reworked get_interaction_matrix and get_interaction_pvals to be faster with numba#345

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Merged
newmana merged 4 commits into from
Apr 16, 2026
Merged

reworked get_interaction_matrix and get_interaction_pvals to be faster with numba #345

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a419ae8
reworked get_interaction_matrix and get_interaction_pvals to be faste…
lhock17 Apr 16, 2026
ccaeefe
Remove unused neighbourhood_bcs parameter from get_interaction_matrix…
lhock17 Apr 16, 2026
749481c
Refactor get_interaction_pvals and get_interaction_matrix for improve…
lhock17 Apr 16, 2026
4444702
fix get_interaction_matrix test
lhock17 Apr 16, 2026
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1 change: 0 additions & 1 deletion stlearn/tl/cci/analysis.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -710,7 +710,6 @@ def run_cci(

int_matrix = get_interaction_matrix(
cell_data,
neighbourhood_bcs,
neighbourhood_indices,
all_set,
sig_bool,
Expand Down
129 changes: 52 additions & 77 deletions stlearn/tl/cci/het.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -8,7 +8,6 @@
from numba.typed import List

from stlearn.tl.cci.het_helpers import (
add_unique_edges,
edge_core,
get_between_spot_edge_array,
get_data_for_counting,
Expand DownExpand Up@@ -203,7 +202,7 @@ def count_interactions(
return int_matrix if trans_dir else int_matrix.transpose()


@jit(parallel=True)
@njit(parallel=True)
def get_interaction_pvals(
int_matrix,
n_perms,
Expand All@@ -216,24 +215,16 @@ def get_interaction_pvals(
R_bool,
cell_prop_cutoff,
):
""" Perturbs the cell labels to get background count frequency to estimate \
p-values.
"""
"""Gets the p-values for the interaction counts."""

# Counting how many times permutation of spots cell data creates interaction
# counts greater than that observed, in order to calculate p-values.
shape_ = (n_perms, int_matrix.shape[0], int_matrix.shape[1])
# Storing the instances where the count is greater randomly for each perm.
# Allows for embarassing parallelisation.
greater_counts = np.zeros(shape_, dtype=np.int64)
indices = np.zeros((cell_data.shape[0]), dtype=np.int64)
for i in range(cell_data.shape[0]):
indices[i] = i

# If dealing with discrete data, no need to randomise columns indendently #
discrete = np.all(np.logical_or(cell_data == 0, cell_data == 1))
for i in prange(n_perms):
# Permuting the cell data by swapping between spots for each column #
if not discrete:
perm_data = cell_data.copy()
for j in range(cell_data.shape[1]):
Expand All@@ -243,95 +234,79 @@ def get_interaction_pvals(
rand_indices = np.random.choice(indices, cell_data.shape[0], False)
perm_data = cell_data[rand_indices, :]

# Calculating interactions for permuted labels #
perm_matrix = get_interaction_matrix(
perm_data,
neighbourhood_bcs,
neighbourhood_indices,
all_set,
sig_bool,
L_bool,
R_bool,
cell_prop_cutoff,
)
# perm_greater = (perm_matrix >= int_matrix).astype(int)
perm_greater = perm_matrix >= int_matrix
greater_counts[i, :, :] = perm_greater
for row in range(int_matrix.shape[0]):
for col in range(int_matrix.shape[1]):
greater_counts[i, row, col] = (
perm_matrix[row, col] >= int_matrix[row, col]
)

# Calculating the pvalues #
total_greater_counts = greater_counts.sum(axis=0) # cts * ct counts
int_pvals = total_greater_counts / n_perms
# Numba parallel sums axis 0 efficiently
out = np.zeros((int_matrix.shape[0], int_matrix.shape[1]), dtype=np.float64)
for i in range(n_perms):
for row in range(int_matrix.shape[0]):
for col in range(int_matrix.shape[1]):
out[row, col] += greater_counts[i, row, col]
int_pvals = out / n_perms
return int_pvals


@njit
def get_interaction_matrix(
cell_data,
neighbourhood_bcs,
neighbourhood_indices,
all_set,
sig_bool,
L_bool,
R_bool,
cell_prop_cutoff,
):
"""Gets the interaction count matrix."""
# Now counting the interactions under 3 situations:
# 1) sig spot with ligand, only neighbours with receptor relevant
# 2) sig spot with receptor, only neighbours with ligand relevant
# NOTE, A<->B is double counted, but on different side of matrix.
# (if bidirectional interaction between two spots, counts as two seperate
# interactions).
LR_edges = get_interactions(
cell_data,
neighbourhood_bcs,
neighbourhood_indices,
all_set,
sig_bool,
L_bool,
R_bool,
cell_prop_cutoff=cell_prop_cutoff,
# sig ligand->receptor mode
)
RL_edges = get_interactions(
cell_data,
neighbourhood_bcs,
neighbourhood_indices,
all_set,
sig_bool,
R_bool,
L_bool,
cell_prop_cutoff=cell_prop_cutoff,
# sig receptor->ligand mode
)

# Counting the number of unique interacting edges
# between different cell type via indicate LR
int_matrix = np.zeros((all_set.shape[0], all_set.shape[0]), dtype=np.int64)
edge_i = 0
for i in range(all_set.shape[0]):
for j in range(all_set.shape[0]):
RL_Atrans_Bedges = LR_edges[edge_i]
LR_Atrans_Bedges = RL_edges[edge_i]
edge_i += 1
max_len = max([len(RL_Atrans_Bedges), len(LR_Atrans_Bedges)])
if max_len == 0: # Nothing to count #
continue

edge_starts = List()
edge_ends = List()
for k in range(max_len):
if k < len(RL_Atrans_Bedges):
edge_starts.append(RL_Atrans_Bedges[k][0])
edge_ends.append(RL_Atrans_Bedges[k][1])
if k < len(LR_Atrans_Bedges):
edge_starts.append(LR_Atrans_Bedges[k][0])
edge_ends.append(LR_Atrans_Bedges[k][1])
Atrans_Bedges = List()
Atrans_Bedges.append((edge_starts[0], edge_ends[0])) # for typing
add_unique_edges(Atrans_Bedges, edge_starts, edge_ends)
# Atrans_Bedges = np.unique(RL_Atrans_Bedges + LR_Atrans_Bedges)
int_matrix[i, j] = len(Atrans_Bedges) - 1 # since added edge for type
"""Gets the interaction matrix for a given cell data matrix."""

n_spots = cell_data.shape[0]
n_types = all_set.shape[0]
int_matrix = np.zeros((n_types, n_types), dtype=np.int64)

for t1 in range(n_types):
for t2 in range(n_types):
s = {np.int64(-1)}
s.clear()

for i in range(n_spots):
if not sig_bool[i]:
continue
if cell_data[i, t1] <= cell_prop_cutoff:
continue

neighs = neighbourhood_indices[i][1]

for k in range(len(neighs)):
n_idx = neighs[k]
if cell_data[n_idx, t2] > cell_prop_cutoff:
valid = False
if L_bool[i] and R_bool[n_idx]:
valid = True
if R_bool[i] and L_bool[n_idx]:
valid = True

if valid:
u = np.int64(i)
v = np.int64(n_idx)
if u > v:
tmp = u
u = v
v = tmp
s.add((u << 32) | v)

int_matrix[t1, t2] = len(s)

return int_matrix

Expand Down
7 changes: 3 additions & 4 deletions tests/adds/test_row_annotations.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -18,14 +18,14 @@ def setUpClass(cls):
f"{test_data_path()}/" + "v1_human_breast_cancer_block_a_section_1.csv"
)


def setUp(self):
"""Set up test data with known clusters."""
self.adata = self.__class__._base_adata.copy()


def test_add_row_annotations(self):
row_annotations.row_annotations(self.adata, self.__class__.annotations_path, "ID")
row_annotations.row_annotations(
self.adata, self.__class__.annotations_path, "ID"
)

assert "annot_type" in self.adata.obs.columns
assert "fine_annot_type" in self.adata.obs.columns
Expand All@@ -35,7 +35,6 @@ def test_add_row_annotations(self):
fine_annotated = self.adata.obs["fine_annot_type"].dropna()
assert len(fine_annotated) == len(annotated)


def test_add_row_annotations_with_missing_column(self):
with self.assertRaises(ValueError):
row_annotations.row_annotations(
Expand Down
1 change: 0 additions & 1 deletion tests/tl/test_cci.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -279,7 +279,6 @@ def test_get_interaction_matrix(self):
# Get interaction matrix
int_matrix = het.get_interaction_matrix(
cell_data,
self.neighbourhood_bcs,
self.neighbourhood_indices,
CELL_TYPE_LABELS,
sig_bool,
Expand Down
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} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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reworked get_interaction_matrix and get_interaction_pvals to be faster with numba#345

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 Merged

newmana
merged 4 commits into

from

Apr 16, 2026
 Merged


reworked get_interaction_matrix and get_interaction_pvals to be faster with numba
#345
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a419ae8

reworked get_interaction_matrix and get_interaction_pvals to be faste…


lhock17 Apr 16, 2026
ccaeefe

Remove unused neighbourhood_bcs parameter from get_interaction_matrix…


lhock17 Apr 16, 2026
749481c

Refactor get_interaction_pvals and get_interaction_matrix for improve…


lhock17 Apr 16, 2026
4444702

fix get_interaction_matrix test


lhock17 Apr 16, 2026

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1 change: 0 additions & 1 deletion
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -710,7 +710,6 @@ def run_cci(

 int_matrix = get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

129 changes: 52 additions & 77 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,7 +8,6 @@
from numba.typed import List

from stlearn.tl.cci.het_helpers import (
 add_unique_edges,
 edge_core,
 get_between_spot_edge_array,
 get_data_for_counting,
Expand DownExpand Up@@ -203,7 +202,7 @@ def count_interactions(
 return int_matrix if trans_dir else int_matrix.transpose()


@jit(parallel=True)
@njit(parallel=True)
def get_interaction_pvals(
 int_matrix,
 n_perms,
Expand All@@ -216,24 +215,16 @@ def get_interaction_pvals(
 R_bool,
 cell_prop_cutoff,
):
 """ Perturbs the cell labels to get background count frequency to estimate \
 p-values.
 """
 """Gets the p-values for the interaction counts."""

 # Counting how many times permutation of spots cell data creates interaction
 # counts greater than that observed, in order to calculate p-values.
 shape_ = (n_perms, int_matrix.shape[0], int_matrix.shape[1])
 # Storing the instances where the count is greater randomly for each perm.
 # Allows for embarassing parallelisation.
 greater_counts = np.zeros(shape_, dtype=np.int64)
 indices = np.zeros((cell_data.shape[0]), dtype=np.int64)
 for i in range(cell_data.shape[0]):
 indices[i] = i

 # If dealing with discrete data, no need to randomise columns indendently #
 discrete = np.all(np.logical_or(cell_data == 0, cell_data == 1))
 for i in prange(n_perms):
 # Permuting the cell data by swapping between spots for each column #
 if not discrete:
 perm_data = cell_data.copy()
 for j in range(cell_data.shape[1]):
Expand All@@ -243,95 +234,79 @@ def get_interaction_pvals(
 rand_indices = np.random.choice(indices, cell_data.shape[0], False)
 perm_data = cell_data[rand_indices, :]

 # Calculating interactions for permuted labels #
 perm_matrix = get_interaction_matrix(
 perm_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
 )
 # perm_greater = (perm_matrix >= int_matrix).astype(int)
 perm_greater = perm_matrix >= int_matrix
 greater_counts[i, :, :] = perm_greater
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 greater_counts[i, row, col] = (
 perm_matrix[row, col] >= int_matrix[row, col]
 )

 # Calculating the pvalues #
 total_greater_counts = greater_counts.sum(axis=0) # cts * ct counts
 int_pvals = total_greater_counts / n_perms
 # Numba parallel sums axis 0 efficiently
 out = np.zeros((int_matrix.shape[0], int_matrix.shape[1]), dtype=np.float64)
 for i in range(n_perms):
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 out[row, col] += greater_counts[i, row, col]
 int_pvals = out / n_perms
 return int_pvals


@njit
def get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
):
 """Gets the interaction count matrix."""
 # Now counting the interactions under 3 situations:
 # 1) sig spot with ligand, only neighbours with receptor relevant
 # 2) sig spot with receptor, only neighbours with ligand relevant
 # NOTE, A<->B is double counted, but on different side of matrix.
 # (if bidirectional interaction between two spots, counts as two seperate
 # interactions).
 LR_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig ligand->receptor mode
 )
 RL_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 R_bool,
 L_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig receptor->ligand mode
 )

 # Counting the number of unique interacting edges
 # between different cell type via indicate LR
 int_matrix = np.zeros((all_set.shape[0], all_set.shape[0]), dtype=np.int64)
 edge_i = 0
 for i in range(all_set.shape[0]):
 for j in range(all_set.shape[0]):
 RL_Atrans_Bedges = LR_edges[edge_i]
 LR_Atrans_Bedges = RL_edges[edge_i]
 edge_i += 1
 max_len = max([len(RL_Atrans_Bedges), len(LR_Atrans_Bedges)])
 if max_len == 0: # Nothing to count #
 continue

 edge_starts = List()
 edge_ends = List()
 for k in range(max_len):
 if k < len(RL_Atrans_Bedges):
 edge_starts.append(RL_Atrans_Bedges[k][0])
 edge_ends.append(RL_Atrans_Bedges[k][1])
 if k < len(LR_Atrans_Bedges):
 edge_starts.append(LR_Atrans_Bedges[k][0])
 edge_ends.append(LR_Atrans_Bedges[k][1])
 Atrans_Bedges = List()
 Atrans_Bedges.append((edge_starts[0], edge_ends[0])) # for typing
 add_unique_edges(Atrans_Bedges, edge_starts, edge_ends)
 # Atrans_Bedges = np.unique(RL_Atrans_Bedges + LR_Atrans_Bedges)
 int_matrix[i, j] = len(Atrans_Bedges) - 1 # since added edge for type
 """Gets the interaction matrix for a given cell data matrix."""

 n_spots = cell_data.shape[0]
 n_types = all_set.shape[0]
 int_matrix = np.zeros((n_types, n_types), dtype=np.int64)

 for t1 in range(n_types):
 for t2 in range(n_types):
 s = {np.int64(-1)}
 s.clear()

 for i in range(n_spots):
 if not sig_bool[i]:
 continue
 if cell_data[i, t1] <= cell_prop_cutoff:
 continue

 neighs = neighbourhood_indices[i][1]

 for k in range(len(neighs)):
 n_idx = neighs[k]
 if cell_data[n_idx, t2] > cell_prop_cutoff:
 valid = False
 if L_bool[i] and R_bool[n_idx]:
 valid = True
 if R_bool[i] and L_bool[n_idx]:
 valid = True

 if valid:
 u = np.int64(i)
 v = np.int64(n_idx)
 if u > v:
 tmp = u
 u = v
 v = tmp
 s.add((u << 32) | v)

 int_matrix[t1, t2] = len(s)

 return int_matrix

Expand Down

7 changes: 3 additions & 4 deletions
tests/adds/test_row_annotations.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,14 +18,14 @@ def setUpClass(cls):
 f"{test_data_path()}/" + "v1_human_breast_cancer_block_a_section_1.csv"
 )


 def setUp(self):
 """Set up test data with known clusters."""
 self.adata = self.__class__._base_adata.copy()


 def test_add_row_annotations(self):
 row_annotations.row_annotations(self.adata, self.__class__.annotations_path, "ID")
 row_annotations.row_annotations(
 self.adata, self.__class__.annotations_path, "ID"
 )

 assert "annot_type" in self.adata.obs.columns
 assert "fine_annot_type" in self.adata.obs.columns
Expand All@@ -35,7 +35,6 @@ def test_add_row_annotations(self):
 fine_annotated = self.adata.obs["fine_annot_type"].dropna()
 assert len(fine_annotated) == len(annotated)


 def test_add_row_annotations_with_missing_column(self):
 with self.assertRaises(ValueError):
 row_annotations.row_annotations(
Expand Down

1 change: 0 additions & 1 deletion
tests/tl/test_cci.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -279,7 +279,6 @@ def test_get_interaction_matrix(self):
 # Get interaction matrix
 int_matrix = het.get_interaction_matrix(
 cell_data,
 self.neighbourhood_bcs,
 self.neighbourhood_indices,
 CELL_TYPE_LABELS,
 sig_bool,
Expand Down
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injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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var __re = new RegExp('^' + ".*" + '

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reworked get_interaction_matrix and get_interaction_pvals to be faster with numba#345

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 Merged

newmana
merged 4 commits into

from

Apr 16, 2026
 Merged


reworked get_interaction_matrix and get_interaction_pvals to be faster with numba
#345
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a419ae8

reworked get_interaction_matrix and get_interaction_pvals to be faste…


lhock17 Apr 16, 2026
ccaeefe

Remove unused neighbourhood_bcs parameter from get_interaction_matrix…


lhock17 Apr 16, 2026
749481c

Refactor get_interaction_pvals and get_interaction_matrix for improve…


lhock17 Apr 16, 2026
4444702

fix get_interaction_matrix test


lhock17 Apr 16, 2026

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1 change: 0 additions & 1 deletion
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -710,7 +710,6 @@ def run_cci(

 int_matrix = get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

129 changes: 52 additions & 77 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,7 +8,6 @@
from numba.typed import List

from stlearn.tl.cci.het_helpers import (
 add_unique_edges,
 edge_core,
 get_between_spot_edge_array,
 get_data_for_counting,
Expand DownExpand Up@@ -203,7 +202,7 @@ def count_interactions(
 return int_matrix if trans_dir else int_matrix.transpose()


@jit(parallel=True)
@njit(parallel=True)
def get_interaction_pvals(
 int_matrix,
 n_perms,
Expand All@@ -216,24 +215,16 @@ def get_interaction_pvals(
 R_bool,
 cell_prop_cutoff,
):
 """ Perturbs the cell labels to get background count frequency to estimate \
 p-values.
 """
 """Gets the p-values for the interaction counts."""

 # Counting how many times permutation of spots cell data creates interaction
 # counts greater than that observed, in order to calculate p-values.
 shape_ = (n_perms, int_matrix.shape[0], int_matrix.shape[1])
 # Storing the instances where the count is greater randomly for each perm.
 # Allows for embarassing parallelisation.
 greater_counts = np.zeros(shape_, dtype=np.int64)
 indices = np.zeros((cell_data.shape[0]), dtype=np.int64)
 for i in range(cell_data.shape[0]):
 indices[i] = i

 # If dealing with discrete data, no need to randomise columns indendently #
 discrete = np.all(np.logical_or(cell_data == 0, cell_data == 1))
 for i in prange(n_perms):
 # Permuting the cell data by swapping between spots for each column #
 if not discrete:
 perm_data = cell_data.copy()
 for j in range(cell_data.shape[1]):
Expand All@@ -243,95 +234,79 @@ def get_interaction_pvals(
 rand_indices = np.random.choice(indices, cell_data.shape[0], False)
 perm_data = cell_data[rand_indices, :]

 # Calculating interactions for permuted labels #
 perm_matrix = get_interaction_matrix(
 perm_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
 )
 # perm_greater = (perm_matrix >= int_matrix).astype(int)
 perm_greater = perm_matrix >= int_matrix
 greater_counts[i, :, :] = perm_greater
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 greater_counts[i, row, col] = (
 perm_matrix[row, col] >= int_matrix[row, col]
 )

 # Calculating the pvalues #
 total_greater_counts = greater_counts.sum(axis=0) # cts * ct counts
 int_pvals = total_greater_counts / n_perms
 # Numba parallel sums axis 0 efficiently
 out = np.zeros((int_matrix.shape[0], int_matrix.shape[1]), dtype=np.float64)
 for i in range(n_perms):
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 out[row, col] += greater_counts[i, row, col]
 int_pvals = out / n_perms
 return int_pvals


@njit
def get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
):
 """Gets the interaction count matrix."""
 # Now counting the interactions under 3 situations:
 # 1) sig spot with ligand, only neighbours with receptor relevant
 # 2) sig spot with receptor, only neighbours with ligand relevant
 # NOTE, A<->B is double counted, but on different side of matrix.
 # (if bidirectional interaction between two spots, counts as two seperate
 # interactions).
 LR_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig ligand->receptor mode
 )
 RL_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 R_bool,
 L_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig receptor->ligand mode
 )

 # Counting the number of unique interacting edges
 # between different cell type via indicate LR
 int_matrix = np.zeros((all_set.shape[0], all_set.shape[0]), dtype=np.int64)
 edge_i = 0
 for i in range(all_set.shape[0]):
 for j in range(all_set.shape[0]):
 RL_Atrans_Bedges = LR_edges[edge_i]
 LR_Atrans_Bedges = RL_edges[edge_i]
 edge_i += 1
 max_len = max([len(RL_Atrans_Bedges), len(LR_Atrans_Bedges)])
 if max_len == 0: # Nothing to count #
 continue

 edge_starts = List()
 edge_ends = List()
 for k in range(max_len):
 if k < len(RL_Atrans_Bedges):
 edge_starts.append(RL_Atrans_Bedges[k][0])
 edge_ends.append(RL_Atrans_Bedges[k][1])
 if k < len(LR_Atrans_Bedges):
 edge_starts.append(LR_Atrans_Bedges[k][0])
 edge_ends.append(LR_Atrans_Bedges[k][1])
 Atrans_Bedges = List()
 Atrans_Bedges.append((edge_starts[0], edge_ends[0])) # for typing
 add_unique_edges(Atrans_Bedges, edge_starts, edge_ends)
 # Atrans_Bedges = np.unique(RL_Atrans_Bedges + LR_Atrans_Bedges)
 int_matrix[i, j] = len(Atrans_Bedges) - 1 # since added edge for type
 """Gets the interaction matrix for a given cell data matrix."""

 n_spots = cell_data.shape[0]
 n_types = all_set.shape[0]
 int_matrix = np.zeros((n_types, n_types), dtype=np.int64)

 for t1 in range(n_types):
 for t2 in range(n_types):
 s = {np.int64(-1)}
 s.clear()

 for i in range(n_spots):
 if not sig_bool[i]:
 continue
 if cell_data[i, t1] <= cell_prop_cutoff:
 continue

 neighs = neighbourhood_indices[i][1]

 for k in range(len(neighs)):
 n_idx = neighs[k]
 if cell_data[n_idx, t2] > cell_prop_cutoff:
 valid = False
 if L_bool[i] and R_bool[n_idx]:
 valid = True
 if R_bool[i] and L_bool[n_idx]:
 valid = True

 if valid:
 u = np.int64(i)
 v = np.int64(n_idx)
 if u > v:
 tmp = u
 u = v
 v = tmp
 s.add((u << 32) | v)

 int_matrix[t1, t2] = len(s)

 return int_matrix

Expand Down

7 changes: 3 additions & 4 deletions
tests/adds/test_row_annotations.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,14 +18,14 @@ def setUpClass(cls):
 f"{test_data_path()}/" + "v1_human_breast_cancer_block_a_section_1.csv"
 )


 def setUp(self):
 """Set up test data with known clusters."""
 self.adata = self.__class__._base_adata.copy()


 def test_add_row_annotations(self):
 row_annotations.row_annotations(self.adata, self.__class__.annotations_path, "ID")
 row_annotations.row_annotations(
 self.adata, self.__class__.annotations_path, "ID"
 )

 assert "annot_type" in self.adata.obs.columns
 assert "fine_annot_type" in self.adata.obs.columns
Expand All@@ -35,7 +35,6 @@ def test_add_row_annotations(self):
 fine_annotated = self.adata.obs["fine_annot_type"].dropna()
 assert len(fine_annotated) == len(annotated)


 def test_add_row_annotations_with_missing_column(self):
 with self.assertRaises(ValueError):
 row_annotations.row_annotations(
Expand Down

1 change: 0 additions & 1 deletion
tests/tl/test_cci.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -279,7 +279,6 @@ def test_get_interaction_matrix(self):
 # Get interaction matrix
 int_matrix = het.get_interaction_matrix(
 cell_data,
 self.neighbourhood_bcs,
 self.neighbourhood_indices,
 CELL_TYPE_LABELS,
 sig_bool,
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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reworked get_interaction_matrix and get_interaction_pvals to be faster with numba#345

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 Merged

newmana
merged 4 commits into

from

Apr 16, 2026
 Merged


reworked get_interaction_matrix and get_interaction_pvals to be faster with numba
#345
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a419ae8

reworked get_interaction_matrix and get_interaction_pvals to be faste…


lhock17 Apr 16, 2026
ccaeefe

Remove unused neighbourhood_bcs parameter from get_interaction_matrix…


lhock17 Apr 16, 2026
749481c

Refactor get_interaction_pvals and get_interaction_matrix for improve…


lhock17 Apr 16, 2026
4444702

fix get_interaction_matrix test


lhock17 Apr 16, 2026

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1 change: 0 additions & 1 deletion
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -710,7 +710,6 @@ def run_cci(

 int_matrix = get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

129 changes: 52 additions & 77 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,7 +8,6 @@
from numba.typed import List

from stlearn.tl.cci.het_helpers import (
 add_unique_edges,
 edge_core,
 get_between_spot_edge_array,
 get_data_for_counting,
Expand DownExpand Up@@ -203,7 +202,7 @@ def count_interactions(
 return int_matrix if trans_dir else int_matrix.transpose()


@jit(parallel=True)
@njit(parallel=True)
def get_interaction_pvals(
 int_matrix,
 n_perms,
Expand All@@ -216,24 +215,16 @@ def get_interaction_pvals(
 R_bool,
 cell_prop_cutoff,
):
 """ Perturbs the cell labels to get background count frequency to estimate \
 p-values.
 """
 """Gets the p-values for the interaction counts."""

 # Counting how many times permutation of spots cell data creates interaction
 # counts greater than that observed, in order to calculate p-values.
 shape_ = (n_perms, int_matrix.shape[0], int_matrix.shape[1])
 # Storing the instances where the count is greater randomly for each perm.
 # Allows for embarassing parallelisation.
 greater_counts = np.zeros(shape_, dtype=np.int64)
 indices = np.zeros((cell_data.shape[0]), dtype=np.int64)
 for i in range(cell_data.shape[0]):
 indices[i] = i

 # If dealing with discrete data, no need to randomise columns indendently #
 discrete = np.all(np.logical_or(cell_data == 0, cell_data == 1))
 for i in prange(n_perms):
 # Permuting the cell data by swapping between spots for each column #
 if not discrete:
 perm_data = cell_data.copy()
 for j in range(cell_data.shape[1]):
Expand All@@ -243,95 +234,79 @@ def get_interaction_pvals(
 rand_indices = np.random.choice(indices, cell_data.shape[0], False)
 perm_data = cell_data[rand_indices, :]

 # Calculating interactions for permuted labels #
 perm_matrix = get_interaction_matrix(
 perm_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
 )
 # perm_greater = (perm_matrix >= int_matrix).astype(int)
 perm_greater = perm_matrix >= int_matrix
 greater_counts[i, :, :] = perm_greater
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 greater_counts[i, row, col] = (
 perm_matrix[row, col] >= int_matrix[row, col]
 )

 # Calculating the pvalues #
 total_greater_counts = greater_counts.sum(axis=0) # cts * ct counts
 int_pvals = total_greater_counts / n_perms
 # Numba parallel sums axis 0 efficiently
 out = np.zeros((int_matrix.shape[0], int_matrix.shape[1]), dtype=np.float64)
 for i in range(n_perms):
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 out[row, col] += greater_counts[i, row, col]
 int_pvals = out / n_perms
 return int_pvals


@njit
def get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
):
 """Gets the interaction count matrix."""
 # Now counting the interactions under 3 situations:
 # 1) sig spot with ligand, only neighbours with receptor relevant
 # 2) sig spot with receptor, only neighbours with ligand relevant
 # NOTE, A<->B is double counted, but on different side of matrix.
 # (if bidirectional interaction between two spots, counts as two seperate
 # interactions).
 LR_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig ligand->receptor mode
 )
 RL_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 R_bool,
 L_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig receptor->ligand mode
 )

 # Counting the number of unique interacting edges
 # between different cell type via indicate LR
 int_matrix = np.zeros((all_set.shape[0], all_set.shape[0]), dtype=np.int64)
 edge_i = 0
 for i in range(all_set.shape[0]):
 for j in range(all_set.shape[0]):
 RL_Atrans_Bedges = LR_edges[edge_i]
 LR_Atrans_Bedges = RL_edges[edge_i]
 edge_i += 1
 max_len = max([len(RL_Atrans_Bedges), len(LR_Atrans_Bedges)])
 if max_len == 0: # Nothing to count #
 continue

 edge_starts = List()
 edge_ends = List()
 for k in range(max_len):
 if k < len(RL_Atrans_Bedges):
 edge_starts.append(RL_Atrans_Bedges[k][0])
 edge_ends.append(RL_Atrans_Bedges[k][1])
 if k < len(LR_Atrans_Bedges):
 edge_starts.append(LR_Atrans_Bedges[k][0])
 edge_ends.append(LR_Atrans_Bedges[k][1])
 Atrans_Bedges = List()
 Atrans_Bedges.append((edge_starts[0], edge_ends[0])) # for typing
 add_unique_edges(Atrans_Bedges, edge_starts, edge_ends)
 # Atrans_Bedges = np.unique(RL_Atrans_Bedges + LR_Atrans_Bedges)
 int_matrix[i, j] = len(Atrans_Bedges) - 1 # since added edge for type
 """Gets the interaction matrix for a given cell data matrix."""

 n_spots = cell_data.shape[0]
 n_types = all_set.shape[0]
 int_matrix = np.zeros((n_types, n_types), dtype=np.int64)

 for t1 in range(n_types):
 for t2 in range(n_types):
 s = {np.int64(-1)}
 s.clear()

 for i in range(n_spots):
 if not sig_bool[i]:
 continue
 if cell_data[i, t1] <= cell_prop_cutoff:
 continue

 neighs = neighbourhood_indices[i][1]

 for k in range(len(neighs)):
 n_idx = neighs[k]
 if cell_data[n_idx, t2] > cell_prop_cutoff:
 valid = False
 if L_bool[i] and R_bool[n_idx]:
 valid = True
 if R_bool[i] and L_bool[n_idx]:
 valid = True

 if valid:
 u = np.int64(i)
 v = np.int64(n_idx)
 if u > v:
 tmp = u
 u = v
 v = tmp
 s.add((u << 32) | v)

 int_matrix[t1, t2] = len(s)

 return int_matrix

Expand Down

7 changes: 3 additions & 4 deletions
tests/adds/test_row_annotations.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,14 +18,14 @@ def setUpClass(cls):
 f"{test_data_path()}/" + "v1_human_breast_cancer_block_a_section_1.csv"
 )


 def setUp(self):
 """Set up test data with known clusters."""
 self.adata = self.__class__._base_adata.copy()


 def test_add_row_annotations(self):
 row_annotations.row_annotations(self.adata, self.__class__.annotations_path, "ID")
 row_annotations.row_annotations(
 self.adata, self.__class__.annotations_path, "ID"
 )

 assert "annot_type" in self.adata.obs.columns
 assert "fine_annot_type" in self.adata.obs.columns
Expand All@@ -35,7 +35,6 @@ def test_add_row_annotations(self):
 fine_annotated = self.adata.obs["fine_annot_type"].dropna()
 assert len(fine_annotated) == len(annotated)


 def test_add_row_annotations_with_missing_column(self):
 with self.assertRaises(ValueError):
 row_annotations.row_annotations(
Expand Down

1 change: 0 additions & 1 deletion
tests/tl/test_cci.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -279,7 +279,6 @@ def test_get_interaction_matrix(self):
 # Get interaction matrix
 int_matrix = het.get_interaction_matrix(
 cell_data,
 self.neighbourhood_bcs,
 self.neighbourhood_indices,
 CELL_TYPE_LABELS,
 sig_bool,
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
reworked get_interaction_matrix and get_interaction_pvals to be faster with numba#345

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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 Merged

newmana
merged 4 commits into

from

Apr 16, 2026
 Merged


reworked get_interaction_matrix and get_interaction_pvals to be faster with numba
#345
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4 commits


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a419ae8

reworked get_interaction_matrix and get_interaction_pvals to be faste…


lhock17 Apr 16, 2026
ccaeefe

Remove unused neighbourhood_bcs parameter from get_interaction_matrix…


lhock17 Apr 16, 2026
749481c

Refactor get_interaction_pvals and get_interaction_matrix for improve…


lhock17 Apr 16, 2026
4444702

fix get_interaction_matrix test


lhock17 Apr 16, 2026

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1 change: 0 additions & 1 deletion
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -710,7 +710,6 @@ def run_cci(

 int_matrix = get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

129 changes: 52 additions & 77 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,7 +8,6 @@
from numba.typed import List

from stlearn.tl.cci.het_helpers import (
 add_unique_edges,
 edge_core,
 get_between_spot_edge_array,
 get_data_for_counting,
Expand DownExpand Up@@ -203,7 +202,7 @@ def count_interactions(
 return int_matrix if trans_dir else int_matrix.transpose()


@jit(parallel=True)
@njit(parallel=True)
def get_interaction_pvals(
 int_matrix,
 n_perms,
Expand All@@ -216,24 +215,16 @@ def get_interaction_pvals(
 R_bool,
 cell_prop_cutoff,
):
 """ Perturbs the cell labels to get background count frequency to estimate \
 p-values.
 """
 """Gets the p-values for the interaction counts."""

 # Counting how many times permutation of spots cell data creates interaction
 # counts greater than that observed, in order to calculate p-values.
 shape_ = (n_perms, int_matrix.shape[0], int_matrix.shape[1])
 # Storing the instances where the count is greater randomly for each perm.
 # Allows for embarassing parallelisation.
 greater_counts = np.zeros(shape_, dtype=np.int64)
 indices = np.zeros((cell_data.shape[0]), dtype=np.int64)
 for i in range(cell_data.shape[0]):
 indices[i] = i

 # If dealing with discrete data, no need to randomise columns indendently #
 discrete = np.all(np.logical_or(cell_data == 0, cell_data == 1))
 for i in prange(n_perms):
 # Permuting the cell data by swapping between spots for each column #
 if not discrete:
 perm_data = cell_data.copy()
 for j in range(cell_data.shape[1]):
Expand All@@ -243,95 +234,79 @@ def get_interaction_pvals(
 rand_indices = np.random.choice(indices, cell_data.shape[0], False)
 perm_data = cell_data[rand_indices, :]

 # Calculating interactions for permuted labels #
 perm_matrix = get_interaction_matrix(
 perm_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
 )
 # perm_greater = (perm_matrix >= int_matrix).astype(int)
 perm_greater = perm_matrix >= int_matrix
 greater_counts[i, :, :] = perm_greater
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 greater_counts[i, row, col] = (
 perm_matrix[row, col] >= int_matrix[row, col]
 )

 # Calculating the pvalues #
 total_greater_counts = greater_counts.sum(axis=0) # cts * ct counts
 int_pvals = total_greater_counts / n_perms
 # Numba parallel sums axis 0 efficiently
 out = np.zeros((int_matrix.shape[0], int_matrix.shape[1]), dtype=np.float64)
 for i in range(n_perms):
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 out[row, col] += greater_counts[i, row, col]
 int_pvals = out / n_perms
 return int_pvals


@njit
def get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
):
 """Gets the interaction count matrix."""
 # Now counting the interactions under 3 situations:
 # 1) sig spot with ligand, only neighbours with receptor relevant
 # 2) sig spot with receptor, only neighbours with ligand relevant
 # NOTE, A<->B is double counted, but on different side of matrix.
 # (if bidirectional interaction between two spots, counts as two seperate
 # interactions).
 LR_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig ligand->receptor mode
 )
 RL_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 R_bool,
 L_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig receptor->ligand mode
 )

 # Counting the number of unique interacting edges
 # between different cell type via indicate LR
 int_matrix = np.zeros((all_set.shape[0], all_set.shape[0]), dtype=np.int64)
 edge_i = 0
 for i in range(all_set.shape[0]):
 for j in range(all_set.shape[0]):
 RL_Atrans_Bedges = LR_edges[edge_i]
 LR_Atrans_Bedges = RL_edges[edge_i]
 edge_i += 1
 max_len = max([len(RL_Atrans_Bedges), len(LR_Atrans_Bedges)])
 if max_len == 0: # Nothing to count #
 continue

 edge_starts = List()
 edge_ends = List()
 for k in range(max_len):
 if k < len(RL_Atrans_Bedges):
 edge_starts.append(RL_Atrans_Bedges[k][0])
 edge_ends.append(RL_Atrans_Bedges[k][1])
 if k < len(LR_Atrans_Bedges):
 edge_starts.append(LR_Atrans_Bedges[k][0])
 edge_ends.append(LR_Atrans_Bedges[k][1])
 Atrans_Bedges = List()
 Atrans_Bedges.append((edge_starts[0], edge_ends[0])) # for typing
 add_unique_edges(Atrans_Bedges, edge_starts, edge_ends)
 # Atrans_Bedges = np.unique(RL_Atrans_Bedges + LR_Atrans_Bedges)
 int_matrix[i, j] = len(Atrans_Bedges) - 1 # since added edge for type
 """Gets the interaction matrix for a given cell data matrix."""

 n_spots = cell_data.shape[0]
 n_types = all_set.shape[0]
 int_matrix = np.zeros((n_types, n_types), dtype=np.int64)

 for t1 in range(n_types):
 for t2 in range(n_types):
 s = {np.int64(-1)}
 s.clear()

 for i in range(n_spots):
 if not sig_bool[i]:
 continue
 if cell_data[i, t1] <= cell_prop_cutoff:
 continue

 neighs = neighbourhood_indices[i][1]

 for k in range(len(neighs)):
 n_idx = neighs[k]
 if cell_data[n_idx, t2] > cell_prop_cutoff:
 valid = False
 if L_bool[i] and R_bool[n_idx]:
 valid = True
 if R_bool[i] and L_bool[n_idx]:
 valid = True

 if valid:
 u = np.int64(i)
 v = np.int64(n_idx)
 if u > v:
 tmp = u
 u = v
 v = tmp
 s.add((u << 32) | v)

 int_matrix[t1, t2] = len(s)

 return int_matrix

Expand Down

7 changes: 3 additions & 4 deletions
tests/adds/test_row_annotations.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,14 +18,14 @@ def setUpClass(cls):
 f"{test_data_path()}/" + "v1_human_breast_cancer_block_a_section_1.csv"
 )


 def setUp(self):
 """Set up test data with known clusters."""
 self.adata = self.__class__._base_adata.copy()


 def test_add_row_annotations(self):
 row_annotations.row_annotations(self.adata, self.__class__.annotations_path, "ID")
 row_annotations.row_annotations(
 self.adata, self.__class__.annotations_path, "ID"
 )

 assert "annot_type" in self.adata.obs.columns
 assert "fine_annot_type" in self.adata.obs.columns
Expand All@@ -35,7 +35,6 @@ def test_add_row_annotations(self):
 fine_annotated = self.adata.obs["fine_annot_type"].dropna()
 assert len(fine_annotated) == len(annotated)


 def test_add_row_annotations_with_missing_column(self):
 with self.assertRaises(ValueError):
 row_annotations.row_annotations(
Expand Down

1 change: 0 additions & 1 deletion
tests/tl/test_cci.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -279,7 +279,6 @@ def test_get_interaction_matrix(self):
 # Get interaction matrix
 int_matrix = het.get_interaction_matrix(
 cell_data,
 self.neighbourhood_bcs,
 self.neighbourhood_indices,
 CELL_TYPE_LABELS,
 sig_bool,
Expand Down
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}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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reworked get_interaction_matrix and get_interaction_pvals to be faster with numba#345

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 Merged

newmana
merged 4 commits into

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Apr 16, 2026
 Merged


reworked get_interaction_matrix and get_interaction_pvals to be faster with numba
#345
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a419ae8

reworked get_interaction_matrix and get_interaction_pvals to be faste…


lhock17 Apr 16, 2026
ccaeefe

Remove unused neighbourhood_bcs parameter from get_interaction_matrix…


lhock17 Apr 16, 2026
749481c

Refactor get_interaction_pvals and get_interaction_matrix for improve…


lhock17 Apr 16, 2026
4444702

fix get_interaction_matrix test


lhock17 Apr 16, 2026

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1 change: 0 additions & 1 deletion
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -710,7 +710,6 @@ def run_cci(

 int_matrix = get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

129 changes: 52 additions & 77 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,7 +8,6 @@
from numba.typed import List

from stlearn.tl.cci.het_helpers import (
 add_unique_edges,
 edge_core,
 get_between_spot_edge_array,
 get_data_for_counting,
Expand DownExpand Up@@ -203,7 +202,7 @@ def count_interactions(
 return int_matrix if trans_dir else int_matrix.transpose()


@jit(parallel=True)
@njit(parallel=True)
def get_interaction_pvals(
 int_matrix,
 n_perms,
Expand All@@ -216,24 +215,16 @@ def get_interaction_pvals(
 R_bool,
 cell_prop_cutoff,
):
 """ Perturbs the cell labels to get background count frequency to estimate \
 p-values.
 """
 """Gets the p-values for the interaction counts."""

 # Counting how many times permutation of spots cell data creates interaction
 # counts greater than that observed, in order to calculate p-values.
 shape_ = (n_perms, int_matrix.shape[0], int_matrix.shape[1])
 # Storing the instances where the count is greater randomly for each perm.
 # Allows for embarassing parallelisation.
 greater_counts = np.zeros(shape_, dtype=np.int64)
 indices = np.zeros((cell_data.shape[0]), dtype=np.int64)
 for i in range(cell_data.shape[0]):
 indices[i] = i

 # If dealing with discrete data, no need to randomise columns indendently #
 discrete = np.all(np.logical_or(cell_data == 0, cell_data == 1))
 for i in prange(n_perms):
 # Permuting the cell data by swapping between spots for each column #
 if not discrete:
 perm_data = cell_data.copy()
 for j in range(cell_data.shape[1]):
Expand All@@ -243,95 +234,79 @@ def get_interaction_pvals(
 rand_indices = np.random.choice(indices, cell_data.shape[0], False)
 perm_data = cell_data[rand_indices, :]

 # Calculating interactions for permuted labels #
 perm_matrix = get_interaction_matrix(
 perm_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
 )
 # perm_greater = (perm_matrix >= int_matrix).astype(int)
 perm_greater = perm_matrix >= int_matrix
 greater_counts[i, :, :] = perm_greater
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 greater_counts[i, row, col] = (
 perm_matrix[row, col] >= int_matrix[row, col]
 )

 # Calculating the pvalues #
 total_greater_counts = greater_counts.sum(axis=0) # cts * ct counts
 int_pvals = total_greater_counts / n_perms
 # Numba parallel sums axis 0 efficiently
 out = np.zeros((int_matrix.shape[0], int_matrix.shape[1]), dtype=np.float64)
 for i in range(n_perms):
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 out[row, col] += greater_counts[i, row, col]
 int_pvals = out / n_perms
 return int_pvals


@njit
def get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
):
 """Gets the interaction count matrix."""
 # Now counting the interactions under 3 situations:
 # 1) sig spot with ligand, only neighbours with receptor relevant
 # 2) sig spot with receptor, only neighbours with ligand relevant
 # NOTE, A<->B is double counted, but on different side of matrix.
 # (if bidirectional interaction between two spots, counts as two seperate
 # interactions).
 LR_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig ligand->receptor mode
 )
 RL_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 R_bool,
 L_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig receptor->ligand mode
 )

 # Counting the number of unique interacting edges
 # between different cell type via indicate LR
 int_matrix = np.zeros((all_set.shape[0], all_set.shape[0]), dtype=np.int64)
 edge_i = 0
 for i in range(all_set.shape[0]):
 for j in range(all_set.shape[0]):
 RL_Atrans_Bedges = LR_edges[edge_i]
 LR_Atrans_Bedges = RL_edges[edge_i]
 edge_i += 1
 max_len = max([len(RL_Atrans_Bedges), len(LR_Atrans_Bedges)])
 if max_len == 0: # Nothing to count #
 continue

 edge_starts = List()
 edge_ends = List()
 for k in range(max_len):
 if k < len(RL_Atrans_Bedges):
 edge_starts.append(RL_Atrans_Bedges[k][0])
 edge_ends.append(RL_Atrans_Bedges[k][1])
 if k < len(LR_Atrans_Bedges):
 edge_starts.append(LR_Atrans_Bedges[k][0])
 edge_ends.append(LR_Atrans_Bedges[k][1])
 Atrans_Bedges = List()
 Atrans_Bedges.append((edge_starts[0], edge_ends[0])) # for typing
 add_unique_edges(Atrans_Bedges, edge_starts, edge_ends)
 # Atrans_Bedges = np.unique(RL_Atrans_Bedges + LR_Atrans_Bedges)
 int_matrix[i, j] = len(Atrans_Bedges) - 1 # since added edge for type
 """Gets the interaction matrix for a given cell data matrix."""

 n_spots = cell_data.shape[0]
 n_types = all_set.shape[0]
 int_matrix = np.zeros((n_types, n_types), dtype=np.int64)

 for t1 in range(n_types):
 for t2 in range(n_types):
 s = {np.int64(-1)}
 s.clear()

 for i in range(n_spots):
 if not sig_bool[i]:
 continue
 if cell_data[i, t1] <= cell_prop_cutoff:
 continue

 neighs = neighbourhood_indices[i][1]

 for k in range(len(neighs)):
 n_idx = neighs[k]
 if cell_data[n_idx, t2] > cell_prop_cutoff:
 valid = False
 if L_bool[i] and R_bool[n_idx]:
 valid = True
 if R_bool[i] and L_bool[n_idx]:
 valid = True

 if valid:
 u = np.int64(i)
 v = np.int64(n_idx)
 if u > v:
 tmp = u
 u = v
 v = tmp
 s.add((u << 32) | v)

 int_matrix[t1, t2] = len(s)

 return int_matrix

Expand Down

7 changes: 3 additions & 4 deletions
tests/adds/test_row_annotations.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,14 +18,14 @@ def setUpClass(cls):
 f"{test_data_path()}/" + "v1_human_breast_cancer_block_a_section_1.csv"
 )


 def setUp(self):
 """Set up test data with known clusters."""
 self.adata = self.__class__._base_adata.copy()


 def test_add_row_annotations(self):
 row_annotations.row_annotations(self.adata, self.__class__.annotations_path, "ID")
 row_annotations.row_annotations(
 self.adata, self.__class__.annotations_path, "ID"
 )

 assert "annot_type" in self.adata.obs.columns
 assert "fine_annot_type" in self.adata.obs.columns
Expand All@@ -35,7 +35,6 @@ def test_add_row_annotations(self):
 fine_annotated = self.adata.obs["fine_annot_type"].dropna()
 assert len(fine_annotated) == len(annotated)


 def test_add_row_annotations_with_missing_column(self):
 with self.assertRaises(ValueError):
 row_annotations.row_annotations(
Expand Down

1 change: 0 additions & 1 deletion
tests/tl/test_cci.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -279,7 +279,6 @@ def test_get_interaction_matrix(self):
 # Get interaction matrix
 int_matrix = het.get_interaction_matrix(
 cell_data,
 self.neighbourhood_bcs,
 self.neighbourhood_indices,
 CELL_TYPE_LABELS,
 sig_bool,
Expand Down
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
reworked get_interaction_matrix and get_interaction_pvals to be faster with numba#345

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 Merged

newmana
merged 4 commits into

from

Apr 16, 2026
 Merged


reworked get_interaction_matrix and get_interaction_pvals to be faster with numba
#345
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a419ae8

reworked get_interaction_matrix and get_interaction_pvals to be faste…


lhock17 Apr 16, 2026
ccaeefe

Remove unused neighbourhood_bcs parameter from get_interaction_matrix…


lhock17 Apr 16, 2026
749481c

Refactor get_interaction_pvals and get_interaction_matrix for improve…


lhock17 Apr 16, 2026
4444702

fix get_interaction_matrix test


lhock17 Apr 16, 2026

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1 change: 0 additions & 1 deletion
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -710,7 +710,6 @@ def run_cci(

 int_matrix = get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

129 changes: 52 additions & 77 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -8,7 +8,6 @@
from numba.typed import List

from stlearn.tl.cci.het_helpers import (
 add_unique_edges,
 edge_core,
 get_between_spot_edge_array,
 get_data_for_counting,
Expand DownExpand Up@@ -203,7 +202,7 @@ def count_interactions(
 return int_matrix if trans_dir else int_matrix.transpose()


@jit(parallel=True)
@njit(parallel=True)
def get_interaction_pvals(
 int_matrix,
 n_perms,
Expand All@@ -216,24 +215,16 @@ def get_interaction_pvals(
 R_bool,
 cell_prop_cutoff,
):
 """ Perturbs the cell labels to get background count frequency to estimate \
 p-values.
 """
 """Gets the p-values for the interaction counts."""

 # Counting how many times permutation of spots cell data creates interaction
 # counts greater than that observed, in order to calculate p-values.
 shape_ = (n_perms, int_matrix.shape[0], int_matrix.shape[1])
 # Storing the instances where the count is greater randomly for each perm.
 # Allows for embarassing parallelisation.
 greater_counts = np.zeros(shape_, dtype=np.int64)
 indices = np.zeros((cell_data.shape[0]), dtype=np.int64)
 for i in range(cell_data.shape[0]):
 indices[i] = i

 # If dealing with discrete data, no need to randomise columns indendently #
 discrete = np.all(np.logical_or(cell_data == 0, cell_data == 1))
 for i in prange(n_perms):
 # Permuting the cell data by swapping between spots for each column #
 if not discrete:
 perm_data = cell_data.copy()
 for j in range(cell_data.shape[1]):
Expand All@@ -243,95 +234,79 @@ def get_interaction_pvals(
 rand_indices = np.random.choice(indices, cell_data.shape[0], False)
 perm_data = cell_data[rand_indices, :]

 # Calculating interactions for permuted labels #
 perm_matrix = get_interaction_matrix(
 perm_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
 )
 # perm_greater = (perm_matrix >= int_matrix).astype(int)
 perm_greater = perm_matrix >= int_matrix
 greater_counts[i, :, :] = perm_greater
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 greater_counts[i, row, col] = (
 perm_matrix[row, col] >= int_matrix[row, col]
 )

 # Calculating the pvalues #
 total_greater_counts = greater_counts.sum(axis=0) # cts * ct counts
 int_pvals = total_greater_counts / n_perms
 # Numba parallel sums axis 0 efficiently
 out = np.zeros((int_matrix.shape[0], int_matrix.shape[1]), dtype=np.float64)
 for i in range(n_perms):
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 out[row, col] += greater_counts[i, row, col]
 int_pvals = out / n_perms
 return int_pvals


@njit
def get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
):
 """Gets the interaction count matrix."""
 # Now counting the interactions under 3 situations:
 # 1) sig spot with ligand, only neighbours with receptor relevant
 # 2) sig spot with receptor, only neighbours with ligand relevant
 # NOTE, A<->B is double counted, but on different side of matrix.
 # (if bidirectional interaction between two spots, counts as two seperate
 # interactions).
 LR_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig ligand->receptor mode
 )
 RL_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 R_bool,
 L_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig receptor->ligand mode
 )

 # Counting the number of unique interacting edges
 # between different cell type via indicate LR
 int_matrix = np.zeros((all_set.shape[0], all_set.shape[0]), dtype=np.int64)
 edge_i = 0
 for i in range(all_set.shape[0]):
 for j in range(all_set.shape[0]):
 RL_Atrans_Bedges = LR_edges[edge_i]
 LR_Atrans_Bedges = RL_edges[edge_i]
 edge_i += 1
 max_len = max([len(RL_Atrans_Bedges), len(LR_Atrans_Bedges)])
 if max_len == 0: # Nothing to count #
 continue

 edge_starts = List()
 edge_ends = List()
 for k in range(max_len):
 if k < len(RL_Atrans_Bedges):
 edge_starts.append(RL_Atrans_Bedges[k][0])
 edge_ends.append(RL_Atrans_Bedges[k][1])
 if k < len(LR_Atrans_Bedges):
 edge_starts.append(LR_Atrans_Bedges[k][0])
 edge_ends.append(LR_Atrans_Bedges[k][1])
 Atrans_Bedges = List()
 Atrans_Bedges.append((edge_starts[0], edge_ends[0])) # for typing
 add_unique_edges(Atrans_Bedges, edge_starts, edge_ends)
 # Atrans_Bedges = np.unique(RL_Atrans_Bedges + LR_Atrans_Bedges)
 int_matrix[i, j] = len(Atrans_Bedges) - 1 # since added edge for type
 """Gets the interaction matrix for a given cell data matrix."""

 n_spots = cell_data.shape[0]
 n_types = all_set.shape[0]
 int_matrix = np.zeros((n_types, n_types), dtype=np.int64)

 for t1 in range(n_types):
 for t2 in range(n_types):
 s = {np.int64(-1)}
 s.clear()

 for i in range(n_spots):
 if not sig_bool[i]:
 continue
 if cell_data[i, t1] <= cell_prop_cutoff:
 continue

 neighs = neighbourhood_indices[i][1]

 for k in range(len(neighs)):
 n_idx = neighs[k]
 if cell_data[n_idx, t2] > cell_prop_cutoff:
 valid = False
 if L_bool[i] and R_bool[n_idx]:
 valid = True
 if R_bool[i] and L_bool[n_idx]:
 valid = True

 if valid:
 u = np.int64(i)
 v = np.int64(n_idx)
 if u > v:
 tmp = u
 u = v
 v = tmp
 s.add((u << 32) | v)

 int_matrix[t1, t2] = len(s)

 return int_matrix

Expand Down

7 changes: 3 additions & 4 deletions
tests/adds/test_row_annotations.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -18,14 +18,14 @@ def setUpClass(cls):
 f"{test_data_path()}/" + "v1_human_breast_cancer_block_a_section_1.csv"
 )


 def setUp(self):
 """Set up test data with known clusters."""
 self.adata = self.__class__._base_adata.copy()


 def test_add_row_annotations(self):
 row_annotations.row_annotations(self.adata, self.__class__.annotations_path, "ID")
 row_annotations.row_annotations(
 self.adata, self.__class__.annotations_path, "ID"
 )

 assert "annot_type" in self.adata.obs.columns
 assert "fine_annot_type" in self.adata.obs.columns
Expand All@@ -35,7 +35,6 @@ def test_add_row_annotations(self):
 fine_annotated = self.adata.obs["fine_annot_type"].dropna()
 assert len(fine_annotated) == len(annotated)


 def test_add_row_annotations_with_missing_column(self):
 with self.assertRaises(ValueError):
 row_annotations.row_annotations(
Expand Down

1 change: 0 additions & 1 deletion
tests/tl/test_cci.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -279,7 +279,6 @@ def test_get_interaction_matrix(self):
 # Get interaction matrix
 int_matrix = het.get_interaction_matrix(
 cell_data,
 self.neighbourhood_bcs,
 self.neighbourhood_indices,
 CELL_TYPE_LABELS,
 sig_bool,
Expand Down
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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reworked get_interaction_matrix and get_interaction_pvals to be faster with numba#345

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 Merged

newmana
merged 4 commits into

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Apr 16, 2026
 Merged


reworked get_interaction_matrix and get_interaction_pvals to be faster with numba
#345
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a419ae8

reworked get_interaction_matrix and get_interaction_pvals to be faste…


lhock17 Apr 16, 2026
ccaeefe

Remove unused neighbourhood_bcs parameter from get_interaction_matrix…


lhock17 Apr 16, 2026
749481c

Refactor get_interaction_pvals and get_interaction_matrix for improve…


lhock17 Apr 16, 2026
4444702

fix get_interaction_matrix test


lhock17 Apr 16, 2026

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1 change: 0 additions & 1 deletion
stlearn/tl/cci/analysis.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -710,7 +710,6 @@ def run_cci(

 int_matrix = get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

129 changes: 52 additions & 77 deletions
stlearn/tl/cci/het.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -8,7 +8,6 @@
from numba.typed import List

from stlearn.tl.cci.het_helpers import (
 add_unique_edges,
 edge_core,
 get_between_spot_edge_array,
 get_data_for_counting,
Expand DownExpand Up@@ -203,7 +202,7 @@ def count_interactions(
 return int_matrix if trans_dir else int_matrix.transpose()


@jit(parallel=True)
@njit(parallel=True)
def get_interaction_pvals(
 int_matrix,
 n_perms,
Expand All@@ -216,24 +215,16 @@ def get_interaction_pvals(
 R_bool,
 cell_prop_cutoff,
):
 """ Perturbs the cell labels to get background count frequency to estimate \
 p-values.
 """
 """Gets the p-values for the interaction counts."""

 # Counting how many times permutation of spots cell data creates interaction
 # counts greater than that observed, in order to calculate p-values.
 shape_ = (n_perms, int_matrix.shape[0], int_matrix.shape[1])
 # Storing the instances where the count is greater randomly for each perm.
 # Allows for embarassing parallelisation.
 greater_counts = np.zeros(shape_, dtype=np.int64)
 indices = np.zeros((cell_data.shape[0]), dtype=np.int64)
 for i in range(cell_data.shape[0]):
 indices[i] = i

 # If dealing with discrete data, no need to randomise columns indendently #
 discrete = np.all(np.logical_or(cell_data == 0, cell_data == 1))
 for i in prange(n_perms):
 # Permuting the cell data by swapping between spots for each column #
 if not discrete:
 perm_data = cell_data.copy()
 for j in range(cell_data.shape[1]):
Expand All@@ -243,95 +234,79 @@ def get_interaction_pvals(
 rand_indices = np.random.choice(indices, cell_data.shape[0], False)
 perm_data = cell_data[rand_indices, :]

 # Calculating interactions for permuted labels #
 perm_matrix = get_interaction_matrix(
 perm_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
 )
 # perm_greater = (perm_matrix >= int_matrix).astype(int)
 perm_greater = perm_matrix >= int_matrix
 greater_counts[i, :, :] = perm_greater
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 greater_counts[i, row, col] = (
 perm_matrix[row, col] >= int_matrix[row, col]
 )

 # Calculating the pvalues #
 total_greater_counts = greater_counts.sum(axis=0) # cts * ct counts
 int_pvals = total_greater_counts / n_perms
 # Numba parallel sums axis 0 efficiently
 out = np.zeros((int_matrix.shape[0], int_matrix.shape[1]), dtype=np.float64)
 for i in range(n_perms):
 for row in range(int_matrix.shape[0]):
 for col in range(int_matrix.shape[1]):
 out[row, col] += greater_counts[i, row, col]
 int_pvals = out / n_perms
 return int_pvals


@njit
def get_interaction_matrix(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff,
):
 """Gets the interaction count matrix."""
 # Now counting the interactions under 3 situations:
 # 1) sig spot with ligand, only neighbours with receptor relevant
 # 2) sig spot with receptor, only neighbours with ligand relevant
 # NOTE, A<->B is double counted, but on different side of matrix.
 # (if bidirectional interaction between two spots, counts as two seperate
 # interactions).
 LR_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 L_bool,
 R_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig ligand->receptor mode
 )
 RL_edges = get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 R_bool,
 L_bool,
 cell_prop_cutoff=cell_prop_cutoff,
 # sig receptor->ligand mode
 )

 # Counting the number of unique interacting edges
 # between different cell type via indicate LR
 int_matrix = np.zeros((all_set.shape[0], all_set.shape[0]), dtype=np.int64)
 edge_i = 0
 for i in range(all_set.shape[0]):
 for j in range(all_set.shape[0]):
 RL_Atrans_Bedges = LR_edges[edge_i]
 LR_Atrans_Bedges = RL_edges[edge_i]
 edge_i += 1
 max_len = max([len(RL_Atrans_Bedges), len(LR_Atrans_Bedges)])
 if max_len == 0: # Nothing to count #
 continue

 edge_starts = List()
 edge_ends = List()
 for k in range(max_len):
 if k < len(RL_Atrans_Bedges):
 edge_starts.append(RL_Atrans_Bedges[k][0])
 edge_ends.append(RL_Atrans_Bedges[k][1])
 if k < len(LR_Atrans_Bedges):
 edge_starts.append(LR_Atrans_Bedges[k][0])
 edge_ends.append(LR_Atrans_Bedges[k][1])
 Atrans_Bedges = List()
 Atrans_Bedges.append((edge_starts[0], edge_ends[0])) # for typing
 add_unique_edges(Atrans_Bedges, edge_starts, edge_ends)
 # Atrans_Bedges = np.unique(RL_Atrans_Bedges + LR_Atrans_Bedges)
 int_matrix[i, j] = len(Atrans_Bedges) - 1 # since added edge for type
 """Gets the interaction matrix for a given cell data matrix."""

 n_spots = cell_data.shape[0]
 n_types = all_set.shape[0]
 int_matrix = np.zeros((n_types, n_types), dtype=np.int64)

 for t1 in range(n_types):
 for t2 in range(n_types):
 s = {np.int64(-1)}
 s.clear()

 for i in range(n_spots):
 if not sig_bool[i]:
 continue
 if cell_data[i, t1] <= cell_prop_cutoff:
 continue

 neighs = neighbourhood_indices[i][1]

 for k in range(len(neighs)):
 n_idx = neighs[k]
 if cell_data[n_idx, t2] > cell_prop_cutoff:
 valid = False
 if L_bool[i] and R_bool[n_idx]:
 valid = True
 if R_bool[i] and L_bool[n_idx]:
 valid = True

 if valid:
 u = np.int64(i)
 v = np.int64(n_idx)
 if u > v:
 tmp = u
 u = v
 v = tmp
 s.add((u << 32) | v)

 int_matrix[t1, t2] = len(s)

 return int_matrix

Expand Down

7 changes: 3 additions & 4 deletions
tests/adds/test_row_annotations.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -18,14 +18,14 @@ def setUpClass(cls):
 f"{test_data_path()}/" + "v1_human_breast_cancer_block_a_section_1.csv"
 )


 def setUp(self):
 """Set up test data with known clusters."""
 self.adata = self.__class__._base_adata.copy()


 def test_add_row_annotations(self):
 row_annotations.row_annotations(self.adata, self.__class__.annotations_path, "ID")
 row_annotations.row_annotations(
 self.adata, self.__class__.annotations_path, "ID"
 )

 assert "annot_type" in self.adata.obs.columns
 assert "fine_annot_type" in self.adata.obs.columns
Expand All@@ -35,7 +35,6 @@ def test_add_row_annotations(self):
 fine_annotated = self.adata.obs["fine_annot_type"].dropna()
 assert len(fine_annotated) == len(annotated)


 def test_add_row_annotations_with_missing_column(self):
 with self.assertRaises(ValueError):
 row_annotations.row_annotations(
Expand Down

1 change: 0 additions & 1 deletion
tests/tl/test_cci.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -279,7 +279,6 @@ def test_get_interaction_matrix(self):
 # Get interaction matrix
 int_matrix = het.get_interaction_matrix(
 cell_data,
 self.neighbourhood_bcs,
 self.neighbourhood_indices,
 CELL_TYPE_LABELS,
 sig_bool,
Expand Down
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