errors_correction2

notebooks/spatialdata_tutorials/4_explore_comunication.ipynb

@@ -273,15 +273,13 @@
     "    - sdata: AnnData object\n",
     "    A spatial data object containing the 'table' and 'xrna_metadata' components.\n",
     "    - proportion_threshold: float, optional (default=0.2)\n",
-    "    The threshold for filtering significant gene proportions. Genes with proportions \n",
-    "    above this value are considered significant.\n",
+    "    The threshold for filtering significant gene proportions. Genes with proportions above this value are considered significant.\n",
     "    - cell_type_key: str, optional (default='cell type')\n",
     "    The key in `adata.obs` that contains the cell type annotations.\n",
     "\n",
     "    Returns:\n",
     "    - number_interactions_df: pandas.DataFrame\n",
-    "    A DataFrame where rows represent source cell types, columns represent target cell \n",
-    "    types, and values indicate the number of significant genes shared between them.\n",
+    "    A DataFrame where rows represent source cell types, columns represent target cell types, and values indicate the number of significant genes shared between them.\n",
     "    \"\"\"\n",
     "    adata=sdata['table']\n",
     "    source_proportions=pd.DataFrame(sdata['xrna_metadata'].varm['source'],index=sdata['xrna_metadata'].var.index,columns=adata.obs[cell_type_key].unique().dropna())\n",

src/troutpy/pl/__init__.py

@@ -1,4 +1,4 @@
 from .plotting import sorted_heatmap,coupled_scatter,heatmap,plot_crosstab,pie_of_positive,genes_over_noise
 from .plotting import moranI_histogram,proportion_above_threshold,nmf_factors_exrna_cells_W
-from .plotting import apply_exrnaH_to_cellular_to_create_cellularW,W
-from .plotting import *
+from .plotting import nmf_gene_contributions,apply_exrnaH_to_cellular_to_create_cellularW,paired_nmf_factors
+from .plotting import W,spatial_interactions,interactions_with_arrows

src/troutpy/pl/plotting.py

@@ -267,8 +267,7 @@ def plot_crosstab(data, xvar: str = '', yvar: str = '', normalize=True, axis=1,
 
 def pie_of_positive(data, groupby: str = '', figures_path: str = '', save: bool = True):
     """
-    Generates a pie chart showing the proportion of positive and negative values 
-    for a specified categorical variable in the data.
+    Generates a pie chart showing the proportion of positive and negative values for a specified categorical variable in the data.
 
     Parameters
     ----------
@@ -284,8 +283,7 @@ def pie_of_positive(data, groupby: str = '', figures_path: str = '', save: bool
     Returns
     -------
     None
-        The function generates and either saves or displays a pie chart, 
-        depending on the value of the `save` parameter.
+        The function generates and either saves or displays a pie chart, depending on the value of the `save` parameter.
     """
 
     plt.figure()
@@ -300,8 +298,7 @@ def pie_of_positive(data, groupby: str = '', figures_path: str = '', save: bool
         plt.savefig(os.path.join(figures_path, plot_filename))
 
 def genes_over_noise(sdata, scores_by_genes,layer='extracellular_transcripts', output_path:str='',save=True,format:str='pdf'):
-    """
-    This function plots log fold change per gene over noise using a boxplot.
+    """This function plots log fold change per gene over noise using a boxplot.
     
     Parameters:
     - data_quantified: DataFrame containing the extracellular transcript data, including feature names and codeword categories.
@@ -333,8 +330,7 @@ def genes_over_noise(sdata, scores_by_genes,layer='extracellular_transcripts', o
     plt.show()
 
 def moranI_histogram(svg_df, save=True, figures_path: str = '', bins: int = 200, format: str = 'pdf'):
-    """
-    Plots the distribution of Moran's I scores from a DataFrame.
+    """Plots the distribution of Moran's I scores from a DataFrame.
 
     Parameters:
     -----------

src/troutpy/pp/__init__.py

@@ -1,2 +1,2 @@
-from .compute import *
-from .format import *
+from .compute import compute_extracellular_counts,define_extracellular,compute_crosstab
+from .format import format_adata

src/troutpy/pp/compute.py

@@ -110,10 +110,22 @@ def define_extracellular(
 
     return sdata if copy else None
 
-#
+def compute_crosstab(data, xvar: str = '', yvar: str = ''):
+    """Compute a crosstabulation (contingency table) of two categorical variables from the given DataFrame.
 
+    Parameters:
+    -----------
+    data : pandas.DataFrame
+        The input DataFrame containing the data to be analyzed.
+    xvar : str, optional
+        The name of the column to use as the rows of the crosstab. Default is an empty string.
+    yvar : str, optional
+        The name of the column to use as the columns of the crosstab. Default is an empty string.
 
-def compute_crosstab(data,xvar:str='',yvar:str=''):
-    """Compute crosstabs for each given variable"""
-    crosstab_data= pd.crosstab(data[xvar], data[yvar])
-    return crosstab_data
+    Returns:
+    --------
+    pandas.DataFrame
+        A DataFrame representing the crosstab of the specified variables, with counts of occurrences for each combination of categories.
+    """
+    crosstab_data = pd.crosstab(data[xvar], data[yvar])
+    return crosstab_data

src/troutpy/pp/format.py

@@ -10,8 +10,7 @@
 
 def format_adata(input_path, outpath_dummy, xlimits, ylimits):
     """
-    Processes and formats AnnData and transcripts by loading data, merging cell information,
-    applying spatial filters, and saving the processed data to a dummy output directory.
+    Processes and formats AnnData and transcripts by loading data, merging cell information,applying spatial filters, and saving the processed data to a dummy output directory.
 
     Args:
         input_path (str): Path to the input directory containing:

src/troutpy/tl/__init__.py

@@ -1,7 +1,7 @@
-from .source_cell import *
-from .target_cell import *
-from .estimate_density import *
-from .quantify_xrna import *
-from .interactions import *
-from .NMF import *
-from .segmentation_free import *
+from .source_cell import create_xrna_metadata,compute_source_cells,distance_to_source_cell,compute_distant_cells_prop,get_proportion_expressed_per_cell_type
+from .target_cell import calculate_target_cells,define_target_by_celltype
+from .estimate_density import colocalization_proportion
+from .quantify_xrna import spatial_variability,create_xrna_metadata,quantify_overexpression,extracellular_enrichment,spatial_colocalization
+from .interactions import get_number_of_communication_genes,get_gene_interaction_strength
+from .NMF import apply_nmf_to_adata,nmf,apply_exrna_factors_to_cells
+from .segmentation_free import segmentation_free_clustering

src/troutpy/tl/interactions.py

@@ -15,6 +15,7 @@ def get_number_of_communication_genes(
     target_proportion_threshold: float = 0.2
     ) -> pd.DataFrame:
     """Compute the number of exchanged genes between any two cell types
+    
     Args:
         source_proportions (pd.DataFrame): A data frame (Gene name x Cell Type) with 
         proportion of cells per cell type expressing corresponding gene 

src/troutpy/tl/quantify_xrna.py

@@ -303,7 +303,6 @@ def extracellular_enrichment(sdata, gene_id_column: str = 'feature_name', copy:
 
     return sdata if copy else None
 
-
 def spatial_colocalization(
     sdata, 
     coords_keys=['x', 'y'], 

src/troutpy/tl/segmentation_free.py

@@ -4,7 +4,6 @@
 import pandas as pd
 
 
-
 def segmentation_free_clustering(
     sdata, 
     params: dict = {}, 

src/troutpy/tl/target_cell.py

@@ -107,9 +107,6 @@ def calculate_target_cells(
     # Return a copy of the modified SpatialData object if requested
     return sdata.copy() if copy else None
 
-
-
-
 def define_target_by_celltype(sdata, layer='transcripts', closest_celltype_key='closest_target_cell_type', feature_key='feature_name'):
     """
     Computes the proportion of features (e.g., transcripts) associated with each cell type in the spatial dataset.