
                 neighbours.append(n_index[n_index != i])
             else:
                 n_spots = adata.obs_names[n_index]
@@ -286,6 +285,7 @@ def lr_pandas(
     -------
     lr_scores: numpy.ndarray   Cells*LR-scores.
     """
+
     # function to calculate mean of lr2 expression between neighbours or within spot (distance==0) for each spot
     def mean_lr2(x):
         # get lr2 expressions from the neighbour(s)
@@ -352,7 +352,6 @@ def lr_grid(
     radius: int = 1,
     verbose: bool = True,
 ) -> AnnData:
-
     """Calculate the proportion of known ligand-receptor co-expression among the neighbouring grids or within each grid
     Parameters
     ----------

stlearn/tools/microenv/cci/het.py

@@ -348,7 +348,7 @@ def get_interactions(
 
         A_gene1_sig_bool = np.logical_and(A_gene1_bool, sig_bool)
         n_true = A_gene1_sig_bool.sum()
-        A_gene1_sig_indices = np.zeros((1, n_true), dtype=np.int_)[
+        A_gene1_sig_indices = np.zeros((1, n_true), dtype=np.int32)[
             0, :
         ]  # np.where(A_gene1_sig_bool)[0]
         index = 0

stlearn/tools/microenv/cci/het_helpers.py

@@ -218,7 +218,7 @@ def get_data_for_counting(adata, use_label, mix_mode, all_set):
         cell_data = np.zeros((len(cell_labels), len(all_set)), dtype=np.float64)
         for i, cell_type in enumerate(all_set):
             cell_data[:, i] = (
-                (cell_labels == cell_type).astype(np.int_).astype(np.float64)
+                (cell_labels == cell_type).astype(np.int32).astype(np.float64)
             )
 
     spot_bcs = adata.obs_names.values.astype(str)
@@ -254,7 +254,7 @@ def get_data_for_counting_OLD(adata, use_label, mix_mode, all_set):
         cell_data = np.zeros((len(cell_labels), len(all_set)), dtype=np.float64)
         for i, cell_type in enumerate(all_set):
             cell_data[:, i] = (
-                (cell_labels == cell_type).astype(np.int_).astype(np.float64)
+                (cell_labels == cell_type).astype(np.int32).astype(np.float64)
             )
 
     spot_bcs = adata.obs_names.values.astype(str)

stlearn/tools/microenv/cci/permutation.py

@@ -13,6 +13,7 @@
 from .merge import merge
 from .perm_utils import get_lr_features, get_lr_bg
 
+
 # Newest method #
 def perform_spot_testing(
     adata: AnnData,
@@ -74,7 +75,7 @@ def perform_spot_testing(
     ######## Background per LR, but only for spots where LR has a score ########
     # Determine the indices of the spots where each LR has a score #
     cols = ["n_spots", "n_spots_sig", "n_spots_sig_pval"]
-    lr_summary = np.zeros((lr_scores.shape[1], 3), np.int)
+    lr_summary = np.zeros((lr_scores.shape[1], 3), np.int32)
     pvals = np.ones(lr_scores.shape, dtype=np.float64)
     pvals_adj = np.ones(lr_scores.shape, dtype=np.float64)
     log10pvals_adj = np.zeros(lr_scores.shape, dtype=np.float64)
@@ -349,7 +350,6 @@ def permutation(
     background: np.array = None,
     **kwargs,
 ) -> AnnData:
-
     """Permutation test for merged result
     Parameters
     ----------

stlearn/wrapper/read.py

@@ -1,5 +1,6 @@
 """Reading and Writing
 """
+
 from pathlib import Path, PurePath
 from typing import Optional, Union
 from anndata import AnnData
@@ -27,7 +28,6 @@ def Read10X(
     quality: _QUALITY = "hires",
     image_path: Union[str, Path] = None,
 ) -> AnnData:
-
     """\
     Read Visium data from 10X (wrap read_visium from scanpy)
 
@@ -209,7 +209,6 @@ def ReadOldST(
     quality: str = "hires",
     spot_diameter_fullres: float = 50,
 ) -> AnnData:
-
     """\
     Read Old Spatial Transcriptomics data
 
@@ -258,7 +257,6 @@ def ReadSlideSeq(
     spot_diameter_fullres: float = 50,
     background_color: _background = "white",
 ) -> AnnData:
-
     """\
     Read Slide-seq data
 
@@ -340,7 +338,6 @@ def ReadMERFISH(
     spot_diameter_fullres: float = 50,
     background_color: _background = "white",
 ) -> AnnData:
-
     """\
     Read MERFISH data
 
@@ -423,7 +420,6 @@ def ReadSeqFish(
     spot_diameter_fullres: float = 50,
     background_color: _background = "white",
 ) -> AnnData:
-
     """\
     Read SeqFish data
 
@@ -510,7 +506,6 @@ def ReadXenium(
     spot_diameter_fullres: float = 15,
     background_color: _background = "white",
 ) -> AnnData:
-
     """\
     Read Xenium data