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‎docs/.ipynb_checkpoints/index-checkpoint.md‎

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@@ -11,6 +11,7 @@ Tools <api/tools.md>
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Plotting <api/plotting.md>
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```
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```{toctree}
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:maxdepth: 1
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:caption: Tutorials
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contributing.md
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references.md
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```
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```

‎docs/api/.ipynb_checkpoints/plotting-checkpoint.md‎

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.. autosummary::
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:toctree: generated/basic_plots
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crosstab
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histogram
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pie
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```{eval-rst}
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.. autosummary::
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:toctree: generated/quantification
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gene_metric_heatmap
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logfoldratio_over_noise
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metric_scatter
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moranI_histogram
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diffusion_results
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spatial_inout_expression
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```
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```
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## Source, target and communication
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```{eval-rst}
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.. autosummary::
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:toctree: generated/communication
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celltype_communication
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gene_communication
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global_distribution_from_source
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target_score_by_celltype
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interactions_with_arrows
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spatial_interactions
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```
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## Factor analysis
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```{eval-rst}
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.. autosummary::
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:toctree: generated/factor_analysis
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factors_in_cells
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rank_factor_genes_loadings
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rank_factor_genes_loadings_matrixplot
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nmf_factors_exrna_cells_W
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nmf_gene_contributions
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paired_nmf_factors
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apply_exrnaH_to_cellular_to_create_cellularW
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```
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```
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## Colormaps & palettes
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get_colormap
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get_palette
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```
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‎docs/api/.ipynb_checkpoints/preprocessing-checkpoint.md‎

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pp.define_urna
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pp.filter_urna
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pp.format_adata
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```

‎docs/api/.ipynb_checkpoints/tools-checkpoint.md‎

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tl.calculate_target_cells
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tl.compute_target_score
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tl.define_target_by_celltype
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tl.cluster_distribution_from_source
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tl.cluster_distribution_from_source
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tl.get_gene_interaction_strength
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tl.communication_strength
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tl.gene_specific_interactions
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```{eval-rst}
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.. autosummary::
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:toctree: generated/cell_scores
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tl.compute_contribution_score
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```
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```{eval-rst}
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.. autosummary::
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:toctree: generated/factors
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tl.factors_to_cells
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tl.latent_factor
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```
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tl.assess_diffusion
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tl.compute_js_divergence
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```
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## Multimodal quantication
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```{eval-rst}
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.. autosummary::
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:toctree: generated/multimodal
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tl.image_intensities_per_transcript
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```
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‎docs/api/plotting.md‎

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.. autosummary::
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:toctree: generated/basic_plots
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crosstab
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histogram
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pie
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```{eval-rst}
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.. autosummary::
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:toctree: generated/quantification
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gene_metric_heatmap
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logfoldratio_over_noise
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metric_scatter
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moranI_histogram
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diffusion_results
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spatial_inout_expression
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```
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```
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## Source, target and communication
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```{eval-rst}
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.. autosummary::
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:toctree: generated/communication
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celltype_communication
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gene_communication
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global_distribution_from_source
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target_score_by_celltype
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interactions_with_arrows
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spatial_interactions
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```
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## Factor analysis
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```{eval-rst}
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.. autosummary::
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:toctree: generated/factor_analysis
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factors_in_cells
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rank_factor_genes_loadings
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rank_factor_genes_loadings_matrixplot
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nmf_factors_exrna_cells_W
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nmf_gene_contributions
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paired_nmf_factors
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apply_exrnaH_to_cellular_to_create_cellularW
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```
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```
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## Colormaps & palettes
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get_colormap
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get_palette
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```
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‎docs/api/preprocessing.md‎

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pp.define_urna
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pp.filter_urna
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pp.format_adata
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```

‎docs/api/tools.md‎

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tl.calculate_target_cells
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tl.compute_target_score
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tl.define_target_by_celltype
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tl.cluster_distribution_from_source
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tl.cluster_distribution_from_source
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tl.get_gene_interaction_strength
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tl.communication_strength
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tl.gene_specific_interactions
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```{eval-rst}
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.. autosummary::
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:toctree: generated/cell_scores
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tl.compute_contribution_score
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```
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```{eval-rst}
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.. autosummary::
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:toctree: generated/factors
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tl.factors_to_cells
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tl.latent_factor
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```
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tl.assess_diffusion
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tl.compute_js_divergence
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```
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## Multimodal quantication
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```{eval-rst}
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.. autosummary::
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:toctree: generated/multimodal
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tl.image_intensities_per_transcript
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```
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‎docs/index.md‎

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Plotting <api/plotting.md>
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```
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```{toctree}
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:maxdepth: 1
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:caption: Tutorials
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contributing.md
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references.md
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```
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```

‎src/troutpy/tl/.ipynb_checkpoints/quantify_urna-checkpoint.py‎

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-------
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sdata: spatialdata.SpatialData
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Sdata containing Moran's I values for each gene, indexed by gene names.
54-
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"""
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if coord_keys is None:
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coord_keys = ["x", "y"]

‎src/troutpy/tl/factor_analysis.py‎

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1+
import warnings
2+
3+
import anndata as ad
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import numpy as np
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import pandas as pd
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from sklearn.decomposition import NMF, LatentDirichletAllocation
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from spatialdata import SpatialData
58

69

7-
import numpy as np
8-
import scanpy as sc
9-
import anndata as ad
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from sklearn.decomposition import NMF, LatentDirichletAllocation
11-
import warnings
12-
1310
def latent_factor(
1411
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1512
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5552
sdata : SpatialData or None
5653
Modified SpatialData object or None if copy=False.
5754
"""
58-
5955
adata = sdata[layer]
6056
counts = adata.X.copy()
6157

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7470
try:
7571
from drvi.model import DRVI
7672
from drvi.utils.tools import (
77-
traverse_latent,
7873
calculate_differential_vars,
7974
set_latent_dimension_stats,
75+
traverse_latent,
8076
)
8177
except ImportError:
82-
raise ImportError(
83-
"The 'drvi' package is required for method='DRVI'. "
84-
"Please install it with: pip install drvi"
85-
)
78+
raise ImportError("The 'drvi' package is required for method='DRVI'. Please install it with: pip install drvi")
8679

8780
# DRVI-specific parameters
8881
encoder_dims = kwargs.pop("encoder_dims", [128, 128])
@@ -145,6 +138,7 @@ def latent_factor(
145138
sdata[layer] = adata
146139
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147140

141+
148142
def combine_loadings_arrays(gene_loadings_pos: np.ndarray, gene_loadings_neg: np.ndarray) -> np.ndarray:
149143
if gene_loadings_pos.shape != gene_loadings_neg.shape:
150144
raise ValueError("Input arrays must have the same shape.")
@@ -157,8 +151,7 @@ def combine_loadings_arrays(gene_loadings_pos: np.ndarray, gene_loadings_neg: np
157151
# Raise warning if conflicts found
158152
if np.any(conflict_mask):
159153
conflict_indices = np.argwhere(conflict_mask)
160-
warnings.warn(f"Conflicts found at {len(conflict_indices)} locations. "
161-
f"Example: {conflict_indices[:5].tolist()}")
154+
warnings.warn(f"Conflicts found at {len(conflict_indices)} locations. Example: {conflict_indices[:5].tolist()}")
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163156
# Initialize combined output
164157
combined = np.zeros_like(gene_loadings_pos)
@@ -168,7 +161,6 @@ def combine_loadings_arrays(gene_loadings_pos: np.ndarray, gene_loadings_neg: np
168161
return combined
169162

170163

171-
172164
def factors_to_cells(
173165
sdata: SpatialData, extracellular_layer: str = "segmentation_free_table", cellular_layer: str = "table", copy: bool | None = None
174166
) -> SpatialData:

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