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reformat
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@Sichao25
Sichao25 committed Jul 23, 2024
1 parent a4c36d6 commit c526925
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Showing 5 changed files with 75 additions and 61 deletions.
4 changes: 3 additions & 1 deletion spateo/svg/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -139,7 +139,9 @@ def add_pos_ratio_to_adata(adata: AnnData, layer: str = None, var_name: str = "r
else np.sum(adata.layers[layer] > 0, axis=0)
)
else:
adata.var[var_name] = np.sum(adata.X.toarray() > 0, axis=0) if issparse(adata.X) else np.sum(adata.X > 0, axis=0)
adata.var[var_name] = (
np.sum(adata.X.toarray() > 0, axis=0) if issparse(adata.X) else np.sum(adata.X > 0, axis=0)
)
adata.var[var_name] = adata.var[var_name] / adata.n_obs


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31 changes: 16 additions & 15 deletions spateo/tools/CCI_effects_modeling/MuSIC.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
Modeling cell-cell communication using a regression model that is considerate of the spatial heterogeneity of (and thus
the context-dependency of the relationships of) the response variable.
"""

import argparse
import itertools
import json
Expand Down Expand Up @@ -1699,13 +1700,11 @@ def define_sig_inputs(self, adata: Optional[anndata.AnnData] = None, recompute:
threshold = (
0.67
if len(receptor_cols) == 2
else 0.5
if len(receptor_cols) == 3
else 0.4
if len(receptor_cols) == 4
else 0.33
if len(receptor_cols) >= 5
else 1
else (
0.5
if len(receptor_cols) == 3
else 0.4 if len(receptor_cols) == 4 else 0.33 if len(receptor_cols) >= 5 else 1
)
)
# If overlap is greater than threshold, combine columns
if len(receptor_cols) > 1 and overlap > threshold:
Expand Down Expand Up @@ -1746,13 +1745,11 @@ def define_sig_inputs(self, adata: Optional[anndata.AnnData] = None, recompute:
threshold = (
0.67
if len(combined_cols) == 2
else 0.5
if len(combined_cols) == 3
else 0.4
if len(combined_cols) == 4
else 0.33
if len(combined_cols) >= 5
else 1
else (
0.5
if len(combined_cols) == 3
else 0.4 if len(combined_cols) == 4 else 0.33 if len(combined_cols) >= 5 else 1
)
)
combined_receptor_df = receptor_df[(receptor_df[combined_cols] != 0).any(axis=1)]
# Calculate overlap for combined ligands
Expand Down Expand Up @@ -2473,7 +2470,11 @@ def map_new_cells(self):
if hasattr(self, "targets_expr"):
targets = self.targets_expr.columns
y_arr = pd.DataFrame(
self.adata[:, targets].X.toarray() if scipy.sparse.issparse(self.adata.X) else self.adata[:, targets].X,
(
self.adata[:, targets].X.toarray()
if scipy.sparse.issparse(self.adata.X)
else self.adata[:, targets].X
),
index=self.sample_names,
columns=targets,
)
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93 changes: 50 additions & 43 deletions spateo/tools/CCI_effects_modeling/MuSIC_downstream.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
- following spatially-aware regression (or a sequence of spatially-aware regressions), overlay the directionality
of the predicted influence of the ligand on downstream expression.
"""

import argparse
import collections
import gc
Expand Down Expand Up @@ -981,9 +982,9 @@ def plot_multiple_interaction_effects_3D(
if len(strong_active_effects) >= 3:
adata.obs.loc[idx, "interaction_categories"] = "Multiple interactions"
elif len(strong_active_effects) == 2:
adata.obs.loc[
idx, "interaction_categories"
] = f"{strong_active_effects[0]} and {strong_active_effects[1]}"
adata.obs.loc[idx, "interaction_categories"] = (
f"{strong_active_effects[0]} and {strong_active_effects[1]}"
)
elif len(active_effects) == 1:
adata.obs.loc[idx, "interaction_categories"] = active_effects[0]
else:
Expand Down Expand Up @@ -1324,24 +1325,24 @@ def visualize_overlap_between_interacting_components_3D(
overlap = target_expressing.intersection(interaction_expressing)

adata.obs[f"{interaction}_{target}"] = "Other"
adata.obs.loc[
target_expressing, f"{interaction}_{target}"
] = f"{target} only (no {interaction} in neighborhood and/or receptor)"
adata.obs.loc[target_expressing, f"{interaction}_{target}"] = (
f"{target} only (no {interaction} in neighborhood and/or receptor)"
)
if self.mod_type == "lr":
ligand, receptor = interaction.split(":")
adata.obs.loc[
interaction_expressing, f"{interaction}_{target}"
] = f"{ligand.title()} in Neighborhood and {receptor}, no {target}"
adata.obs.loc[
overlap, f"{interaction}_{target}"
] = f"{ligand.title()} in Neighborhood, {receptor} and {target}"
adata.obs.loc[interaction_expressing, f"{interaction}_{target}"] = (
f"{ligand.title()} in Neighborhood and {receptor}, no {target}"
)
adata.obs.loc[overlap, f"{interaction}_{target}"] = (
f"{ligand.title()} in Neighborhood, {receptor} and {target}"
)
elif self.mod_type == "ligand":
adata.obs.loc[
interaction_expressing, f"{interaction}_{target}"
] = f"{interaction.title()} in Neighborhood and Receptor, no {target}"
adata.obs.loc[
overlap, f"{interaction}_{target}"
] = f"{interaction.title()} in Neighborhood, Receptor and {target}"
adata.obs.loc[interaction_expressing, f"{interaction}_{target}"] = (
f"{interaction.title()} in Neighborhood and Receptor, no {target}"
)
adata.obs.loc[overlap, f"{interaction}_{target}"] = (
f"{interaction.title()} in Neighborhood, Receptor and {target}"
)

color_mapping = dict(zip(adata.obs[f"{interaction}_{target}"].value_counts().index, godsnot_102))
color_mapping["Other"] = "#D3D3D3"
Expand Down Expand Up @@ -3536,9 +3537,9 @@ def visualize_neighborhood(
adata.obs.loc[
target_expressing_selected, f"{interaction}_{target}_{select_examples_criterion}_example_points"
] = target_expression
adata.obs.loc[
neighbors_selected, f"{interaction}_{target}_{select_examples_criterion}_example_points"
] = ligand_expression
adata.obs.loc[neighbors_selected, f"{interaction}_{target}_{select_examples_criterion}_example_points"] = (
ligand_expression
)

if display_plot:
# plotly to create 3D scatter plot:
Expand Down Expand Up @@ -5568,13 +5569,13 @@ def get_effect_potential(
if store_summed_potential:
if self.mod_type == "niche":
if receiver_cell_type is None:
self.adata.obs[
f"norm_sum_sent_effect_potential_{sender_cell_type}_for_{target}"
] = normalized_effect_potential_sum_sender
self.adata.obs[f"norm_sum_sent_effect_potential_{sender_cell_type}_for_{target}"] = (
normalized_effect_potential_sum_sender
)

self.adata.obs[
f"norm_sum_received_effect_potential_from_{sender_cell_type}_for_{target}"
] = normalized_effect_potential_sum_receiver
self.adata.obs[f"norm_sum_received_effect_potential_from_{sender_cell_type}_for_{target}"] = (
normalized_effect_potential_sum_receiver
)
else:
self.adata.obs[
f"norm_sum_sent_{sender_cell_type}_effect_potential_to_{receiver_cell_type}_for_{target}"
Expand All @@ -5589,26 +5590,26 @@ def get_effect_potential(
ligand = replace_col_with_collagens(ligand)
ligand = replace_hla_with_hlas(ligand)

self.adata.obs[
f"norm_sum_sent_effect_potential_{ligand}_for_{target}"
] = normalized_effect_potential_sum_sender
self.adata.obs[f"norm_sum_sent_effect_potential_{ligand}_for_{target}"] = (
normalized_effect_potential_sum_sender
)

self.adata.obs[
f"norm_sum_received_effect_potential_from_{ligand}_for_{target}"
] = normalized_effect_potential_sum_receiver
self.adata.obs[f"norm_sum_received_effect_potential_from_{ligand}_for_{target}"] = (
normalized_effect_potential_sum_receiver
)

elif self.mod_type == "lr":
if "/" in ligand:
ligand = replace_col_with_collagens(ligand)
ligand = replace_hla_with_hlas(ligand)

self.adata.obs[
f"norm_sum_sent_effect_potential_{ligand}_for_{target}_via_{receptor}"
] = normalized_effect_potential_sum_sender
self.adata.obs[f"norm_sum_sent_effect_potential_{ligand}_for_{target}_via_{receptor}"] = (
normalized_effect_potential_sum_sender
)

self.adata.obs[
f"norm_sum_received_effect_potential_from_{ligand}_for_{target}_via_{receptor}"
] = normalized_effect_potential_sum_receiver
self.adata.obs[f"norm_sum_received_effect_potential_from_{ligand}_for_{target}_via_{receptor}"] = (
normalized_effect_potential_sum_receiver
)

self.adata.obs["effect_sign"] = effect_sign.reshape(-1, 1)

Expand Down Expand Up @@ -6846,7 +6847,11 @@ def CCI_deg_detection_setup(
targets = [t for t in targets if t in self.adata.var_names]
targets = list(set(targets))
targets_expr = pd.DataFrame(
self.adata[:, targets].X.toarray() if scipy.sparse.issparse(self.adata.X) else self.adata[:, targets].X,
(
self.adata[:, targets].X.toarray()
if scipy.sparse.issparse(self.adata.X)
else self.adata[:, targets].X
),
index=self.adata.obs_names,
columns=targets,
)
Expand Down Expand Up @@ -6879,9 +6884,11 @@ def CCI_deg_detection_setup(
ct_signaling = ct_signaling.var.index[sig_expr_percentage > self.target_expr_threshold]

sig_expr = pd.DataFrame(
self.adata[:, ct_signaling].X.toarray()
if scipy.sparse.issparse(self.adata.X)
else self.adata[:, ct_signaling].X,
(
self.adata[:, ct_signaling].X.toarray()
if scipy.sparse.issparse(self.adata.X)
else self.adata[:, ct_signaling].X
),
index=self.sample_names,
columns=ct_signaling,
)
Expand Down
4 changes: 3 additions & 1 deletion spateo/tools/cci_two_cluster.py
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Original file line number Diff line number Diff line change
Expand Up @@ -374,7 +374,9 @@ def find_cci_two_group(
per_ligand_data = adata[per_sender_id, lr_network["from"]]
per_receptor_data = adata[per_receiver_id, lr_network["to"]]
per_lr_data = (
per_ligand_data.X.toarray() * per_receptor_data.X.toarray() if x_sparse else per_ligand_data.X * per_receptor_data.X
per_ligand_data.X.toarray() * per_receptor_data.X.toarray()
if x_sparse
else per_ligand_data.X * per_receptor_data.X
)
per_lr_co_exp_ratio = np.apply_along_axis(lambda x: np.sum(x > 0) / x.size, 0, per_lr_data)
if np.isnan(per_lr_co_exp_ratio).all():
Expand Down
4 changes: 3 additions & 1 deletion spateo/tools/cell_communication.py
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Original file line number Diff line number Diff line change
Expand Up @@ -380,7 +380,9 @@ def predict_ligand_activities(
response_expressed_genes = list(set(expressed_genes_receiver) & set(ligand_target_matrix.index))
response_expressed_genes_df = pd.DataFrame(response_expressed_genes)
response_expressed_genes_df = response_expressed_genes_df.rename(columns={0: "gene"})
response_expressed_genes_df["avg_expr"] = np.mean(adata[receiver_cells, response_expressed_genes].X.toarray(), axis=0)
response_expressed_genes_df["avg_expr"] = np.mean(
adata[receiver_cells, response_expressed_genes].X.toarray(), axis=0
)
lt_matrix = ligand_target_matrix[potential_ligands.tolist()].loc[response_expressed_genes_df["gene"].tolist()]
de = []
for ligand in lt_matrix:
Expand Down

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