Refactor.#346

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newmana merged 1 commit into from
Apr 17, 2026
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Refactor. #346

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11 changes: 4 additions & 7 deletions stlearn/tl/cci/analysis.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -3,7 +3,6 @@
"""

import os
import os as os

import numba
import numpy as np
Expand DownExpand Up@@ -40,7 +39,7 @@ def load_lrs(names: str | list | None = None, species: str = "human") -> np.ndar
Format of the LR genes, either 'human' or 'mouse'.
Returns
-------
lrs: np.array
lrs: np.ndarray
lr pairs from the database in format ['L1_R1', 'LN_RN']
"""
if names is None:
Expand DownExpand Up@@ -270,7 +269,6 @@ def run(
per spot.
"""
# Setting threads for parallelisation
# Setting threads for paralellisation #
if n_cpus is not None:
numba.set_num_threads(n_cpus)
else:
Expand DownExpand Up@@ -467,7 +465,7 @@ def run_lr_go(
r_path: str
Path to R, must have clusterProfiler, org.Mm.eg.db, and org.Hs.eg.db
installed.
bg_genes: np.array
bg_genes: np.ndarray
Genes to be used as the background. If None, defaults to all genes in
lr database: 'connectomeDB2020_put'.
n_top: int
Expand DownExpand Up@@ -662,12 +660,12 @@ def run_cci(
if verbose:
print("Getting cached neighbourhood information...")
# Getting the neighbourhoods #
_, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

if verbose:
print("Getting information for CCI counting...")

spot_bcs, cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)

lr_summary = adata.uns["lr_summary"]
col_i = 1 if sig_spots else 0
Expand DownExpand Up@@ -723,7 +721,6 @@ def run_cci(
int_matrix,
n_perms,
cell_data,
neighbourhood_bcs,
neighbourhood_indices,
all_set,
sig_bool,
Expand Down
95 changes: 24 additions & 71 deletions stlearn/tl/cci/het.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -95,7 +95,9 @@ def count(
return adata


def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.array):
def get_edges(
adata: AnnData, L_bool: np.ndarray, R_bool: np.ndarray, sig_bool: np.ndarray
):
"""Gets a list edges representing significant interactions.

Parameters
Expand All@@ -116,14 +118,14 @@ def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.a
interactions between spots.
"""
# Getting the neighbourhoods #
neighbours, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

# Getting the edges to draw in-between #
L_spot_indices = np.where(np.logical_and(L_bool, sig_bool))[0]
R_spot_indices = np.where(np.logical_and(R_bool, sig_bool))[0]

# To keep the get_between_spot_edge_array function happy #
cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float_)
cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float64)

# Retrieving the edges #
gene_bools = [R_bool, L_bool]
Expand DownExpand Up@@ -162,12 +164,8 @@ def count_interactions(
):
"""Counts the interactions."""
# Getting minimal information necessary for the counting #
(
spot_bcs,
cell_data,
neighbourhood_bcs,
neighbourhood_indices,
) = get_data_for_counting(adata, use_label, mix_mode, all_set)
cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

# if trans_dir, rows are transmitter cell, cols receiver, otherwise reverse.
int_matrix = np.zeros((len(all_set), len(all_set)), dtype=int)
Expand All@@ -186,7 +184,7 @@ def count_interactions(
A_gene1_sig_indices = np.where(A_gene1_sig_bool)[0]

for j, cell_B in enumerate(all_set): # receiver if trans_dir else transmitter
cellA_cellB_counts = len(
cell_a_cell_b_counts = len(
edge_core(
cell_data,
j,
Expand All@@ -197,7 +195,7 @@ def count_interactions(
cutoff=cell_prop_cutoff,
)
)
int_matrix[i, j] = cellA_cellB_counts
int_matrix[i, j] = cell_a_cell_b_counts

return int_matrix if trans_dir else int_matrix.transpose()

Expand All@@ -207,7 +205,6 @@ def get_interaction_pvals(
int_matrix,
n_perms,
cell_data,
neighbourhood_bcs,
neighbourhood_indices,
all_set,
sig_bool,
Expand All@@ -217,14 +214,20 @@ def get_interaction_pvals(
):
"""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@@ -234,6 +237,7 @@ 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_indices,
Expand DownExpand Up@@ -311,57 +315,6 @@ def get_interaction_matrix(
return int_matrix


@njit
def get_interactions(
cell_data,
neighbourhood_bcs,
neighbourhood_indices,
all_set,
sig_bool,
gene1_bool,
gene2_bool,
cell_prop_cutoff=None,
):
""" Gets spot edges between cell types where the first cell type fits \
criteria of gene1_bool, & second second cell type of gene2_bool.
"""

# Creating list of lists to store edges for respective cell types #
interaction_edges = List()

# Now retrieving the interaction edges #
for i in range(all_set.shape[0]):
# Determining which spots have cell type A #
A_bool_2 = cell_data[:, i] > cell_prop_cutoff
A_gene1_bool = np.logical_and(A_bool_2, gene1_bool)

A_gene1_sig_bool = np.logical_and(A_gene1_bool, sig_bool)
n_true = A_gene1_sig_bool.sum()
A_gene1_sig_indices = np.zeros((1, n_true), dtype=np.int32)[
0, :
] # np.where(A_gene1_sig_bool)[0]
index = 0
for k in range(A_gene1_sig_bool.shape[0]):
if A_gene1_sig_bool[k]:
A_gene1_sig_indices[index] = k
index += 1

for j in range(all_set.shape[0]):
edge_list = edge_core(
cell_data,
j,
neighbourhood_bcs,
neighbourhood_indices,
spot_indices=A_gene1_sig_indices,
neigh_bool=gene2_bool,
cutoff=cell_prop_cutoff,
)

interaction_edges.append(edge_list)

return interaction_edges


def create_grids(adata: AnnData, num_row: int, num_col: int, radius: int = 1):
"""Generate screening grids across the tissue sample
Parameters
Expand DownExpand Up@@ -472,20 +425,20 @@ def count_grid(
@jit(parallel=True, forceobj=True)
def grid_parallel(
grid_coords: np.ndarray,
xedges: np.array,
yedges: np.array,
xedges: np.ndarray,
yedges: np.ndarray,
n_row: int,
n_col: int,
xs: np.array,
ys: np.array,
cell_bcs: np.array,
grid_cell_counts: np.array,
xs: np.ndarray,
ys: np.ndarray,
cell_bcs: np.ndarray,
grid_cell_counts: np.ndarray,
grid_expr: np.ndarray,
cell_expr: np.ndarray,
use_label_bool: bool,
cell_labels: np.array,
cell_labels: np.ndarray,
cell_info: np.ndarray,
cell_set: np.array,
cell_set: np.ndarray,
):
"""Grids the gene expression information."""
# generate grids from top to bottom and left to right
Expand Down
Loading
Loading
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Refactor.#346

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 Merged

newmana
merged 1 commit into

from

Apr 17, 2026
 Merged


Refactor.
#346
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11 changes: 4 additions & 7 deletions
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,7 +3,6 @@
"""

import os
import os as os

import numba
import numpy as np
Expand DownExpand Up@@ -40,7 +39,7 @@ def load_lrs(names: str | list | None = None, species: str = "human") -> np.ndar
 Format of the LR genes, either 'human' or 'mouse'.
 Returns
 -------
 lrs: np.array
 lrs: np.ndarray
 lr pairs from the database in format ['L1_R1', 'LN_RN']
 """
 if names is None:
Expand DownExpand Up@@ -270,7 +269,6 @@ def run(
 per spot.
 """
 # Setting threads for parallelisation
 # Setting threads for paralellisation #
 if n_cpus is not None:
 numba.set_num_threads(n_cpus)
 else:
Expand DownExpand Up@@ -467,7 +465,7 @@ def run_lr_go(
 r_path: str
 Path to R, must have clusterProfiler, org.Mm.eg.db, and org.Hs.eg.db
 installed.
 bg_genes: np.array
 bg_genes: np.ndarray
 Genes to be used as the background. If None, defaults to all genes in
 lr database: 'connectomeDB2020_put'.
 n_top: int
Expand DownExpand Up@@ -662,12 +660,12 @@ def run_cci(
 if verbose:
 print("Getting cached neighbourhood information...")
 # Getting the neighbourhoods #
_, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 if verbose:
 print("Getting information for CCI counting...")

spot_bcs, cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)

 lr_summary = adata.uns["lr_summary"]
 col_i = 1 if sig_spots else 0
Expand DownExpand Up@@ -723,7 +721,6 @@ def run_cci(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

95 changes: 24 additions & 71 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,7 +95,9 @@ def count(
 return adata


def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.array):
def get_edges(
 adata: AnnData, L_bool: np.ndarray, R_bool: np.ndarray, sig_bool: np.ndarray
):
 """Gets a list edges representing significant interactions.

 Parameters
Expand All@@ -116,14 +118,14 @@ def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.a
 interactions between spots.
 """
 # Getting the neighbourhoods #
neighbours, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # Getting the edges to draw in-between #
 L_spot_indices = np.where(np.logical_and(L_bool, sig_bool))[0]
 R_spot_indices = np.where(np.logical_and(R_bool, sig_bool))[0]

 # To keep the get_between_spot_edge_array function happy #
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float_)
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float64)

 # Retrieving the edges #
 gene_bools = [R_bool, L_bool]
Expand DownExpand Up@@ -162,12 +164,8 @@ def count_interactions(
):
 """Counts the interactions."""
 # Getting minimal information necessary for the counting #
 (
 spot_bcs,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 ) = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # if trans_dir, rows are transmitter cell, cols receiver, otherwise reverse.
 int_matrix = np.zeros((len(all_set), len(all_set)), dtype=int)
Expand All@@ -186,7 +184,7 @@ def count_interactions(
 A_gene1_sig_indices = np.where(A_gene1_sig_bool)[0]

 for j, cell_B in enumerate(all_set): # receiver if trans_dir else transmitter
cellA_cellB_counts = len(
cell_a_cell_b_counts = len(
 edge_core(
 cell_data,
 j,
Expand All@@ -197,7 +195,7 @@ def count_interactions(
 cutoff=cell_prop_cutoff,
 )
 )
 int_matrix[i, j] = cellA_cellB_counts
 int_matrix[i, j] = cell_a_cell_b_counts

 return int_matrix if trans_dir else int_matrix.transpose()

Expand All@@ -207,7 +205,6 @@ def get_interaction_pvals(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand All@@ -217,14 +214,20 @@ def get_interaction_pvals(
):
 """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@@ -234,6 +237,7 @@ 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_indices,
Expand DownExpand Up@@ -311,57 +315,6 @@ def get_interaction_matrix(
 return int_matrix


@njit
def get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 gene1_bool,
 gene2_bool,
 cell_prop_cutoff=None,
):
 """ Gets spot edges between cell types where the first cell type fits \
 criteria of gene1_bool, & second second cell type of gene2_bool.
 """

 # Creating list of lists to store edges for respective cell types #
 interaction_edges = List()

 # Now retrieving the interaction edges #
 for i in range(all_set.shape[0]):
 # Determining which spots have cell type A #
 A_bool_2 = cell_data[:, i] > cell_prop_cutoff
 A_gene1_bool = np.logical_and(A_bool_2, gene1_bool)

 A_gene1_sig_bool = np.logical_and(A_gene1_bool, sig_bool)
 n_true = A_gene1_sig_bool.sum()
 A_gene1_sig_indices = np.zeros((1, n_true), dtype=np.int32)[
 0, :
 ] # np.where(A_gene1_sig_bool)[0]
 index = 0
 for k in range(A_gene1_sig_bool.shape[0]):
 if A_gene1_sig_bool[k]:
 A_gene1_sig_indices[index] = k
 index += 1

 for j in range(all_set.shape[0]):
 edge_list = edge_core(
 cell_data,
 j,
 neighbourhood_bcs,
 neighbourhood_indices,
 spot_indices=A_gene1_sig_indices,
 neigh_bool=gene2_bool,
 cutoff=cell_prop_cutoff,
 )

 interaction_edges.append(edge_list)

 return interaction_edges


def create_grids(adata: AnnData, num_row: int, num_col: int, radius: int = 1):
 """Generate screening grids across the tissue sample
 Parameters
Expand DownExpand Up@@ -472,20 +425,20 @@ def count_grid(
@jit(parallel=True, forceobj=True)
def grid_parallel(
 grid_coords: np.ndarray,
 xedges: np.array,
 yedges: np.array,
 xedges: np.ndarray,
 yedges: np.ndarray,
 n_row: int,
 n_col: int,
 xs: np.array,
 ys: np.array,
 cell_bcs: np.array,
 grid_cell_counts: np.array,
 xs: np.ndarray,
 ys: np.ndarray,
 cell_bcs: np.ndarray,
 grid_cell_counts: np.ndarray,
 grid_expr: np.ndarray,
 cell_expr: np.ndarray,
 use_label_bool: bool,
 cell_labels: np.array,
 cell_labels: np.ndarray,
 cell_info: np.ndarray,
 cell_set: np.array,
 cell_set: np.ndarray,
):
 """Grids the gene expression information."""
 # generate grids from top to bottom and left to right
Expand Down
Loading
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
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");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Refactor.#346

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 Merged

newmana
merged 1 commit into

from

Apr 17, 2026
 Merged


Refactor.
#346
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11 changes: 4 additions & 7 deletions
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,7 +3,6 @@
"""

import os
import os as os

import numba
import numpy as np
Expand DownExpand Up@@ -40,7 +39,7 @@ def load_lrs(names: str | list | None = None, species: str = "human") -> np.ndar
 Format of the LR genes, either 'human' or 'mouse'.
 Returns
 -------
 lrs: np.array
 lrs: np.ndarray
 lr pairs from the database in format ['L1_R1', 'LN_RN']
 """
 if names is None:
Expand DownExpand Up@@ -270,7 +269,6 @@ def run(
 per spot.
 """
 # Setting threads for parallelisation
 # Setting threads for paralellisation #
 if n_cpus is not None:
 numba.set_num_threads(n_cpus)
 else:
Expand DownExpand Up@@ -467,7 +465,7 @@ def run_lr_go(
 r_path: str
 Path to R, must have clusterProfiler, org.Mm.eg.db, and org.Hs.eg.db
 installed.
 bg_genes: np.array
 bg_genes: np.ndarray
 Genes to be used as the background. If None, defaults to all genes in
 lr database: 'connectomeDB2020_put'.
 n_top: int
Expand DownExpand Up@@ -662,12 +660,12 @@ def run_cci(
 if verbose:
 print("Getting cached neighbourhood information...")
 # Getting the neighbourhoods #
_, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 if verbose:
 print("Getting information for CCI counting...")

spot_bcs, cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)

 lr_summary = adata.uns["lr_summary"]
 col_i = 1 if sig_spots else 0
Expand DownExpand Up@@ -723,7 +721,6 @@ def run_cci(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

95 changes: 24 additions & 71 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,7 +95,9 @@ def count(
 return adata


def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.array):
def get_edges(
 adata: AnnData, L_bool: np.ndarray, R_bool: np.ndarray, sig_bool: np.ndarray
):
 """Gets a list edges representing significant interactions.

 Parameters
Expand All@@ -116,14 +118,14 @@ def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.a
 interactions between spots.
 """
 # Getting the neighbourhoods #
neighbours, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # Getting the edges to draw in-between #
 L_spot_indices = np.where(np.logical_and(L_bool, sig_bool))[0]
 R_spot_indices = np.where(np.logical_and(R_bool, sig_bool))[0]

 # To keep the get_between_spot_edge_array function happy #
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float_)
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float64)

 # Retrieving the edges #
 gene_bools = [R_bool, L_bool]
Expand DownExpand Up@@ -162,12 +164,8 @@ def count_interactions(
):
 """Counts the interactions."""
 # Getting minimal information necessary for the counting #
 (
 spot_bcs,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 ) = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # if trans_dir, rows are transmitter cell, cols receiver, otherwise reverse.
 int_matrix = np.zeros((len(all_set), len(all_set)), dtype=int)
Expand All@@ -186,7 +184,7 @@ def count_interactions(
 A_gene1_sig_indices = np.where(A_gene1_sig_bool)[0]

 for j, cell_B in enumerate(all_set): # receiver if trans_dir else transmitter
cellA_cellB_counts = len(
cell_a_cell_b_counts = len(
 edge_core(
 cell_data,
 j,
Expand All@@ -197,7 +195,7 @@ def count_interactions(
 cutoff=cell_prop_cutoff,
 )
 )
 int_matrix[i, j] = cellA_cellB_counts
 int_matrix[i, j] = cell_a_cell_b_counts

 return int_matrix if trans_dir else int_matrix.transpose()

Expand All@@ -207,7 +205,6 @@ def get_interaction_pvals(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand All@@ -217,14 +214,20 @@ def get_interaction_pvals(
):
 """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@@ -234,6 +237,7 @@ 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_indices,
Expand DownExpand Up@@ -311,57 +315,6 @@ def get_interaction_matrix(
 return int_matrix


@njit
def get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 gene1_bool,
 gene2_bool,
 cell_prop_cutoff=None,
):
 """ Gets spot edges between cell types where the first cell type fits \
 criteria of gene1_bool, & second second cell type of gene2_bool.
 """

 # Creating list of lists to store edges for respective cell types #
 interaction_edges = List()

 # Now retrieving the interaction edges #
 for i in range(all_set.shape[0]):
 # Determining which spots have cell type A #
 A_bool_2 = cell_data[:, i] > cell_prop_cutoff
 A_gene1_bool = np.logical_and(A_bool_2, gene1_bool)

 A_gene1_sig_bool = np.logical_and(A_gene1_bool, sig_bool)
 n_true = A_gene1_sig_bool.sum()
 A_gene1_sig_indices = np.zeros((1, n_true), dtype=np.int32)[
 0, :
 ] # np.where(A_gene1_sig_bool)[0]
 index = 0
 for k in range(A_gene1_sig_bool.shape[0]):
 if A_gene1_sig_bool[k]:
 A_gene1_sig_indices[index] = k
 index += 1

 for j in range(all_set.shape[0]):
 edge_list = edge_core(
 cell_data,
 j,
 neighbourhood_bcs,
 neighbourhood_indices,
 spot_indices=A_gene1_sig_indices,
 neigh_bool=gene2_bool,
 cutoff=cell_prop_cutoff,
 )

 interaction_edges.append(edge_list)

 return interaction_edges


def create_grids(adata: AnnData, num_row: int, num_col: int, radius: int = 1):
 """Generate screening grids across the tissue sample
 Parameters
Expand DownExpand Up@@ -472,20 +425,20 @@ def count_grid(
@jit(parallel=True, forceobj=True)
def grid_parallel(
 grid_coords: np.ndarray,
 xedges: np.array,
 yedges: np.array,
 xedges: np.ndarray,
 yedges: np.ndarray,
 n_row: int,
 n_col: int,
 xs: np.array,
 ys: np.array,
 cell_bcs: np.array,
 grid_cell_counts: np.array,
 xs: np.ndarray,
 ys: np.ndarray,
 cell_bcs: np.ndarray,
 grid_cell_counts: np.ndarray,
 grid_expr: np.ndarray,
 cell_expr: np.ndarray,
 use_label_bool: bool,
 cell_labels: np.array,
 cell_labels: np.ndarray,
 cell_info: np.ndarray,
 cell_set: np.array,
 cell_set: np.ndarray,
):
 """Grids the gene expression information."""
 # generate grids from top to bottom and left to right
Expand Down
Loading
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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Apr 17, 2026
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Refactor.
#346
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11 changes: 4 additions & 7 deletions
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,7 +3,6 @@
"""

import os
import os as os

import numba
import numpy as np
Expand DownExpand Up@@ -40,7 +39,7 @@ def load_lrs(names: str | list | None = None, species: str = "human") -> np.ndar
 Format of the LR genes, either 'human' or 'mouse'.
 Returns
 -------
 lrs: np.array
 lrs: np.ndarray
 lr pairs from the database in format ['L1_R1', 'LN_RN']
 """
 if names is None:
Expand DownExpand Up@@ -270,7 +269,6 @@ def run(
 per spot.
 """
 # Setting threads for parallelisation
 # Setting threads for paralellisation #
 if n_cpus is not None:
 numba.set_num_threads(n_cpus)
 else:
Expand DownExpand Up@@ -467,7 +465,7 @@ def run_lr_go(
 r_path: str
 Path to R, must have clusterProfiler, org.Mm.eg.db, and org.Hs.eg.db
 installed.
 bg_genes: np.array
 bg_genes: np.ndarray
 Genes to be used as the background. If None, defaults to all genes in
 lr database: 'connectomeDB2020_put'.
 n_top: int
Expand DownExpand Up@@ -662,12 +660,12 @@ def run_cci(
 if verbose:
 print("Getting cached neighbourhood information...")
 # Getting the neighbourhoods #
_, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 if verbose:
 print("Getting information for CCI counting...")

spot_bcs, cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)

 lr_summary = adata.uns["lr_summary"]
 col_i = 1 if sig_spots else 0
Expand DownExpand Up@@ -723,7 +721,6 @@ def run_cci(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

95 changes: 24 additions & 71 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,7 +95,9 @@ def count(
 return adata


def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.array):
def get_edges(
 adata: AnnData, L_bool: np.ndarray, R_bool: np.ndarray, sig_bool: np.ndarray
):
 """Gets a list edges representing significant interactions.

 Parameters
Expand All@@ -116,14 +118,14 @@ def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.a
 interactions between spots.
 """
 # Getting the neighbourhoods #
neighbours, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # Getting the edges to draw in-between #
 L_spot_indices = np.where(np.logical_and(L_bool, sig_bool))[0]
 R_spot_indices = np.where(np.logical_and(R_bool, sig_bool))[0]

 # To keep the get_between_spot_edge_array function happy #
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float_)
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float64)

 # Retrieving the edges #
 gene_bools = [R_bool, L_bool]
Expand DownExpand Up@@ -162,12 +164,8 @@ def count_interactions(
):
 """Counts the interactions."""
 # Getting minimal information necessary for the counting #
 (
 spot_bcs,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 ) = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # if trans_dir, rows are transmitter cell, cols receiver, otherwise reverse.
 int_matrix = np.zeros((len(all_set), len(all_set)), dtype=int)
Expand All@@ -186,7 +184,7 @@ def count_interactions(
 A_gene1_sig_indices = np.where(A_gene1_sig_bool)[0]

 for j, cell_B in enumerate(all_set): # receiver if trans_dir else transmitter
cellA_cellB_counts = len(
cell_a_cell_b_counts = len(
 edge_core(
 cell_data,
 j,
Expand All@@ -197,7 +195,7 @@ def count_interactions(
 cutoff=cell_prop_cutoff,
 )
 )
 int_matrix[i, j] = cellA_cellB_counts
 int_matrix[i, j] = cell_a_cell_b_counts

 return int_matrix if trans_dir else int_matrix.transpose()

Expand All@@ -207,7 +205,6 @@ def get_interaction_pvals(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand All@@ -217,14 +214,20 @@ def get_interaction_pvals(
):
 """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@@ -234,6 +237,7 @@ 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_indices,
Expand DownExpand Up@@ -311,57 +315,6 @@ def get_interaction_matrix(
 return int_matrix


@njit
def get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 gene1_bool,
 gene2_bool,
 cell_prop_cutoff=None,
):
 """ Gets spot edges between cell types where the first cell type fits \
 criteria of gene1_bool, & second second cell type of gene2_bool.
 """

 # Creating list of lists to store edges for respective cell types #
 interaction_edges = List()

 # Now retrieving the interaction edges #
 for i in range(all_set.shape[0]):
 # Determining which spots have cell type A #
 A_bool_2 = cell_data[:, i] > cell_prop_cutoff
 A_gene1_bool = np.logical_and(A_bool_2, gene1_bool)

 A_gene1_sig_bool = np.logical_and(A_gene1_bool, sig_bool)
 n_true = A_gene1_sig_bool.sum()
 A_gene1_sig_indices = np.zeros((1, n_true), dtype=np.int32)[
 0, :
 ] # np.where(A_gene1_sig_bool)[0]
 index = 0
 for k in range(A_gene1_sig_bool.shape[0]):
 if A_gene1_sig_bool[k]:
 A_gene1_sig_indices[index] = k
 index += 1

 for j in range(all_set.shape[0]):
 edge_list = edge_core(
 cell_data,
 j,
 neighbourhood_bcs,
 neighbourhood_indices,
 spot_indices=A_gene1_sig_indices,
 neigh_bool=gene2_bool,
 cutoff=cell_prop_cutoff,
 )

 interaction_edges.append(edge_list)

 return interaction_edges


def create_grids(adata: AnnData, num_row: int, num_col: int, radius: int = 1):
 """Generate screening grids across the tissue sample
 Parameters
Expand DownExpand Up@@ -472,20 +425,20 @@ def count_grid(
@jit(parallel=True, forceobj=True)
def grid_parallel(
 grid_coords: np.ndarray,
 xedges: np.array,
 yedges: np.array,
 xedges: np.ndarray,
 yedges: np.ndarray,
 n_row: int,
 n_col: int,
 xs: np.array,
 ys: np.array,
 cell_bcs: np.array,
 grid_cell_counts: np.array,
 xs: np.ndarray,
 ys: np.ndarray,
 cell_bcs: np.ndarray,
 grid_cell_counts: np.ndarray,
 grid_expr: np.ndarray,
 cell_expr: np.ndarray,
 use_label_bool: bool,
 cell_labels: np.array,
 cell_labels: np.ndarray,
 cell_info: np.ndarray,
 cell_set: np.array,
 cell_set: np.ndarray,
):
 """Grids the gene expression information."""
 # generate grids from top to bottom and left to right
Expand Down
Loading
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" + '

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Refactor.#346

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 Merged

newmana
merged 1 commit into

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Apr 17, 2026
 Merged


Refactor.
#346
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11 changes: 4 additions & 7 deletions
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,7 +3,6 @@
"""

import os
import os as os

import numba
import numpy as np
Expand DownExpand Up@@ -40,7 +39,7 @@ def load_lrs(names: str | list | None = None, species: str = "human") -> np.ndar
 Format of the LR genes, either 'human' or 'mouse'.
 Returns
 -------
 lrs: np.array
 lrs: np.ndarray
 lr pairs from the database in format ['L1_R1', 'LN_RN']
 """
 if names is None:
Expand DownExpand Up@@ -270,7 +269,6 @@ def run(
 per spot.
 """
 # Setting threads for parallelisation
 # Setting threads for paralellisation #
 if n_cpus is not None:
 numba.set_num_threads(n_cpus)
 else:
Expand DownExpand Up@@ -467,7 +465,7 @@ def run_lr_go(
 r_path: str
 Path to R, must have clusterProfiler, org.Mm.eg.db, and org.Hs.eg.db
 installed.
 bg_genes: np.array
 bg_genes: np.ndarray
 Genes to be used as the background. If None, defaults to all genes in
 lr database: 'connectomeDB2020_put'.
 n_top: int
Expand DownExpand Up@@ -662,12 +660,12 @@ def run_cci(
 if verbose:
 print("Getting cached neighbourhood information...")
 # Getting the neighbourhoods #
_, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 if verbose:
 print("Getting information for CCI counting...")

spot_bcs, cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)

 lr_summary = adata.uns["lr_summary"]
 col_i = 1 if sig_spots else 0
Expand DownExpand Up@@ -723,7 +721,6 @@ def run_cci(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

95 changes: 24 additions & 71 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,7 +95,9 @@ def count(
 return adata


def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.array):
def get_edges(
 adata: AnnData, L_bool: np.ndarray, R_bool: np.ndarray, sig_bool: np.ndarray
):
 """Gets a list edges representing significant interactions.

 Parameters
Expand All@@ -116,14 +118,14 @@ def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.a
 interactions between spots.
 """
 # Getting the neighbourhoods #
neighbours, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # Getting the edges to draw in-between #
 L_spot_indices = np.where(np.logical_and(L_bool, sig_bool))[0]
 R_spot_indices = np.where(np.logical_and(R_bool, sig_bool))[0]

 # To keep the get_between_spot_edge_array function happy #
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float_)
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float64)

 # Retrieving the edges #
 gene_bools = [R_bool, L_bool]
Expand DownExpand Up@@ -162,12 +164,8 @@ def count_interactions(
):
 """Counts the interactions."""
 # Getting minimal information necessary for the counting #
 (
 spot_bcs,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 ) = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # if trans_dir, rows are transmitter cell, cols receiver, otherwise reverse.
 int_matrix = np.zeros((len(all_set), len(all_set)), dtype=int)
Expand All@@ -186,7 +184,7 @@ def count_interactions(
 A_gene1_sig_indices = np.where(A_gene1_sig_bool)[0]

 for j, cell_B in enumerate(all_set): # receiver if trans_dir else transmitter
cellA_cellB_counts = len(
cell_a_cell_b_counts = len(
 edge_core(
 cell_data,
 j,
Expand All@@ -197,7 +195,7 @@ def count_interactions(
 cutoff=cell_prop_cutoff,
 )
 )
 int_matrix[i, j] = cellA_cellB_counts
 int_matrix[i, j] = cell_a_cell_b_counts

 return int_matrix if trans_dir else int_matrix.transpose()

Expand All@@ -207,7 +205,6 @@ def get_interaction_pvals(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand All@@ -217,14 +214,20 @@ def get_interaction_pvals(
):
 """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@@ -234,6 +237,7 @@ 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_indices,
Expand DownExpand Up@@ -311,57 +315,6 @@ def get_interaction_matrix(
 return int_matrix


@njit
def get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 gene1_bool,
 gene2_bool,
 cell_prop_cutoff=None,
):
 """ Gets spot edges between cell types where the first cell type fits \
 criteria of gene1_bool, & second second cell type of gene2_bool.
 """

 # Creating list of lists to store edges for respective cell types #
 interaction_edges = List()

 # Now retrieving the interaction edges #
 for i in range(all_set.shape[0]):
 # Determining which spots have cell type A #
 A_bool_2 = cell_data[:, i] > cell_prop_cutoff
 A_gene1_bool = np.logical_and(A_bool_2, gene1_bool)

 A_gene1_sig_bool = np.logical_and(A_gene1_bool, sig_bool)
 n_true = A_gene1_sig_bool.sum()
 A_gene1_sig_indices = np.zeros((1, n_true), dtype=np.int32)[
 0, :
 ] # np.where(A_gene1_sig_bool)[0]
 index = 0
 for k in range(A_gene1_sig_bool.shape[0]):
 if A_gene1_sig_bool[k]:
 A_gene1_sig_indices[index] = k
 index += 1

 for j in range(all_set.shape[0]):
 edge_list = edge_core(
 cell_data,
 j,
 neighbourhood_bcs,
 neighbourhood_indices,
 spot_indices=A_gene1_sig_indices,
 neigh_bool=gene2_bool,
 cutoff=cell_prop_cutoff,
 )

 interaction_edges.append(edge_list)

 return interaction_edges


def create_grids(adata: AnnData, num_row: int, num_col: int, radius: int = 1):
 """Generate screening grids across the tissue sample
 Parameters
Expand DownExpand Up@@ -472,20 +425,20 @@ def count_grid(
@jit(parallel=True, forceobj=True)
def grid_parallel(
 grid_coords: np.ndarray,
 xedges: np.array,
 yedges: np.array,
 xedges: np.ndarray,
 yedges: np.ndarray,
 n_row: int,
 n_col: int,
 xs: np.array,
 ys: np.array,
 cell_bcs: np.array,
 grid_cell_counts: np.array,
 xs: np.ndarray,
 ys: np.ndarray,
 cell_bcs: np.ndarray,
 grid_cell_counts: np.ndarray,
 grid_expr: np.ndarray,
 cell_expr: np.ndarray,
 use_label_bool: bool,
 cell_labels: np.array,
 cell_labels: np.ndarray,
 cell_info: np.ndarray,
 cell_set: np.array,
 cell_set: np.ndarray,
):
 """Grids the gene expression information."""
 # generate grids from top to bottom and left to right
Expand Down
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Refactor.
#346
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11 changes: 4 additions & 7 deletions
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,7 +3,6 @@
"""

import os
import os as os

import numba
import numpy as np
Expand DownExpand Up@@ -40,7 +39,7 @@ def load_lrs(names: str | list | None = None, species: str = "human") -> np.ndar
 Format of the LR genes, either 'human' or 'mouse'.
 Returns
 -------
 lrs: np.array
 lrs: np.ndarray
 lr pairs from the database in format ['L1_R1', 'LN_RN']
 """
 if names is None:
Expand DownExpand Up@@ -270,7 +269,6 @@ def run(
 per spot.
 """
 # Setting threads for parallelisation
 # Setting threads for paralellisation #
 if n_cpus is not None:
 numba.set_num_threads(n_cpus)
 else:
Expand DownExpand Up@@ -467,7 +465,7 @@ def run_lr_go(
 r_path: str
 Path to R, must have clusterProfiler, org.Mm.eg.db, and org.Hs.eg.db
 installed.
 bg_genes: np.array
 bg_genes: np.ndarray
 Genes to be used as the background. If None, defaults to all genes in
 lr database: 'connectomeDB2020_put'.
 n_top: int
Expand DownExpand Up@@ -662,12 +660,12 @@ def run_cci(
 if verbose:
 print("Getting cached neighbourhood information...")
 # Getting the neighbourhoods #
_, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 if verbose:
 print("Getting information for CCI counting...")

spot_bcs, cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)

 lr_summary = adata.uns["lr_summary"]
 col_i = 1 if sig_spots else 0
Expand DownExpand Up@@ -723,7 +721,6 @@ def run_cci(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

95 changes: 24 additions & 71 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,7 +95,9 @@ def count(
 return adata


def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.array):
def get_edges(
 adata: AnnData, L_bool: np.ndarray, R_bool: np.ndarray, sig_bool: np.ndarray
):
 """Gets a list edges representing significant interactions.

 Parameters
Expand All@@ -116,14 +118,14 @@ def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.a
 interactions between spots.
 """
 # Getting the neighbourhoods #
neighbours, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # Getting the edges to draw in-between #
 L_spot_indices = np.where(np.logical_and(L_bool, sig_bool))[0]
 R_spot_indices = np.where(np.logical_and(R_bool, sig_bool))[0]

 # To keep the get_between_spot_edge_array function happy #
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float_)
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float64)

 # Retrieving the edges #
 gene_bools = [R_bool, L_bool]
Expand DownExpand Up@@ -162,12 +164,8 @@ def count_interactions(
):
 """Counts the interactions."""
 # Getting minimal information necessary for the counting #
 (
 spot_bcs,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 ) = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # if trans_dir, rows are transmitter cell, cols receiver, otherwise reverse.
 int_matrix = np.zeros((len(all_set), len(all_set)), dtype=int)
Expand All@@ -186,7 +184,7 @@ def count_interactions(
 A_gene1_sig_indices = np.where(A_gene1_sig_bool)[0]

 for j, cell_B in enumerate(all_set): # receiver if trans_dir else transmitter
cellA_cellB_counts = len(
cell_a_cell_b_counts = len(
 edge_core(
 cell_data,
 j,
Expand All@@ -197,7 +195,7 @@ def count_interactions(
 cutoff=cell_prop_cutoff,
 )
 )
 int_matrix[i, j] = cellA_cellB_counts
 int_matrix[i, j] = cell_a_cell_b_counts

 return int_matrix if trans_dir else int_matrix.transpose()

Expand All@@ -207,7 +205,6 @@ def get_interaction_pvals(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand All@@ -217,14 +214,20 @@ def get_interaction_pvals(
):
 """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@@ -234,6 +237,7 @@ 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_indices,
Expand DownExpand Up@@ -311,57 +315,6 @@ def get_interaction_matrix(
 return int_matrix


@njit
def get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 gene1_bool,
 gene2_bool,
 cell_prop_cutoff=None,
):
 """ Gets spot edges between cell types where the first cell type fits \
 criteria of gene1_bool, & second second cell type of gene2_bool.
 """

 # Creating list of lists to store edges for respective cell types #
 interaction_edges = List()

 # Now retrieving the interaction edges #
 for i in range(all_set.shape[0]):
 # Determining which spots have cell type A #
 A_bool_2 = cell_data[:, i] > cell_prop_cutoff
 A_gene1_bool = np.logical_and(A_bool_2, gene1_bool)

 A_gene1_sig_bool = np.logical_and(A_gene1_bool, sig_bool)
 n_true = A_gene1_sig_bool.sum()
 A_gene1_sig_indices = np.zeros((1, n_true), dtype=np.int32)[
 0, :
 ] # np.where(A_gene1_sig_bool)[0]
 index = 0
 for k in range(A_gene1_sig_bool.shape[0]):
 if A_gene1_sig_bool[k]:
 A_gene1_sig_indices[index] = k
 index += 1

 for j in range(all_set.shape[0]):
 edge_list = edge_core(
 cell_data,
 j,
 neighbourhood_bcs,
 neighbourhood_indices,
 spot_indices=A_gene1_sig_indices,
 neigh_bool=gene2_bool,
 cutoff=cell_prop_cutoff,
 )

 interaction_edges.append(edge_list)

 return interaction_edges


def create_grids(adata: AnnData, num_row: int, num_col: int, radius: int = 1):
 """Generate screening grids across the tissue sample
 Parameters
Expand DownExpand Up@@ -472,20 +425,20 @@ def count_grid(
@jit(parallel=True, forceobj=True)
def grid_parallel(
 grid_coords: np.ndarray,
 xedges: np.array,
 yedges: np.array,
 xedges: np.ndarray,
 yedges: np.ndarray,
 n_row: int,
 n_col: int,
 xs: np.array,
 ys: np.array,
 cell_bcs: np.array,
 grid_cell_counts: np.array,
 xs: np.ndarray,
 ys: np.ndarray,
 cell_bcs: np.ndarray,
 grid_cell_counts: np.ndarray,
 grid_expr: np.ndarray,
 cell_expr: np.ndarray,
 use_label_bool: bool,
 cell_labels: np.array,
 cell_labels: np.ndarray,
 cell_info: np.ndarray,
 cell_set: np.array,
 cell_set: np.ndarray,
):
 """Grids the gene expression information."""
 # generate grids from top to bottom and left to right
Expand Down
Loading
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");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Refactor.#346

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 Merged

newmana
merged 1 commit into

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Apr 17, 2026
 Merged


Refactor.
#346
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11 changes: 4 additions & 7 deletions
stlearn/tl/cci/analysis.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,7 +3,6 @@
"""

import os
import os as os

import numba
import numpy as np
Expand DownExpand Up@@ -40,7 +39,7 @@ def load_lrs(names: str | list | None = None, species: str = "human") -> np.ndar
 Format of the LR genes, either 'human' or 'mouse'.
 Returns
 -------
 lrs: np.array
 lrs: np.ndarray
 lr pairs from the database in format ['L1_R1', 'LN_RN']
 """
 if names is None:
Expand DownExpand Up@@ -270,7 +269,6 @@ def run(
 per spot.
 """
 # Setting threads for parallelisation
 # Setting threads for paralellisation #
 if n_cpus is not None:
 numba.set_num_threads(n_cpus)
 else:
Expand DownExpand Up@@ -467,7 +465,7 @@ def run_lr_go(
 r_path: str
 Path to R, must have clusterProfiler, org.Mm.eg.db, and org.Hs.eg.db
 installed.
 bg_genes: np.array
 bg_genes: np.ndarray
 Genes to be used as the background. If None, defaults to all genes in
 lr database: 'connectomeDB2020_put'.
 n_top: int
Expand DownExpand Up@@ -662,12 +660,12 @@ def run_cci(
 if verbose:
 print("Getting cached neighbourhood information...")
 # Getting the neighbourhoods #
_, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 if verbose:
 print("Getting information for CCI counting...")

spot_bcs, cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)

 lr_summary = adata.uns["lr_summary"]
 col_i = 1 if sig_spots else 0
Expand DownExpand Up@@ -723,7 +721,6 @@ def run_cci(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

95 changes: 24 additions & 71 deletions
stlearn/tl/cci/het.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,7 +95,9 @@ def count(
 return adata


def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.array):
def get_edges(
 adata: AnnData, L_bool: np.ndarray, R_bool: np.ndarray, sig_bool: np.ndarray
):
 """Gets a list edges representing significant interactions.

 Parameters
Expand All@@ -116,14 +118,14 @@ def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.a
 interactions between spots.
 """
 # Getting the neighbourhoods #
neighbours, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # Getting the edges to draw in-between #
 L_spot_indices = np.where(np.logical_and(L_bool, sig_bool))[0]
 R_spot_indices = np.where(np.logical_and(R_bool, sig_bool))[0]

 # To keep the get_between_spot_edge_array function happy #
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float_)
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float64)

 # Retrieving the edges #
 gene_bools = [R_bool, L_bool]
Expand DownExpand Up@@ -162,12 +164,8 @@ def count_interactions(
):
 """Counts the interactions."""
 # Getting minimal information necessary for the counting #
 (
 spot_bcs,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 ) = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # if trans_dir, rows are transmitter cell, cols receiver, otherwise reverse.
 int_matrix = np.zeros((len(all_set), len(all_set)), dtype=int)
Expand All@@ -186,7 +184,7 @@ def count_interactions(
 A_gene1_sig_indices = np.where(A_gene1_sig_bool)[0]

 for j, cell_B in enumerate(all_set): # receiver if trans_dir else transmitter
cellA_cellB_counts = len(
cell_a_cell_b_counts = len(
 edge_core(
 cell_data,
 j,
Expand All@@ -197,7 +195,7 @@ def count_interactions(
 cutoff=cell_prop_cutoff,
 )
 )
 int_matrix[i, j] = cellA_cellB_counts
 int_matrix[i, j] = cell_a_cell_b_counts

 return int_matrix if trans_dir else int_matrix.transpose()

Expand All@@ -207,7 +205,6 @@ def get_interaction_pvals(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand All@@ -217,14 +214,20 @@ def get_interaction_pvals(
):
 """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@@ -234,6 +237,7 @@ 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_indices,
Expand DownExpand Up@@ -311,57 +315,6 @@ def get_interaction_matrix(
 return int_matrix


@njit
def get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 gene1_bool,
 gene2_bool,
 cell_prop_cutoff=None,
):
 """ Gets spot edges between cell types where the first cell type fits \
 criteria of gene1_bool, & second second cell type of gene2_bool.
 """

 # Creating list of lists to store edges for respective cell types #
 interaction_edges = List()

 # Now retrieving the interaction edges #
 for i in range(all_set.shape[0]):
 # Determining which spots have cell type A #
 A_bool_2 = cell_data[:, i] > cell_prop_cutoff
 A_gene1_bool = np.logical_and(A_bool_2, gene1_bool)

 A_gene1_sig_bool = np.logical_and(A_gene1_bool, sig_bool)
 n_true = A_gene1_sig_bool.sum()
 A_gene1_sig_indices = np.zeros((1, n_true), dtype=np.int32)[
 0, :
 ] # np.where(A_gene1_sig_bool)[0]
 index = 0
 for k in range(A_gene1_sig_bool.shape[0]):
 if A_gene1_sig_bool[k]:
 A_gene1_sig_indices[index] = k
 index += 1

 for j in range(all_set.shape[0]):
 edge_list = edge_core(
 cell_data,
 j,
 neighbourhood_bcs,
 neighbourhood_indices,
 spot_indices=A_gene1_sig_indices,
 neigh_bool=gene2_bool,
 cutoff=cell_prop_cutoff,
 )

 interaction_edges.append(edge_list)

 return interaction_edges


def create_grids(adata: AnnData, num_row: int, num_col: int, radius: int = 1):
 """Generate screening grids across the tissue sample
 Parameters
Expand DownExpand Up@@ -472,20 +425,20 @@ def count_grid(
@jit(parallel=True, forceobj=True)
def grid_parallel(
 grid_coords: np.ndarray,
 xedges: np.array,
 yedges: np.array,
 xedges: np.ndarray,
 yedges: np.ndarray,
 n_row: int,
 n_col: int,
 xs: np.array,
 ys: np.array,
 cell_bcs: np.array,
 grid_cell_counts: np.array,
 xs: np.ndarray,
 ys: np.ndarray,
 cell_bcs: np.ndarray,
 grid_cell_counts: np.ndarray,
 grid_expr: np.ndarray,
 cell_expr: np.ndarray,
 use_label_bool: bool,
 cell_labels: np.array,
 cell_labels: np.ndarray,
 cell_info: np.ndarray,
 cell_set: np.array,
 cell_set: np.ndarray,
):
 """Grids the gene expression information."""
 # generate grids from top to bottom and left to right
Expand Down
Loading
Loading

, '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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newmana
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Apr 17, 2026
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Refactor.
#346
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11 changes: 4 additions & 7 deletions
stlearn/tl/cci/analysis.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,7 +3,6 @@
"""

import os
import os as os

import numba
import numpy as np
Expand DownExpand Up@@ -40,7 +39,7 @@ def load_lrs(names: str | list | None = None, species: str = "human") -> np.ndar
 Format of the LR genes, either 'human' or 'mouse'.
 Returns
 -------
 lrs: np.array
 lrs: np.ndarray
 lr pairs from the database in format ['L1_R1', 'LN_RN']
 """
 if names is None:
Expand DownExpand Up@@ -270,7 +269,6 @@ def run(
 per spot.
 """
 # Setting threads for parallelisation
 # Setting threads for paralellisation #
 if n_cpus is not None:
 numba.set_num_threads(n_cpus)
 else:
Expand DownExpand Up@@ -467,7 +465,7 @@ def run_lr_go(
 r_path: str
 Path to R, must have clusterProfiler, org.Mm.eg.db, and org.Hs.eg.db
 installed.
 bg_genes: np.array
 bg_genes: np.ndarray
 Genes to be used as the background. If None, defaults to all genes in
 lr database: 'connectomeDB2020_put'.
 n_top: int
Expand DownExpand Up@@ -662,12 +660,12 @@ def run_cci(
 if verbose:
 print("Getting cached neighbourhood information...")
 # Getting the neighbourhoods #
_, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 if verbose:
 print("Getting information for CCI counting...")

spot_bcs, cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)

 lr_summary = adata.uns["lr_summary"]
 col_i = 1 if sig_spots else 0
Expand DownExpand Up@@ -723,7 +721,6 @@ def run_cci(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand Down

95 changes: 24 additions & 71 deletions
stlearn/tl/cci/het.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,7 +95,9 @@ def count(
 return adata


def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.array):
def get_edges(
 adata: AnnData, L_bool: np.ndarray, R_bool: np.ndarray, sig_bool: np.ndarray
):
 """Gets a list edges representing significant interactions.

 Parameters
Expand All@@ -116,14 +118,14 @@ def get_edges(adata: AnnData, L_bool: np.array, R_bool: np.array, sig_bool: np.a
 interactions between spots.
 """
 # Getting the neighbourhoods #
neighbours, neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # Getting the edges to draw in-between #
 L_spot_indices = np.where(np.logical_and(L_bool, sig_bool))[0]
 R_spot_indices = np.where(np.logical_and(R_bool, sig_bool))[0]

 # To keep the get_between_spot_edge_array function happy #
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float_)
 cell_data = np.ones((1, len(sig_bool)))[0, :].astype(np.float64)

 # Retrieving the edges #
 gene_bools = [R_bool, L_bool]
Expand DownExpand Up@@ -162,12 +164,8 @@ def count_interactions(
):
 """Counts the interactions."""
 # Getting minimal information necessary for the counting #
 (
 spot_bcs,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 ) = get_data_for_counting(adata, use_label, mix_mode, all_set)
 cell_data = get_data_for_counting(adata, use_label, mix_mode, all_set)
 neighbourhood_bcs, neighbourhood_indices = get_neighbourhoods(adata)

 # if trans_dir, rows are transmitter cell, cols receiver, otherwise reverse.
 int_matrix = np.zeros((len(all_set), len(all_set)), dtype=int)
Expand All@@ -186,7 +184,7 @@ def count_interactions(
 A_gene1_sig_indices = np.where(A_gene1_sig_bool)[0]

 for j, cell_B in enumerate(all_set): # receiver if trans_dir else transmitter
cellA_cellB_counts = len(
cell_a_cell_b_counts = len(
 edge_core(
 cell_data,
 j,
Expand All@@ -197,7 +195,7 @@ def count_interactions(
 cutoff=cell_prop_cutoff,
 )
 )
 int_matrix[i, j] = cellA_cellB_counts
 int_matrix[i, j] = cell_a_cell_b_counts

 return int_matrix if trans_dir else int_matrix.transpose()

Expand All@@ -207,7 +205,6 @@ def get_interaction_pvals(
 int_matrix,
 n_perms,
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
Expand All@@ -217,14 +214,20 @@ def get_interaction_pvals(
):
 """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@@ -234,6 +237,7 @@ 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_indices,
Expand DownExpand Up@@ -311,57 +315,6 @@ def get_interaction_matrix(
 return int_matrix


@njit
def get_interactions(
 cell_data,
 neighbourhood_bcs,
 neighbourhood_indices,
 all_set,
 sig_bool,
 gene1_bool,
 gene2_bool,
 cell_prop_cutoff=None,
):
 """ Gets spot edges between cell types where the first cell type fits \
 criteria of gene1_bool, & second second cell type of gene2_bool.
 """

 # Creating list of lists to store edges for respective cell types #
 interaction_edges = List()

 # Now retrieving the interaction edges #
 for i in range(all_set.shape[0]):
 # Determining which spots have cell type A #
 A_bool_2 = cell_data[:, i] > cell_prop_cutoff
 A_gene1_bool = np.logical_and(A_bool_2, gene1_bool)

 A_gene1_sig_bool = np.logical_and(A_gene1_bool, sig_bool)
 n_true = A_gene1_sig_bool.sum()
 A_gene1_sig_indices = np.zeros((1, n_true), dtype=np.int32)[
 0, :
 ] # np.where(A_gene1_sig_bool)[0]
 index = 0
 for k in range(A_gene1_sig_bool.shape[0]):
 if A_gene1_sig_bool[k]:
 A_gene1_sig_indices[index] = k
 index += 1

 for j in range(all_set.shape[0]):
 edge_list = edge_core(
 cell_data,
 j,
 neighbourhood_bcs,
 neighbourhood_indices,
 spot_indices=A_gene1_sig_indices,
 neigh_bool=gene2_bool,
 cutoff=cell_prop_cutoff,
 )

 interaction_edges.append(edge_list)

 return interaction_edges


def create_grids(adata: AnnData, num_row: int, num_col: int, radius: int = 1):
 """Generate screening grids across the tissue sample
 Parameters
Expand DownExpand Up@@ -472,20 +425,20 @@ def count_grid(
@jit(parallel=True, forceobj=True)
def grid_parallel(
 grid_coords: np.ndarray,
 xedges: np.array,
 yedges: np.array,
 xedges: np.ndarray,
 yedges: np.ndarray,
 n_row: int,
 n_col: int,
 xs: np.array,
 ys: np.array,
 cell_bcs: np.array,
 grid_cell_counts: np.array,
 xs: np.ndarray,
 ys: np.ndarray,
 cell_bcs: np.ndarray,
 grid_cell_counts: np.ndarray,
 grid_expr: np.ndarray,
 cell_expr: np.ndarray,
 use_label_bool: bool,
 cell_labels: np.array,
 cell_labels: np.ndarray,
 cell_info: np.ndarray,
 cell_set: np.array,
 cell_set: np.ndarray,
):
 """Grids the gene expression information."""
 # generate grids from top to bottom and left to right
Expand Down
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