`module` `preprocessing`

`function` `run_scvi`

run_scvi(
    adata,
    outdir='data/interim/res_scvi.csv',
    highly_variable_genes=False,
    plot_extra=[]
)

Runs scVI on the input AnnData object and returns a DataFrame with the cell embeddings and source labels.

Parameters: adata (anndata.AnnData): Input AnnData object with gene expression data. outdir (str): Output directory to save the resulting DataFrame. highly_variable_genes (bool): Flag indicating whether to identify highly variable genes. plot_extra (list): List of additional variables to include in the visualization.

Returns: pandas.DataFrame: DataFrame with cell embeddings and source labels.

`function` `record_edges`

record_edges(emb_rna, emb_spatial, n_neigb, edge_type, metric='minkowski')

Create edges between nodes based on nearest neighbors.

Parameters: emb_rna (pd.DataFrame): DataFrame containing RNA embeddings. emb_spatial (pd.DataFrame): DataFrame containing spatial embeddings. n_neigb (int): Number of nearest neighbors to consider. edge_type (str): Type of edge to create. Must be either ‘sc2xen’, ‘sc2sc’, or ‘sc2vis’. metric (str, optional): Distance metric to use. Defaults to ‘minkowski’.

Returns: pd.DataFrame: DataFrame containing the edges with columns ‘node1’, ‘node2’, ‘weight’, and ‘type’.

`function` `show_weights_distribution`

show_weights_distribution(
    edges,
    spatial,
    spatial_type='visium',
    library_id=None
)

Display the distribution of weights for each node in a spatial dataset.

Parameters: edges (pandas.DataFrame): DataFrame containing the edges information. spatial (anndata.AnnData): Annotated data matrix containing the spatial dataset. spatial_type (str, optional): Type of spatial dataset. Defaults to “visium”. library_id (str, optional): ID of the library. Defaults to None.

`function` `create_edges_for_visium_nodes`

create_edges_for_visium_nodes(visium)

Create edges between Visium nodes based on their spatial proximity.

Args:

visium (DataFrame): DataFrame containing Visium data.

Returns:

DataFrame: DataFrame containing the edges between Visium nodes. The DataFrame has columns “node1”, “node2”, “weight”, and “type”.

`function` `create_edges_for_xenium_nodes_global`

create_edges_for_xenium_nodes_global(xenium, percentile=1, sample_size=1000)

Create edges between xenium nodes based on their centroids.

Parameters:

xenium (object): The xenium object containing the node data.
percentile (float): The percentile value used to determine the distance threshold.
sample_size (int): The size of the sample used to estimate the distance threshold.

Returns:

edges_xen_df (DataFrame): A DataFrame containing the edges between xenium nodes, along with their weights, distance threshold, and type.

`function` `save_edges_and_embeddings`

save_edges_and_embeddings(
    edges,
    emb_spatial,
    emb_rna,
    outdir='data/interim/',
    suffix=''
)

Save the edges, spatial embeddings, and RNA embeddings to CSV files.

Parameters: edges (DataFrame): DataFrame containing the edges. emb_spatial (DataFrame): DataFrame containing the spatial embeddings. emb_rna (DataFrame): DataFrame containing the RNA embeddings. outdir (str): Directory to save the CSV files. Default is “data/interim/”. suffix (str): Suffix to add to the CSV file names. Default is an empty string.

This file was automatically generated via lazydocs.

module preprocessing

function run_scvi

function record_edges

function show_weights_distribution

function create_edges_for_visium_nodes

function create_edges_for_xenium_nodes_global

function save_edges_and_embeddings