diff --git a/conda-scimap.yaml b/conda-scimap.yaml
new file mode 100644
index 0000000..58c4dd0
--- /dev/null
+++ b/conda-scimap.yaml
@@ -0,0 +1,43 @@
+# Setup:
+# mamba env create -f conda-scimap.yaml
+# mamba activate spatialcells-scimap
+# pip install --no-deps .
+# pip install --no-deps scimap==2.3.6 smart_open
+
+# scimap pins numba 0.58 / llvmlite 0.41 / scipy 1.12 / pandas <2.2 / numpy <2,
+# which strictly forces the rest of the stack to certain older versions captured here.
+
+name: spatialcells-scimap
+
+channels:
+ - conda-forge
+
+dependencies:
+ - python >=3.10,<3.12
+ - pip
+ - ipykernel
+ - nbconvert
+ - nbclient
+
+ # Core runtime (pinned to scimap-compatible versions)
+ - shapely >=2.0
+ - numpy <2
+ - pandas <2.2
+ - scipy =1.12.*
+ - scikit-learn >=1.6
+ - matplotlib <3.9
+ - seaborn
+ - tqdm
+ - leidenalg
+ - scanpy <1.11
+ - anndata <0.11
+
+ # Optional: for joint analyses with other packages (as was done in the tutorials)
+ - squidpy =1.4.*
+ - plotly
+ - numba =0.58.*
+ - llvmlite =0.41.*
+ - dask >=2023.11,<2024
+ - dask-image <2024
+ - zarr >=2.11,<3
+ - setuptools <81
diff --git a/conda.yaml b/conda.yaml
index d0bc246..81b4016 100644
--- a/conda.yaml
+++ b/conda.yaml
@@ -1,19 +1,35 @@
-name: spatial-cells-env
+# Setup:
+# mamba env create -f conda.yaml
+# mamba activate spatialcells
+# pip install --no-deps .
+
+# Use conda-scimap.yaml instead if you need scimap in the same env as well.
+
+name: spatialcells
channels:
- conda-forge
- - defaults
dependencies:
- - python>=3.7, <3.11
+ - python >=3.10,<3.13
+ - pip
- ipykernel
- - matplotlib>=3.7.0
- - pandas>=2.0.3
- - seaborn>=0.12.2
- - shapely>=2.0
+ - nbconvert
+ - nbclient
+
+ # Core runtime
+ - shapely >=2.0
+ - numpy
+ - pandas
+ - scipy
+ - scikit-learn
+ - matplotlib
+ - seaborn
- tqdm
- - pip
- - pip:
- - anndata==0.9.2
- - scanpy==1.9.4
-
\ No newline at end of file
+ - leidenalg
+ - scanpy
+ - anndata
+
+ # Optional: for joint analyses with other packages (as was done in the tutorials)
+ - squidpy
+ - plotly
diff --git a/spatialcells/__init__.py b/spatialcells/__init__.py
index 248828e..10eb3a5 100644
--- a/spatialcells/__init__.py
+++ b/spatialcells/__init__.py
@@ -1,8 +1,8 @@
-from pkg_resources import get_distribution, DistributionNotFound
+from importlib.metadata import version, PackageNotFoundError
try:
- __version__ = get_distribution("spatialcells").version
-except DistributionNotFound:
+ __version__ = version("spatialcells")
+except PackageNotFoundError:
__version__ = "(local)"
diff --git a/spatialcells/measurements/_getSlidingWindowsComposition.py b/spatialcells/measurements/_getSlidingWindowsComposition.py
index 7e63bf7..31764c4 100644
--- a/spatialcells/measurements/_getSlidingWindowsComposition.py
+++ b/spatialcells/measurements/_getSlidingWindowsComposition.py
@@ -1,3 +1,4 @@
+import anndata as ad
import numpy as np
import pandas as pd
@@ -26,33 +27,55 @@ def getSlidingWindowsComposition(
:returns: A dataframe containing the cell type composition of the region in each window
"""
if region_subset is None:
- cells_roi = adata
+ obs = adata.obs
else:
- cells_roi = adata[adata.obs[region_col].isin(region_subset)]
- cells_roi_maxx = int(cells_roi.obs["X_centroid"].max())
- cells_roi_maxy = int(cells_roi.obs["Y_centroid"].max())
- cells_roi_minx = int(cells_roi.obs["X_centroid"].min())
- cells_roi_miny = int(cells_roi.obs["Y_centroid"].min())
- all_windows_comp_df = []
- for x in range(cells_roi_minx, cells_roi_maxx + window_size, step_size):
- for y in range(cells_roi_miny, cells_roi_maxy + window_size, step_size):
- cells_roi_window = cells_roi[
- (cells_roi.obs["X_centroid"] >= x)
- & (cells_roi.obs["X_centroid"] < x + window_size)
- & (cells_roi.obs["Y_centroid"] >= y)
- & (cells_roi.obs["Y_centroid"] < y + window_size)
- ]
- if cells_roi_window.shape[0] > min_cells:
- cells_roi_window_composition = getRegionComposition(
- cells_roi_window, phenotype_col
- )
- cells_roi_window_composition["X_start"] = x
- cells_roi_window_composition["Y_start"] = y
- cells_roi_window_composition["window_size"] = window_size
- cells_roi_window_composition["step_size"] = step_size
- all_windows_comp_df.append(cells_roi_window_composition)
- all_windows_comp_df = pd.concat(all_windows_comp_df)
- return all_windows_comp_df
+ obs = adata.obs[adata.obs[region_col].isin(region_subset)]
+ obs = obs.dropna(subset=["X_centroid", "Y_centroid"])
+ if obs.empty:
+ return pd.DataFrame()
+
+ minx = int(obs["X_centroid"].min())
+ miny = int(obs["Y_centroid"].min())
+ maxx = int(obs["X_centroid"].max())
+ maxy = int(obs["Y_centroid"].max())
+
+ def composition_for(window_obs, x, y):
+ comp = getRegionComposition(
+ ad.AnnData(obs=window_obs), phenotype_col, regioncol=region_col
+ )
+ comp["X_start"] = x
+ comp["Y_start"] = y
+ return comp
+
+ rows = []
+ if window_size == step_size:
+ # speed up: each cell falls into exactly one window, so bucket once
+ # and group, rather than iterating all (x, y) pairs
+ x_bin = ((obs["X_centroid"] - minx) // step_size * step_size + minx).astype(int)
+ y_bin = ((obs["Y_centroid"] - miny) // step_size * step_size + miny).astype(int)
+ for (x, y), window_obs in obs.assign(_X_bin=x_bin, _Y_bin=y_bin).groupby(
+ ["_X_bin", "_Y_bin"], observed=True
+ ):
+ if window_obs.shape[0] > min_cells:
+ rows.append(composition_for(window_obs, int(x), int(y)))
+ else:
+ for x in range(minx, maxx + window_size, step_size):
+ for y in range(miny, maxy + window_size, step_size):
+ window_obs = obs[
+ (obs["X_centroid"] >= x)
+ & (obs["X_centroid"] < x + window_size)
+ & (obs["Y_centroid"] >= y)
+ & (obs["Y_centroid"] < y + window_size)
+ ]
+ if window_obs.shape[0] > min_cells:
+ rows.append(composition_for(window_obs, x, y))
+
+ if not rows:
+ return pd.DataFrame()
+ out = pd.concat(rows)
+ out["window_size"] = window_size
+ out["step_size"] = step_size
+ return out
def get_comp_mask(df, pheno_col, pheno_vals, step_size):
diff --git a/spatialcells/spatial/_getBoundary.py b/spatialcells/spatial/_getBoundary.py
index 6fdb705..b02ec53 100644
--- a/spatialcells/spatial/_getBoundary.py
+++ b/spatialcells/spatial/_getBoundary.py
@@ -1,3 +1,4 @@
+import numpy as np
import shapely
from shapely.geometry import Polygon, MultiPolygon
from shapely.validation import make_valid
@@ -29,19 +30,22 @@ def getBoundary(anndata, communitycolumn, communityIndexList, alpha=100, debug=F
xy = anndata.obs[anndata.obs[communitycolumn].isin(communityIndexList)][
["X_centroid", "Y_centroid"]
].to_numpy()
+ # slightly nudge the input points so they lie strictly inside.
+ eps = max(float(np.ptp(xy, axis=0).max()), 1.0) * 1e-9
edge_components = getAlphaShapes(xy, alpha)
polygons = [Polygon(edge[:, 0, :]).buffer(0) for edge in edge_components]
boundary = MultiPolygon(polygons)
- # make sure the boundary is valid and points of interest are inside
- boundary = make_valid(boundary).buffer(1)
+ boundary = make_valid(boundary).buffer(eps)
if boundary.geom_type == "Polygon":
boundary = MultiPolygon([boundary])
+
new_boundary = []
for poly in boundary.geoms:
holes = poly.interiors
hole_polygons = [Polygon(hole) for hole in holes]
- # prevent holes from touching the boundary
- hole_limit = Polygon(poly.exterior).buffer(-1)
+ # shrink the limit by eps avoid the edge case of holes (internal polygons)
+ # being exactly tangential to the exterior, leading to an invalid polygon.
+ hole_limit = Polygon(poly.exterior).buffer(-eps)
hole_multipoly = shapely.unary_union(hole_polygons) & hole_limit
if hole_multipoly.geom_type == "Polygon":
hole_multipoly = MultiPolygon([hole_multipoly])
@@ -49,6 +53,8 @@ def getBoundary(anndata, communitycolumn, communityIndexList, alpha=100, debug=F
new_poly = Polygon(poly.exterior, new_holes_polygons)
new_boundary.append(new_poly)
boundary = make_valid(MultiPolygon(new_boundary))
+ if boundary.geom_type == "Polygon":
+ boundary = MultiPolygon([boundary])
if debug:
return boundary, polygons, edge_components
diff --git a/spatialcells/spatial/_utils.py b/spatialcells/spatial/_utils.py
index 3e130c4..f433fed 100644
--- a/spatialcells/spatial/_utils.py
+++ b/spatialcells/spatial/_utils.py
@@ -6,6 +6,50 @@
from collections import defaultdict
+def getAlphaShapes(points, alpha, debug=False):
+ """
+ Compute the alpha shape of a set of points.
+
+ :param points: np.array of shape (n,2) points.
+ :param alpha: alpha value.
+ :returns: set of (i,j) point pairs representing edges of the alpha-shape.
+ """
+ assert points.shape[0] > 3, "Need at least four points"
+
+ # triangulate all points
+ tri = Delaunay(points)
+
+ pa = points[tri.simplices[:, 0]]
+ pb = points[tri.simplices[:, 1]]
+ pc = points[tri.simplices[:, 2]]
+ a = np.sqrt((pa[:, 0] - pb[:, 0]) ** 2 + (pa[:, 1] - pb[:, 1]) ** 2)
+ b = np.sqrt((pb[:, 0] - pc[:, 0]) ** 2 + (pb[:, 1] - pc[:, 1]) ** 2)
+ c = np.sqrt((pc[:, 0] - pa[:, 0]) ** 2 + (pc[:, 1] - pa[:, 1]) ** 2)
+ s = (a + b + c) / 2.0
+ area = np.sqrt(s * (s - a) * (s - b) * (s - c))
+ # Computing radius of triangle circumcircle
+ # www.mathalino.com/reviewer/derivation-of-formulas/derivation-of-formula-for-radius-of-circumcircle
+ circum_r = a * b * c / (4.0 * area + 1e-10)
+ verts = tri.simplices[circum_r < alpha]
+
+ # get edges that only appear once
+ edges = _getUniqueEdges(
+ np.concatenate([verts[:, [0, 1]], verts[:, [1, 2]], verts[:, [0, 2]]], axis=0)
+ )
+ # order all edges
+ edge_component_indices = _getOrderedEdgeComponents(edges)
+ if debug:
+ print("edge_component_indices:", len(edge_component_indices))
+
+ # points
+ edge_components = []
+ for component in edge_component_indices:
+ edge_components.append(points[component])
+ if debug:
+ print(component.shape)
+ return edge_components
+
+
def _bfsGetShortestRing(edge_dict, start_point):
"""
Given a set of edges, return the shortest ring starting from start_point.
@@ -118,50 +162,6 @@ def _pruneTouchingComponents(edge_dict):
return new_edge_dict, components
-def getAlphaShapes(points, alpha, debug=False):
- """
- Compute the alpha shape of a set of points.
-
- :param points: np.array of shape (n,2) points.
- :param alpha: alpha value.
- :returns: set of (i,j) point pairs representing edges of the alpha-shape.
- """
- assert points.shape[0] > 3, "Need at least four points"
-
- # triangulate all points
- tri = Delaunay(points)
-
- pa = points[tri.simplices[:, 0]]
- pb = points[tri.simplices[:, 1]]
- pc = points[tri.simplices[:, 2]]
- a = np.sqrt((pa[:, 0] - pb[:, 0]) ** 2 + (pa[:, 1] - pb[:, 1]) ** 2)
- b = np.sqrt((pb[:, 0] - pc[:, 0]) ** 2 + (pb[:, 1] - pc[:, 1]) ** 2)
- c = np.sqrt((pc[:, 0] - pa[:, 0]) ** 2 + (pc[:, 1] - pa[:, 1]) ** 2)
- s = (a + b + c) / 2.0
- area = np.sqrt(s * (s - a) * (s - b) * (s - c))
- # Computing radius of triangle circumcircle
- # www.mathalino.com/reviewer/derivation-of-formulas/derivation-of-formula-for-radius-of-circumcircle
- circum_r = a * b * c / (4.0 * area + 1e-10)
- verts = tri.simplices[circum_r < alpha]
-
- # get edges that only appear once
- edges = _getUniqueEdges(
- np.concatenate([verts[:, [0, 1]], verts[:, [1, 2]], verts[:, [0, 2]]], axis=0)
- )
- # order all edges
- edge_component_indices = _getOrderedEdgeComponents(edges)
- if debug:
- print("edge_component_indices:", len(edge_component_indices))
-
- # points
- edge_components = []
- for component in edge_component_indices:
- edge_components.append(points[component])
- if debug:
- print(component.shape)
- return edge_components
-
-
def getComponents(boundary, keep_holes=True):
"""
Get the components of a boundary defined by a MultiPolygon.
@@ -222,226 +222,4 @@ def pruneSmallComponents(
holes_to_keep.append(hole)
if geom.area >= min_area and len(geom.exterior.coords) - 1 >= min_edges:
polygons.append(Polygon(geom.exterior.coords, holes_to_keep))
- return MultiPolygon(polygons)
-
-
-# def getEdgesOnBoundaries(boundaries):
-# """ """
-# points = []
-# for boundary_set in boundaries:
-# for compt in boundary_set:
-# points.append(compt)
-# return np.concatenate(points)
-
-
-# def groupRemoveEdgeComponents(edge_components, nedges_min, nedges_out_min):
-# edge_components.sort(key=lambda x: len(x), reverse=True)
-# new_edge_components = []
-# for comp in edge_components:
-# is_in_comp = -1
-# for i, prev_comp in enumerate(new_edge_components):
-# # print(comp[0][0])
-# if isInRegion(comp[0][0], prev_comp[0]):
-# is_in_comp = i
-# # print(i, comp.shape)
-# break
-# if is_in_comp != -1 and len(comp) >= nedges_min:
-# new_edge_components[is_in_comp].append(comp)
-# elif is_in_comp == -1 and (len(comp) >= nedges_out_min):
-# new_edge_components.append([comp])
-# return new_edge_components
-
-# def isInRegion(point, boundary, debug=False):
-# """
-# Check whether "point" is within the boundary.
-# """
-
-# count = 0
-# larger_y2_count = 0
-# smaller_y2_count = 0
-
-# # point = np.around(point, decimals=3)
-# point = np.array(point)
-
-# # print(point, point.shape, boundary, len(boundary))
-# for pi, pj in boundary:
-# # pi = np.around(pi, decimals=3)
-# # pj = np.around(pj, decimals=3)
-
-# # if point is on the edge points, return True
-# if (point[0] == pi[0] and point[1] == pi[1]) or (
-# point[0] == pj[0] and point[1] == pj[1]
-# ):
-# return True
-
-# # one of x values must be bigger than the point.x
-# # ignore other edges
-# if pi[0] >= point[0] or pj[0] >= point[0]:
-# cond1 = (pi[1] >= point[1]) and (pj[1] <= point[1])
-# cond2 = (pi[1] <= point[1]) and (pj[1] >= point[1])
-# if cond1 or cond2:
-# # if debug: print("dealing", pi, pj)
-
-# # if point and edge are on a horizantal line
-# if (point[1] == pi[1]) and (point[1] == pj[1]):
-# count += ret
-
-# # handles edge case of touching line segments / segments on a straight line
-# # if either x == point.x, let this point be x1.
-# elif pi[1] == point[1] or pj[1] == point[1]:
-# if debug:
-# print("touching")
-# if (pi[1] + pj[1]) / 2 > point[1]: # x2 larger
-# larger_y2_count += 1
-# elif (pi[1] + pj[1]) / 2 < point[1]: # x2 smaller
-# smaller_y2_count += 1
-
-# # # check if line segment intersects with point, (0, point.y). if so count += 1
-# # # only check if the line segment is on the left side of the point and point between the two y values
-# # if (pi[1] < point[1]) != (pj[1] < point[1]) and (pi[0] < point[0] or pj[0] < point[0]):
-# else:
-# ret = isIntersect(point, (max(pi[0], pj[0]), point[1]), pi, pj)
-# if debug:
-# print("isIntersect", ret, pi, pj)
-# # if debug:print(point, (0, point[1]))
-# # if debug:print(pi, pj)
-# count += ret
-
-# # if debug: print("before:", count)
-# count += (larger_y2_count % 2) and (smaller_y2_count % 2)
-# if debug:
-# print(
-# "count:",
-# count,
-# "larger_y2_count",
-# larger_y2_count,
-# "smaller_y2_count",
-# smaller_y2_count,
-# )
-
-# return (count % 2) != 0
-
-
-# def getPolygons(boundaries):
-# polygons = []
-# for boundary_set in boundaries:
-# external_boundaries = set(np.arange(len(boundary_set)))
-# internal_boundaries = defaultdict(list)
-# for i in range(len(boundary_set)):
-# for j in range(i + 1, len(boundary_set)):
-# pointi = boundary_set[i][:, 0, :]
-# pointj = boundary_set[j][:, 0, :]
-# polygoni = Polygon(pointi)
-# polygonj = Polygon(pointj)
-# if polygoni.contains(polygonj):
-# internal_boundaries[i].append(pointj)
-# external_boundaries.discard(j)
-# elif polygonj.contains(polygoni):
-# internal_boundaries[j].append(pointi)
-# external_boundaries.discard(i)
-# for external_boundary_idx in external_boundaries:
-# outer_points = boundary_set[external_boundary_idx][:, 0, :]
-# inner_points = internal_boundaries[external_boundary_idx]
-# polygon = Polygon(outer_points, inner_points)
-# polygons.append(polygon)
-# return polygons
-
-
-# def isCounterClockwise(A, B, C):
-# # if ABC is counterclockwise, then slope of AB less than AC
-# return (C[1] - A[1]) * (B[0] - A[0]) > (B[1] - A[1]) * (C[0] - A[0])
-
-
-# def isIntersect(p1, p2, p3, p4):
-# """
-# Checkswhether the line segment p1-p2 intersects with p3-p4,
-# assuming no colinear points
-# """
-# return isCounterClockwise(p1, p3, p4) != isCounterClockwise(
-# p2, p3, p4
-# ) and isCounterClockwise(p1, p2, p3) != isCounterClockwise(p1, p2, p4)
-
-
-# def PointsInCircum(eachPoint, r, n=100):
-# """
-# Return n points within r distance from eachPoint
-# """
-# return [
-# (
-# eachPoint[0] + math.cos(2 * math.pi / n * x) * r,
-# eachPoint[1] + math.sin(2 * math.pi / n * x) * r,
-# )
-# for x in range(0, n + 1)
-# ]
-
-
-# def bufferPoints(inPoints, stretchCoef, n=100):
-# """
-# Return n*len(inPoints) points that are within r distance of each point.
-# """
-# newPoints = []
-# for eachPoint in inPoints:
-# newPoints += PointsInCircum(eachPoint, stretchCoef, n)
-# # newPoints.append(np.array(PointsInCircum(eachPoint, stretchCoef, n)))
-# newPoints = np.array(newPoints)
-
-# return newPoints
-
-
-# def hasEdge(point, step, polygons):
-# grid_edges = Polygon(
-# [
-# (point[0], point[1]),
-# (point[0] + step, point[1]),
-# (point[0], point[1] + step),
-# (point[0] + step, point[1] + step),
-# ]
-# ).boundary
-# for polygon in polygons:
-# if polygon.boundary.intersects(grid_edges):
-# return True
-# return False
-
-
-# def getBufferedBoundary(boundaries, offset=200, minsize=20):
-# """ """
-
-# buffered_boundaries = []
-# outer_multipolygon = Polygon()
-# inner_multipolygon = Polygon()
-# for boundary_set in boundaries:
-# for i, boundary in enumerate(boundary_set):
-# boundary_points = boundary[:, 0, :]
-# boundary_polygon = Polygon(boundary_points)
-# if i == 0:
-# outer_multipolygon = outer_multipolygon | boundary_polygon.buffer(
-# offset
-# )
-# else:
-# inner_multipolygon = inner_multipolygon | boundary_polygon.buffer(
-# -offset
-# )
-
-# s_boundary_polygons = outer_multipolygon - inner_multipolygon
-
-# if isinstance(s_boundary_polygons, MultiPolygon):
-# s_boundary_polygons = s_boundary_polygons.geoms
-# else:
-# s_boundary_polygons = [s_boundary_polygons]
-
-# for s_boundary_polygon in s_boundary_polygons:
-# for ring in [s_boundary_polygon.exterior] + list(s_boundary_polygon.interiors):
-# buffered_points = np.array(ring.coords)
-# if buffered_points.shape[0] < minsize:
-# continue
-# buffered_edges = np.stack(
-# [np.roll(buffered_points, -1, axis=0), buffered_points], axis=1
-# )
-# buffered_boundaries.append(buffered_edges)
-
-# # comp_polygons = getPolygons([buffered_boundaries])
-
-# # return [buffered_boundaries], comp_polygons
-
-# buffered_boundaries_edge = getEdgesOnBoundaries([buffered_boundaries])
-# return buffered_boundaries_edge, [buffered_boundaries]
+ return MultiPolygon(polygons)
\ No newline at end of file
diff --git a/tutorials/1_tutorial_measurements.ipynb b/tutorials/1_tutorial_measurements.ipynb
index 97eebc5..c0eb4f4 100644
--- a/tutorials/1_tutorial_measurements.ipynb
+++ b/tutorials/1_tutorial_measurements.ipynb
@@ -59,7 +59,7 @@
"metadata": {},
"outputs": [],
"source": [
- "adata = ad.read_h5ad(\"../../data/MEL1_adata.h5ad\")"
+ "adata = ad.read_h5ad(\"../data/MEL1_adata.h5ad\")"
]
},
{
@@ -629,7 +629,8 @@
" adata, [metric_col, pheno], regions=[\"Tumor\"]\n",
").sort_values(by=metric_col, ascending=False)\n",
"\n",
- "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, log=True, ax=ax2)\n",
+ "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, ax=ax2)\n",
+ "ax2.set_xscale(\"log\")\n",
"ax2.legend(bbox_to_anchor=(1.02, 1), loc=\"upper left\", borderaxespad=0)\n",
"plt.tight_layout()\n",
"plt.show()"
@@ -682,7 +683,8 @@
"ax1.invert_yaxis()\n",
"ax1.legend(loc=\"upper right\", title=metric_col)\n",
"\n",
- "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, log=True, ax=ax2)\n",
+ "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, ax=ax2)\n",
+ "ax2.set_xscale(\"log\")\n",
"ax2.legend(bbox_to_anchor=(1.02, 1), loc=\"upper left\", borderaxespad=0)\n",
"\n",
"plt.tight_layout()\n",
@@ -735,7 +737,8 @@
" adata, [metric_col, pheno], regions=[\"Tumor\"]\n",
").sort_values(by=metric_col, ascending=False)\n",
"\n",
- "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, log=True, ax=ax2)\n",
+ "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, ax=ax2)\n",
+ "ax2.set_xscale(\"log\")\n",
"ax2.legend(bbox_to_anchor=(1.02, 1), loc=\"upper left\", borderaxespad=0)\n",
"plt.tight_layout()\n",
"plt.show()"
@@ -787,7 +790,8 @@
" adata, [metric_col, pheno], regions=[\"Tumor\"]\n",
").sort_values(by=metric_col, ascending=False)\n",
"\n",
- "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, log=True, ax=ax2)\n",
+ "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, ax=ax2)\n",
+ "ax2.set_xscale(\"log\")\n",
"ax2.legend(bbox_to_anchor=(1.02, 1), loc=\"upper left\", borderaxespad=0)\n",
"plt.tight_layout()\n",
"plt.show()"
@@ -1025,7 +1029,8 @@
" adata, [metric_col, pheno], regions=[\"Tumor\"]\n",
").sort_values(by=metric_col, ascending=False)\n",
"\n",
- "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, log=True, ax=ax2)\n",
+ "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, ax=ax2)\n",
+ "ax2.set_xscale(\"log\")\n",
"ax2.legend(bbox_to_anchor=(1.02, 1), loc=\"upper left\", borderaxespad=0)\n",
"plt.tight_layout()\n",
"plt.show()"
@@ -1080,7 +1085,8 @@
" adata, [metric_col, pheno], regions=[\"Tumor\"]\n",
").sort_values(by=metric_col, ascending=False)\n",
"\n",
- "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, log=True, ax=ax2)\n",
+ "sns.barplot(data=df, y=metric_col, x=\"composition\", hue=pheno, ax=ax2)\n",
+ "ax2.set_xscale(\"log\")\n",
"ax2.legend(bbox_to_anchor=(1.02, 1), loc=\"upper left\", borderaxespad=0)\n",
"plt.tight_layout()\n",
"plt.show()"
@@ -1296,7 +1302,7 @@
" \n",
" \n",
"\n",
- "1605 rows × 7 columns
\n",
+ "1605 rows \u00d7 7 columns
\n",
""
],
"text/plain": [
diff --git a/tutorials/2_tutorial_tumor_proliferation.ipynb b/tutorials/2_tutorial_tumor_proliferation.ipynb
index b46f64e..2293c08 100644
--- a/tutorials/2_tutorial_tumor_proliferation.ipynb
+++ b/tutorials/2_tutorial_tumor_proliferation.ipynb
@@ -56,7 +56,7 @@
}
],
"source": [
- "adata = ad.read_h5ad(\"../../data/MEL1_adata.h5ad\")\n",
+ "adata = ad.read_h5ad(\"../data/MEL1_adata.h5ad\")\n",
"spc.prep.setGate(adata, \"SOX10_cellRingMask\", 7.9, debug=True)\n",
"spc.prep.setGate(adata, \"KERATIN_cellRingMask\", 6.4, debug=True)"
]
@@ -161,7 +161,7 @@
{
"data": {
"text/plain": [
- ""
]
@@ -937,7 +937,7 @@
" \n",
" \n",
"\n",
- "1997 rows × 10 columns
\n",
+ "1997 rows \u00d7 10 columns
\n",
""
],
"text/plain": [
diff --git a/tutorials/3_tutorial_immune_infiltration_overlap.ipynb b/tutorials/3_tutorial_immune_infiltration_overlap.ipynb
index 55f6bbf..fc86aee 100644
--- a/tutorials/3_tutorial_immune_infiltration_overlap.ipynb
+++ b/tutorials/3_tutorial_immune_infiltration_overlap.ipynb
@@ -59,7 +59,7 @@
}
],
"source": [
- "adata = ad.read_h5ad(\"../../data/MEL1_adata.h5ad\")\n",
+ "adata = ad.read_h5ad(\"../data/MEL1_adata.h5ad\")\n",
"spc.prep.setGate(adata, \"KERATIN_cellRingMask\", 6.4, debug=True)\n",
"spc.prep.setGate(adata, \"SOX10_cellRingMask\", 7.9, debug=True)\n",
"spc.prep.setGate(adata, \"CD3D_cellRingMask\", 7, debug=True)"
diff --git a/tutorials/4_tutorial_macro_micro_regions.ipynb b/tutorials/4_tutorial_macro_micro_regions.ipynb
index 3cabf9c..6832e66 100644
--- a/tutorials/4_tutorial_macro_micro_regions.ipynb
+++ b/tutorials/4_tutorial_macro_micro_regions.ipynb
@@ -44,7 +44,7 @@
"metadata": {},
"outputs": [],
"source": [
- "adata = ad.read_h5ad(\"../../data/MEL1_adata.h5ad\")\n",
+ "adata = ad.read_h5ad(\"../data/MEL1_adata.h5ad\")\n",
"adata.obs[\"id\"] = adata.obs_names"
]
},
@@ -3077,7 +3077,7 @@
" \n",
" \n",
"\n",
- "5 rows × 53 columns
\n",
+ "5 rows \u00d7 53 columns
\n",
""
],
"text/plain": [
@@ -3490,7 +3490,7 @@
" \n",
" \n",
"\n",
- "378 rows × 5 columns
\n",
+ "378 rows \u00d7 5 columns
\n",
""
],
"text/plain": [
diff --git a/tutorials/5_tutorial_import_export_omero.ipynb b/tutorials/5_tutorial_import_export_omero.ipynb
index 9c17ac9..6317e9f 100644
--- a/tutorials/5_tutorial_import_export_omero.ipynb
+++ b/tutorials/5_tutorial_import_export_omero.ipynb
@@ -49,7 +49,7 @@
"outputs": [],
"source": [
"# Load data\n",
- "adata = ad.read(\"../../data/Z147_1_750.h5ad\")"
+ "adata = ad.read_h5ad(\"../data/Z147_1_750.h5ad\")"
]
},
{
@@ -311,7 +311,7 @@
{
"data": {
"text/plain": [
- "AnnData object with n_obs × n_vars = 1110585 × 30\n",
+ "AnnData object with n_obs \u00d7 n_vars = 1110585 \u00d7 30\n",
" obs: 'X_centroid', 'Y_centroid', 'column_centroid', 'row_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Solidity', 'Extent', 'Orientation', 'imageid', 'phenotype', 'kmeans', 'kmeans_renamed', 'pickseq_roi', 'pickseq_roi_minimal', 'dermis_roi', 'epidermis_roi', 'general_roi', 'Kmeans_r', 'der_epider_roi', 'phenotype_proliferation', 'phenograph', 'spatial_expression_phenograph', 'spatial_expression_kmeans', 'phenograph_raw', 'phenograph_raw_minimal', 'phenotype_1', 'phenotype_1_tumor_kmeans', 'phenotype_1_tumor_kmeans_renamed', 'phenotype_final', 'spatial_expression_consolidated', 'MC', 'spatial_count_kmeans', 'phenotype_2', 'phenotype_2_tumor_kmeans', 'phenotype_2_tumor_kmeans_renamed', 'phenotype_large_cohort', 'phenotype_large_cohort_RJP', 'spatial_lda_kmeans', 'phenotype_2_tumor_kmeans_old', 'tumor_expression_trimmed', 'tumor_expression_TC', 'IM_roi', 'TC_ROI_IM_large', 'TC_ROI_IM_small', 'TC_ROI_IM_elarge', 'manuscript_roi', 'SOX10_positive', 'tumor_region', 'epi_region', 'distance_from_epidermis', 'distance_from_epidermis_binned'\n",
" uns: 'all_markers', \"dendrogram_['kmeans']\", \"dendrogram_['phenograph']\", \"dendrogram_['phenograph_raw']\", \"dendrogram_['phenograph_raw_minimal']\", \"dendrogram_['phenotype']\", \"dendrogram_['phenotype_2_tumor_kmeans']\", \"dendrogram_['phenotype_2_tumor_kmeans_scaled']\", \"dendrogram_['spatial_expression_consolidated']\", \"dendrogram_['spatial_expression_kmeans']\", \"dendrogram_['spatial_expression_phenograph']\", 'interaction_all', 'rank_genes_groups', 'spatial_count_radius', 'spatial_distance', 'spatial_expression_radius', 'spatial_interaction', 'spatial_interaction_1', 'spatial_interaction_motif_analysis'"
]
diff --git a/tutorials/6_tutorial_visium_cell2location.ipynb b/tutorials/6_tutorial_visium_cell2location.ipynb
index 1c627bd..b56e86f 100644
--- a/tutorials/6_tutorial_visium_cell2location.ipynb
+++ b/tutorials/6_tutorial_visium_cell2location.ipynb
@@ -55,7 +55,7 @@
{
"data": {
"text/plain": [
- "AnnData object with n_obs × n_vars = 4035 × 10217\n",
+ "AnnData object with n_obs \u00d7 n_vars = 4035 \u00d7 10217\n",
" obs: 'in_tissue', 'array_row', 'array_col', 'sample', '_indices', '_scvi_batch', '_scvi_labels'\n",
" var: 'feature_types', 'genome', 'SYMBOL', 'MT_gene'\n",
" uns: '_scvi_manager_uuid', '_scvi_uuid', 'mod', 'spatial'\n",
@@ -68,7 +68,7 @@
}
],
"source": [
- "adata = ad.read_h5ad(\"../../data/sp_1.h5ad\")\n",
+ "adata = ad.read_h5ad(\"../data/sp_1.h5ad\")\n",
"adata"
]
},
@@ -77,7 +77,7 @@
"id": "3b078b56",
"metadata": {},
"source": [
- "We use 5% quantile of the posterior distribution, representing the value of cell abundance that the model has high confidence in (aka ‘at least this amount is present’).\n",
+ "We use 5% quantile of the posterior distribution, representing the value of cell abundance that the model has high confidence in (aka \u2018at least this amount is present\u2019).\n",
"\n",
"https://cell2location.readthedocs.io/en/latest/notebooks/cell2location_tutorial.html#Visualising-cell-abundance-in-spatial-coordinates"
]
@@ -799,7 +799,7 @@
" \n",
" \n",
"\n",
- "2 rows × 34 columns
\n",
+ "2 rows \u00d7 34 columns
\n",
""
],
"text/plain": [
@@ -966,7 +966,7 @@
" \n",
" \n",
"\n",
- "2 rows × 34 columns
\n",
+ "2 rows \u00d7 34 columns
\n",
""
],
"text/plain": [
@@ -1650,7 +1650,7 @@
" \n",
" \n",
"\n",
- "4 rows × 34 columns
\n",
+ "4 rows \u00d7 34 columns
\n",
""
],
"text/plain": [
diff --git a/tutorials/8_tutorial_scimap_joint_analysis.ipynb b/tutorials/8_tutorial_scimap_joint_analysis.ipynb
index b572bbc..fdb8d4d 100644
--- a/tutorials/8_tutorial_scimap_joint_analysis.ipynb
+++ b/tutorials/8_tutorial_scimap_joint_analysis.ipynb
@@ -51,7 +51,7 @@
"metadata": {},
"outputs": [],
"source": [
- "adata = ad.read_h5ad(\"../../data/MEL1_adata.h5ad\")"
+ "adata = ad.read_h5ad(\"../data/MEL1_adata.h5ad\")"
]
},
{
@@ -606,7 +606,7 @@
{
"data": {
"text/plain": [
- "AnnData object with n_obs × n_vars = 632296 × 6\n",
+ "AnnData object with n_obs \u00d7 n_vars = 632296 \u00d7 6\n",
" obs: 'X_centroid', 'Y_centroid', 'phenotype_large_cohort', 'SOX10_cellRingMask_positive', 'MITF_cellRingMask_positive', 'KERATIN_cellRingMask_positive', 'pheno', 'COI_community', 'region', 'CD3D_cellRingMask_positive'\n",
" uns: 'all_markers'"
]
@@ -630,7 +630,7 @@
{
"data": {
"text/plain": [
- "AnnData object with n_obs × n_vars = 356569 × 6\n",
+ "AnnData object with n_obs \u00d7 n_vars = 356569 \u00d7 6\n",
" obs: 'X_centroid', 'Y_centroid', 'phenotype_large_cohort', 'SOX10_cellRingMask_positive', 'MITF_cellRingMask_positive', 'KERATIN_cellRingMask_positive', 'pheno', 'COI_community', 'region', 'CD3D_cellRingMask_positive'\n",
" uns: 'all_markers'"
]
@@ -654,7 +654,7 @@
{
"data": {
"text/plain": [
- "AnnData object with n_obs × n_vars = 10000 × 6\n",
+ "AnnData object with n_obs \u00d7 n_vars = 10000 \u00d7 6\n",
" obs: 'X_centroid', 'Y_centroid', 'phenotype_large_cohort', 'SOX10_cellRingMask_positive', 'MITF_cellRingMask_positive', 'KERATIN_cellRingMask_positive', 'pheno', 'COI_community', 'region', 'CD3D_cellRingMask_positive'\n",
" uns: 'all_markers', 'neighbors'\n",
" obsp: 'distances', 'connectivities'"
diff --git a/tutorials/MEL1_part1_animation_umap2xy_scimap.gif b/tutorials/MEL1_part1_animation_umap2xy_scimap.gif
index ae91b03..654272f 100644
Binary files a/tutorials/MEL1_part1_animation_umap2xy_scimap.gif and b/tutorials/MEL1_part1_animation_umap2xy_scimap.gif differ
diff --git a/tutorials/MEL1_part2_animation_umap2xy_scimap.gif b/tutorials/MEL1_part2_animation_umap2xy_scimap.gif
index bd5d53e..824960c 100644
Binary files a/tutorials/MEL1_part2_animation_umap2xy_scimap.gif and b/tutorials/MEL1_part2_animation_umap2xy_scimap.gif differ