segger.data¶

The segger.data module in Segger is designed to facilitate data preparation for spatial transcriptomics datasets, focusing on Xenium and Merscope. It provides a unified, scalable interface for handling large datasets, performing boundary and transcript filtering, and preparing data for graph-based deep learning models.

Submodules¶

Constants: Contains predefined constants for spatial data processing.
IO: Provides utilities for input/output operations, supporting various data formats.
Utils: Miscellaneous utility functions for processing and analysis.

Key Features¶

Lazy Loading: Efficiently handles large datasets using Dask, avoiding memory bottlenecks.
Flexible Tiling: Spatially segments datasets into tiles, optimizing for parallel processing.
Graph Construction: Converts spatial data into PyTorch Geometric (PyG) graph formats for GNN-based models.
Boundary Handling: Processes spatial polygons and computes transcript overlaps.
Dataset-agnostic API: Unified API for multiple spatial transcriptomics technologies, such as Xenium and Merscope.

API Documentation¶

Data module for Segger.

Contains utilities for handling and processing spatial transcriptomics data.

MerscopeSample ¶

MerscopeSample(transcripts_df=None, transcripts_radius=10, boundaries_graph=False)

Bases: SpatialTranscriptomicsSample

Source code in src/segger/data/io.py

def __init__(self, transcripts_df: dd.DataFrame = None, transcripts_radius: int = 10, boundaries_graph: bool = False):
    super().__init__(transcripts_df, transcripts_radius, boundaries_graph, MerscopeKeys)

filter_transcripts ¶

filter_transcripts(transcripts_df, min_qv=20.0)

Filters transcripts based on specific criteria for Merscope using Dask.

Parameters:

Name	Type	Description	Default
`transcripts_df`		dd.DataFrame The Dask DataFrame containing transcript data.	required
`min_qv`		float, optional The minimum quality value threshold for filtering transcripts.	`20.0`

Returns:

Type	Description
`DataFrame`	dd.DataFrame The filtered Dask DataFrame.

Source code in src/segger/data/io.py

def filter_transcripts(self, transcripts_df: dd.DataFrame, min_qv: float = 20.0) -> dd.DataFrame:
    """
    Filters transcripts based on specific criteria for Merscope using Dask.

    Parameters:
        transcripts_df : dd.DataFrame
            The Dask DataFrame containing transcript data.
        min_qv : float, optional
            The minimum quality value threshold for filtering transcripts.

    Returns:
        dd.DataFrame
            The filtered Dask DataFrame.
    """
    # Add custom Merscope-specific filtering logic if needed
    # For now, apply only the quality value filter
    return transcripts_df[transcripts_df[self.keys.QUALITY_VALUE.value] >= min_qv]

SpatialTranscriptomicsDataset ¶

SpatialTranscriptomicsDataset(root, transform=None, pre_transform=None, pre_filter=None)

Bases: InMemoryDataset

A dataset class for handling SpatialTranscriptomics spatial transcriptomics data.

Attributes:

Name	Type	Description
`root`	`str`	The root directory where the dataset is stored.
`transform`	`callable`	A function/transform that takes in a Data object and returns a transformed version.
`pre_transform`	`callable`	A function/transform that takes in a Data object and returns a transformed version.
`pre_filter`	`callable`	A function that takes in a Data object and returns a boolean indicating whether to keep it.

Initialize the SpatialTranscriptomicsDataset.

Parameters:

Name	Type	Description	Default
`root`	`str`	Root directory where the dataset is stored.	required
`transform`	`callable`	A function/transform that takes in a Data object and returns a transformed version. Defaults to None.	`None`
`pre_transform`	`callable`	A function/transform that takes in a Data object and returns a transformed version. Defaults to None.	`None`
`pre_filter`	`callable`	A function that takes in a Data object and returns a boolean indicating whether to keep it. Defaults to None.	`None`

Source code in src/segger/data/utils.py

def __init__(self, root: str, transform: Callable = None, pre_transform: Callable = None, pre_filter: Callable = None):
    """Initialize the SpatialTranscriptomicsDataset.

    Args:
        root (str): Root directory where the dataset is stored.
        transform (callable, optional): A function/transform that takes in a Data object and returns a transformed version. Defaults to None.
        pre_transform (callable, optional): A function/transform that takes in a Data object and returns a transformed version. Defaults to None.
        pre_filter (callable, optional): A function that takes in a Data object and returns a boolean indicating whether to keep it. Defaults to None.
    """
    super().__init__(root, transform, pre_transform, pre_filter)

processed_file_names `property` ¶

processed_file_names

Return a list of processed file names in the processed directory.

Returns:

Type	Description
`List[str]`	List[str]: List of processed file names.

raw_file_names `property` ¶

raw_file_names

Return a list of raw file names in the raw directory.

Returns:

Type	Description
`List[str]`	List[str]: List of raw file names.

download ¶

download()

Download the raw data. This method should be overridden if you need to download the data.

Source code in src/segger/data/utils.py

def download(self) -> None:
    """Download the raw data. This method should be overridden if you need to download the data.
    """
    pass

get ¶

get(idx)

Get a processed data object.

Parameters:

Name	Type	Description	Default
`idx`	`int`	Index of the data object to retrieve.	required

Returns:

Name	Type	Description
`Data`	`Data`	The processed data object.

Source code in src/segger/data/utils.py

def get(self, idx: int) -> Data:
    """Get a processed data object.

    Args:
        idx (int): Index of the data object to retrieve.

    Returns:
        Data: The processed data object.
    """
    data = torch.load(os.path.join(self.processed_dir, self.processed_file_names[idx]))
    data['tx'].x = data['tx'].x.to_dense()
    return data

len ¶

len()

Return the number of processed files.

Returns:

Name	Type	Description
`int`	`int`	Number of processed files.

Source code in src/segger/data/utils.py

def len(self) -> int:
    """Return the number of processed files.

    Returns:
        int: Number of processed files.
    """
    return len(self.processed_file_names)

process ¶

process()

Process the raw data and save it to the processed directory. This method should be overridden if you need to process the data.

Source code in src/segger/data/utils.py

def process(self) -> None:
    """Process the raw data and save it to the processed directory. This method should be overridden if you need to process the data.
    """
    pass

SpatialTranscriptomicsSample ¶

SpatialTranscriptomicsSample(transcripts_df=None, transcripts_radius=10, boundaries_graph=False, keys=None, verbose=True)

Bases: ABC

Initialize the SpatialTranscriptomicsSample class.

Parameters:

Name	Type	Description	Default
`transcripts_df`	`DataFrame`	A DataFrame containing transcript data.	`None`
`transcripts_radius`	`int`	Radius for transcripts in the analysis.	`10`
`boundaries_graph`	`bool`	Whether to include boundaries (e.g., nucleus, cell) graph information.	`False`
`keys`	`Dict`	The enum class containing key mappings specific to the dataset.	`None`

Source code in src/segger/data/io.py

def __init__(
    self,
    transcripts_df: pd.DataFrame = None,
    transcripts_radius: int = 10,
    boundaries_graph: bool = False,
    keys: Dict = None,
    verbose: bool = True,
):
    """Initialize the SpatialTranscriptomicsSample class.

    Args:
        transcripts_df (pd.DataFrame, optional): A DataFrame containing transcript data.
        transcripts_radius (int, optional): Radius for transcripts in the analysis.
        boundaries_graph (bool, optional): Whether to include boundaries (e.g., nucleus, cell) graph information.
        keys (Dict, optional): The enum class containing key mappings specific to the dataset.
    """
    self.transcripts_df = transcripts_df
    self.transcripts_radius = transcripts_radius
    self.boundaries_graph = boundaries_graph
    self.keys = keys
    self.embedding_df = None
    self.current_embedding = 'token'
    self.verbose = verbose

build_pyg_data_from_tile ¶

build_pyg_data_from_tile(boundaries_df, transcripts_df, r_tx=5.0, k_tx=3, method='kd_tree', gpu=False, workers=1, scale_boundaries=1.0)

Builds PyG data from a tile of boundaries and transcripts data using Dask utilities for efficient processing.

Parameters:

Name	Type	Description	Default
`boundaries_df`	`DataFrame`	Dask DataFrame containing boundaries data (e.g., nucleus, cell).	required
`transcripts_df`	`DataFrame`	Dask DataFrame containing transcripts data.	required
`r_tx`	`float`	Radius for building the transcript-to-transcript graph.	`5.0`
`k_tx`	`int`	Number of nearest neighbors for the tx-tx graph.	`3`
`method`	`str`	Method for computing edge indices (e.g., 'kd_tree', 'faiss').	`'kd_tree'`
`gpu`	`bool`	Whether to use GPU acceleration for edge index computation.	`False`
`workers`	`int`	Number of workers to use for parallel processing.	`1`
`scale_boundaries`	`float`	The factor by which to scale the boundary polygons. Default is 1.0.	`1.0`

Returns:

Name	Type	Description
`HeteroData`	`HeteroData`	PyG Heterogeneous Data object.

Source code in src/segger/data/io.py

def build_pyg_data_from_tile(
    self, 
    boundaries_df: dd.DataFrame, 
    transcripts_df: dd.DataFrame, 
    r_tx: float = 5.0, 
    k_tx: int = 3, 
    method: str = 'kd_tree', 
    gpu: bool = False, 
    workers: int = 1,
    scale_boundaries: float = 1.0

) -> HeteroData:
    """
    Builds PyG data from a tile of boundaries and transcripts data using Dask utilities for efficient processing.

    Parameters:
        boundaries_df (dd.DataFrame): Dask DataFrame containing boundaries data (e.g., nucleus, cell).
        transcripts_df (dd.DataFrame): Dask DataFrame containing transcripts data.
        r_tx (float): Radius for building the transcript-to-transcript graph.
        k_tx (int): Number of nearest neighbors for the tx-tx graph.
        method (str, optional): Method for computing edge indices (e.g., 'kd_tree', 'faiss').
        gpu (bool, optional): Whether to use GPU acceleration for edge index computation.
        workers (int, optional): Number of workers to use for parallel processing.
        scale_boundaries (float, optional): The factor by which to scale the boundary polygons. Default is 1.0.

    Returns:
        HeteroData: PyG Heterogeneous Data object.
    """
    # Initialize the PyG HeteroData object
    data = HeteroData()

    # Lazily compute boundaries geometries using Dask
    if self.verbose: print("Computing boundaries geometries...")
    bd_gdf = self.compute_boundaries_geometries(boundaries_df, scale_factor=scale_boundaries)
    bd_gdf = bd_gdf[bd_gdf['geometry'].notnull()]

    # Add boundary node data to PyG HeteroData lazily
    data['bd'].id = bd_gdf[self.keys.CELL_ID.value].values
    data['bd'].pos = torch.as_tensor(bd_gdf[['centroid_x', 'centroid_y']].values.astype(float))

    if data['bd'].pos.isnan().any():
        raise ValueError(data['bd'].id[data['bd'].pos.isnan().any(1)])

    bd_x = bd_gdf.iloc[:, 4:]
    data['bd'].x = torch.as_tensor(bd_x.to_numpy(), dtype=torch.float32)


    # Extract the transcript coordinates lazily
    if self.verbose: print("Preparing transcript features and positions...")
    x_xyz = transcripts_df[[self.keys.TRANSCRIPTS_X.value, self.keys.TRANSCRIPTS_Y.value]].to_numpy()
    data['tx'].id = torch.as_tensor(transcripts_df[self.keys.TRANSCRIPTS_ID.value].values.astype(int))
    data['tx'].pos = torch.tensor(x_xyz, dtype=torch.float32)




    # Lazily prepare transcript embeddings (if available)
    if self.verbose: print("Preparing transcript embeddings..")
    token_encoding = self.tx_encoder.transform(transcripts_df[self.keys.FEATURE_NAME.value])
    transcripts_df['token'] = token_encoding  # Store the integer tokens in the 'token' column
    data['tx'].token = torch.as_tensor(token_encoding).int()
    # Handle additional embeddings lazily as well
    if self.embedding_df:
        embeddings = delayed(lambda df: self.embedding_df.loc[
            df[self.keys.FEATURE_NAME.value].values
        ].values)(transcripts_df)
    else:  
        embeddings = token_encoding
    x_features = torch.as_tensor(embeddings).int()
    data['tx'].x = x_features

    # Check if the overlap column exists, if not, compute it lazily using Dask
    if self.keys.OVERLAPS_BOUNDARY.value not in transcripts_df.columns:
        if self.verbose: print(f"Computing overlaps for transcripts...")
        transcripts_df = self.compute_transcript_overlap_with_boundaries(
            transcripts_df, bd_gdf, scale_factor=1.0
        )

    # Connect transcripts with their corresponding boundaries (e.g., nuclei, cells)
    if self.verbose: print("Connecting transcripts with boundaries...")
    overlaps = transcripts_df[self.keys.OVERLAPS_BOUNDARY.value].values
    valid_cell_ids = bd_gdf[self.keys.CELL_ID.value].values
    ind = np.where(
        overlaps & transcripts_df[self.keys.CELL_ID.value].isin(valid_cell_ids)
    )[0]
    tx_bd_edge_index = np.column_stack((
        ind,
        np.searchsorted(
            valid_cell_ids,
            transcripts_df.iloc[ind][self.keys.CELL_ID.value]
        )
    ))

    # Add transcript-boundary edge index to PyG HeteroData
    data['tx', 'belongs', 'bd'].edge_index = torch.as_tensor(tx_bd_edge_index.T, dtype=torch.long)

    # Compute transcript-to-transcript (tx-tx) edges using Dask (lazy computation)
    if self.verbose: print("Computing tx-tx edges...")
    tx_positions = transcripts_df[[self.keys.TRANSCRIPTS_X.value, self.keys.TRANSCRIPTS_Y.value]].values
    delayed_tx_edge_index = delayed(get_edge_index)(
        tx_positions,
        tx_positions,
        k=k_tx,
        dist=r_tx,
        method=method,
        gpu=gpu,
        workers=workers
    )
    tx_edge_index = delayed_tx_edge_index.compute()

    # Add the tx-tx edge index to the PyG HeteroData object
    data['tx', 'neighbors', 'tx'].edge_index = torch.as_tensor(tx_edge_index.T, dtype=torch.long)


    if self.verbose: print("Finished building PyG data for the tile.")
    return data

compute_boundaries_geometries ¶

compute_boundaries_geometries(boundaries_df=None, polygons_gdf=None, scale_factor=1.0, area=True, convexity=True, elongation=True, circularity=True)

Computes geometries for boundaries (e.g., nuclei, cells) from the dataframe using Dask.

Parameters:

Name	Type	Description	Default
`boundaries_df`	`DataFrame`	The dataframe containing boundaries data. Required if polygons_gdf is not provided.	`None`
`polygons_gdf`	`GeoDataFrame`	Precomputed Dask GeoDataFrame containing boundary polygons. If None, will compute from boundaries_df.	`None`
`scale_factor`	`float`	The factor by which to scale the polygons (default is 1.0).	`1.0`
`area`	`bool`	Whether to compute area.	`True`
`convexity`	`bool`	Whether to compute convexity.	`True`
`elongation`	`bool`	Whether to compute elongation.	`True`
`circularity`	`bool`	Whether to compute circularity.	`True`

Returns:

Type	Description
`GeoDataFrame`	dgpd.GeoDataFrame: A GeoDataFrame containing computed geometries.

Source code in src/segger/data/io.py

def compute_boundaries_geometries(
    self,
    boundaries_df: dd.DataFrame = None,
    polygons_gdf: dgpd.GeoDataFrame = None,
    scale_factor: float = 1.0,
    area: bool = True,
    convexity: bool = True,
    elongation: bool = True,
    circularity: bool = True,
) -> dgpd.GeoDataFrame:
    """
    Computes geometries for boundaries (e.g., nuclei, cells) from the dataframe using Dask.

    Parameters:
        boundaries_df (dd.DataFrame, optional): The dataframe containing boundaries data. Required if polygons_gdf is not provided.
        polygons_gdf (dgpd.GeoDataFrame, optional): Precomputed Dask GeoDataFrame containing boundary polygons. If None, will compute from boundaries_df.
        scale_factor (float, optional): The factor by which to scale the polygons (default is 1.0).
        area (bool, optional): Whether to compute area.
        convexity (bool, optional): Whether to compute convexity.
        elongation (bool, optional): Whether to compute elongation.
        circularity (bool, optional): Whether to compute circularity.

    Returns:
        dgpd.GeoDataFrame: A GeoDataFrame containing computed geometries.
    """
    # Check if polygons_gdf is provided, otherwise compute from boundaries_df
    if polygons_gdf is None:
        if boundaries_df is None:
            raise ValueError("Both boundaries_df and polygons_gdf cannot be None. Provide at least one.")

        # Generate polygons from boundaries_df if polygons_gdf is None
        if self.verbose: print(f"No precomputed polygons provided. Computing polygons from boundaries with a scale factor of {scale_factor}.")
        polygons_gdf = self.generate_and_scale_polygons(boundaries_df, scale_factor)

    # Check if the generated polygons_gdf is empty
    if polygons_gdf.shape[0] == 0:
        raise ValueError("No valid polygons were generated from the boundaries.")
    else:
        if self.verbose: print(f"Polygons are available. Proceeding with geometrical computations.")

    # Compute additional geometrical properties
    polygons = polygons_gdf.geometry

    # Compute additional geometrical properties
    if area:
        if self.verbose: print("Computing area...")
        polygons_gdf['area'] = polygons.area
    if convexity:
        if self.verbose: print("Computing convexity...")
        polygons_gdf['convexity'] = polygons.convex_hull.area / polygons.area
    if elongation:
        if self.verbose: print("Computing elongation...")
        r = polygons.minimum_rotated_rectangle()
        polygons_gdf['elongation'] = (r.length * r.length) / r.area
    if circularity:
        if self.verbose: print("Computing circularity...")
        r = polygons_gdf.minimum_bounding_radius()
        polygons_gdf['circularity'] = polygons.area / (r * r)

    if self.verbose: print("Geometrical computations completed.")

    return polygons_gdf.reset_index(drop=True)

compute_transcript_overlap_with_boundaries ¶

compute_transcript_overlap_with_boundaries(transcripts_df, boundaries_df=None, polygons_gdf=None, scale_factor=1.0)

Computes the overlap of transcript locations with scaled boundary polygons and assigns corresponding cell IDs to the transcripts using Dask.

Parameters:

Name	Type	Description	Default
`transcripts_df`	`DataFrame`	Dask DataFrame containing transcript data.	required
`boundaries_df`	`DataFrame`	Dask DataFrame containing boundary data. Required if polygons_gdf is not provided.	`None`
`polygons_gdf`	`GeoDataFrame`	Precomputed Dask GeoDataFrame containing boundary polygons. If None, will compute from boundaries_df.	`None`
`scale_factor`	`float`	The factor by which to scale the boundary polygons. Default is 1.0.	`1.0`

Returns:

Type	Description
`DataFrame`	dd.DataFrame: The updated DataFrame with overlap information and assigned cell IDs.

Source code in src/segger/data/io.py

def compute_transcript_overlap_with_boundaries(
    self,
    transcripts_df: dd.DataFrame,
    boundaries_df: dd.DataFrame = None,
    polygons_gdf: dgpd.GeoDataFrame = None,
    scale_factor: float = 1.0
    ) -> dd.DataFrame:
    """
    Computes the overlap of transcript locations with scaled boundary polygons
    and assigns corresponding cell IDs to the transcripts using Dask.

    Parameters:
        transcripts_df (dd.DataFrame): Dask DataFrame containing transcript data.
        boundaries_df (dd.DataFrame, optional): Dask DataFrame containing boundary data. Required if polygons_gdf is not provided.
        polygons_gdf (dgpd.GeoDataFrame, optional): Precomputed Dask GeoDataFrame containing boundary polygons. If None, will compute from boundaries_df.
        scale_factor (float, optional): The factor by which to scale the boundary polygons. Default is 1.0.

    Returns:
        dd.DataFrame: The updated DataFrame with overlap information and assigned cell IDs.
    """
    # Check if polygons_gdf is provided, otherwise compute from boundaries_df
    if polygons_gdf is None:
        if boundaries_df is None:
            raise ValueError("Both boundaries_df and polygons_gdf cannot be None. Provide at least one.")

        # Generate polygons from boundaries_df if polygons_gdf is None
        # if self.verbose: print(f"No precomputed polygons provided. Computing polygons from boundaries with a scale factor of {scale_factor}.")
        polygons_gdf = self.generate_and_scale_polygons(boundaries_df, scale_factor)

    if polygons_gdf.empty():
        raise ValueError("No valid polygons were generated from the boundaries.")
    else:
        if self.verbose: print(f"Polygons are available. Proceeding with overlap computation.")

    # Create a delayed function to check if a point is within any polygon
    def check_overlap(transcript, polygons_gdf):
        x = transcript[self.keys.TRANSCRIPTS_X.value]
        y = transcript[self.keys.TRANSCRIPTS_Y.value]
        point = Point(x, y)

        overlap = False
        cell_id = None

        # Check for point containment lazily within polygons
        for _, polygon in polygons_gdf.iterrows():
            if polygon.geometry.contains(point):
                overlap = True
                cell_id = polygon[self.keys.CELL_ID.value]
                break

        return overlap, cell_id

    # Apply the check_overlap function in parallel to each row using Dask's map_partitions
    if self.verbose: print(f"Starting overlap computation for transcripts with the boundary polygons.")
    transcripts_df = transcripts_df.map_partitions(
        lambda df: df.assign(
            **{
                self.keys.OVERLAPS_BOUNDARY.value: df.apply(lambda row: delayed(check_overlap)(row, polygons_gdf)[0], axis=1),
                self.keys.CELL_ID.value: df.apply(lambda row: delayed(check_overlap)(row, polygons_gdf)[1], axis=1),
            }
        )
    )

    return transcripts_df

create_scaled_polygon `staticmethod` ¶

create_scaled_polygon(group, scale_factor, keys)

Static method to create and scale a polygon from boundary vertices and return a GeoDataFrame.

Parameters:

Name	Type	Description	Default
`group`	`DataFrame`	Group of boundary coordinates (for a specific cell).	required
`scale_factor`	`float`	The factor by which to scale the polygons.	required
`keys`	`Dict or Enum`	A collection of keys to access column names for 'cell_id', 'vertex_x', and 'vertex_y'.	required

Returns:

Type	Description
`GeoDataFrame`	gpd.GeoDataFrame: A GeoDataFrame containing the scaled Polygon and cell_id.

Source code in src/segger/data/io.py

@staticmethod
def create_scaled_polygon(group: pd.DataFrame, scale_factor: float, keys) -> gpd.GeoDataFrame:
    """
    Static method to create and scale a polygon from boundary vertices and return a GeoDataFrame.

    Parameters:
        group (pd.DataFrame): Group of boundary coordinates (for a specific cell).
        scale_factor (float): The factor by which to scale the polygons.
        keys (Dict or Enum): A collection of keys to access column names for 'cell_id', 'vertex_x', and 'vertex_y'.

    Returns:
        gpd.GeoDataFrame: A GeoDataFrame containing the scaled Polygon and cell_id.
    """
    # Extract coordinates and cell ID from the group using keys
    x_coords = group[keys['vertex_x']]
    y_coords = group[keys['vertex_y']]
    cell_id = group[keys['cell_id']].iloc[0]

    # Ensure there are at least 3 points to form a polygon
    if len(x_coords) >= 3:

        polygon = Polygon(zip(x_coords, y_coords))
        if polygon.is_valid and not polygon.is_empty:
            # Scale the polygon by the provided factor
            scaled_polygon = polygon.buffer(scale_factor)
            if scaled_polygon.is_valid and not scaled_polygon.is_empty:
                return gpd.GeoDataFrame({
                    'geometry': [scaled_polygon], 
                    keys['cell_id']: [cell_id]
                }, geometry='geometry', crs="EPSG:4326")
    # Return an empty GeoDataFrame if no valid polygon is created
    return gpd.GeoDataFrame({
        'geometry': [None], 
        keys['cell_id']: [cell_id]
    }, geometry='geometry', crs="EPSG:4326")

filter_transcripts `abstractmethod` ¶

filter_transcripts(transcripts_df, min_qv=20.0)

Abstract method to filter transcripts based on dataset-specific criteria.

Parameters:

Name	Type	Description	Default
`transcripts_df`	`DataFrame`	The dataframe containing transcript data.	required
`min_qv`	`float`	The minimum quality value threshold for filtering transcripts.	`20.0`

Returns:

Type	Description
`DataFrame`	pd.DataFrame: The filtered dataframe.

Source code in src/segger/data/io.py

@abstractmethod
def filter_transcripts(self, transcripts_df: pd.DataFrame, min_qv: float = 20.0) -> pd.DataFrame:
    """
    Abstract method to filter transcripts based on dataset-specific criteria.

    Parameters:
        transcripts_df (pd.DataFrame): The dataframe containing transcript data.
        min_qv (float, optional): The minimum quality value threshold for filtering transcripts.

    Returns:
        pd.DataFrame: The filtered dataframe.
    """
    pass

generate_and_scale_polygons ¶

generate_and_scale_polygons(boundaries_df, scale_factor=1.0)

Generate and scale polygons from boundary coordinates using Dask. Keeps class structure intact by using static method for the core polygon generation.

Parameters:

Name	Type	Description	Default
`boundaries_df`	`DataFrame`	DataFrame containing boundary coordinates.	required
`scale_factor`	`float`	The factor by which to scale the polygons (default is 1.0).	`1.0`

Returns:

Type	Description
`GeoDataFrame`	dgpd.GeoDataFrame: A GeoDataFrame containing scaled Polygon objects and their centroids.

Source code in src/segger/data/io.py

def generate_and_scale_polygons(
    self, boundaries_df: dd.DataFrame, scale_factor: float = 1.0
    ) -> dgpd.GeoDataFrame:
    """
    Generate and scale polygons from boundary coordinates using Dask.
    Keeps class structure intact by using static method for the core polygon generation.

    Parameters:
        boundaries_df (dd.DataFrame): DataFrame containing boundary coordinates.
        scale_factor (float, optional): The factor by which to scale the polygons (default is 1.0).

    Returns:
        dgpd.GeoDataFrame: A GeoDataFrame containing scaled Polygon objects and their centroids.
    """
    # if self.verbose: print(f"No precomputed polygons provided. Computing polygons from boundaries with a scale factor of {scale_factor}.")

    # Extract required columns from self.keys
    cell_id_column = self.keys.CELL_ID.value
    vertex_x_column = self.keys.BOUNDARIES_VERTEX_X.value
    vertex_y_column = self.keys.BOUNDARIES_VERTEX_Y.value

    create_polygon = self.create_scaled_polygon
    # Use a lambda to wrap the static method call and avoid passing the function object directly to Dask
    polygons_ddf = boundaries_df.groupby(cell_id_column).apply(
        lambda group: create_polygon(
            group=group, scale_factor=scale_factor,
            keys={  # Pass keys as a dict for the lambda function
                'vertex_x': vertex_x_column,
                'vertex_y': vertex_y_column,
                'cell_id': cell_id_column
            }
        )
    )

    # Lazily compute centroids for each polygon
    if self.verbose: print("Adding centroids to the polygons...")
    polygons_ddf['centroid_x'] = polygons_ddf.geometry.centroid.x
    polygons_ddf['centroid_y'] = polygons_ddf.geometry.centroid.y

    polygons_ddf = polygons_ddf.drop_duplicates()
    # polygons_ddf = polygons_ddf.to_crs("EPSG:3857")

    return polygons_ddf

load_boundaries ¶

load_boundaries(path, file_format='parquet', x_min=None, x_max=None, y_min=None, y_max=None)

Load boundaries data lazily using Dask, filtering by the specified bounding box.

Parameters:

Name	Type	Description	Default
`path`	`Path`	Path to the boundaries file.	required
`file_format`	`str`	Format of the file to load. Only 'parquet' is supported in this refactor.	`'parquet'`
`x_min`	`float`	Minimum X-coordinate for the bounding box.	`None`
`x_max`	`float`	Maximum X-coordinate for the bounding box.	`None`
`y_min`	`float`	Minimum Y-coordinate for the bounding box.	`None`
`y_max`	`float`	Maximum Y-coordinate for the bounding box.	`None`

Returns:

Type	Description
`DataFrame`	dd.DataFrame: The filtered boundaries DataFrame.

Source code in src/segger/data/io.py

def load_boundaries(
    self, 
    path: Path, 
    file_format: str = "parquet", 
    x_min: float = None, 
    x_max: float = None, 
    y_min: float = None, 
    y_max: float = None
) -> dd.DataFrame:
    """
    Load boundaries data lazily using Dask, filtering by the specified bounding box.

    Parameters:
        path (Path): Path to the boundaries file.
        file_format (str, optional): Format of the file to load. Only 'parquet' is supported in this refactor.
        x_min (float, optional): Minimum X-coordinate for the bounding box.
        x_max (float, optional): Maximum X-coordinate for the bounding box.
        y_min (float, optional): Minimum Y-coordinate for the bounding box.
        y_max (float, optional): Maximum Y-coordinate for the bounding box.

    Returns:
        dd.DataFrame: The filtered boundaries DataFrame.
    """
    if file_format != "parquet":
        raise ValueError(f"Unsupported file format: {file_format}")

    self.boundaries_path = path

    # Use bounding box values from set_metadata if not explicitly provided
    x_min = x_min or self.x_min
    x_max = x_max or self.x_max
    y_min = y_min or self.y_min
    y_max = y_max or self.y_max

    # Define the list of columns to read
    columns_to_read = [
        self.keys.BOUNDARIES_VERTEX_X.value,
        self.keys.BOUNDARIES_VERTEX_Y.value,
        self.keys.CELL_ID.value
    ]

    # Use filters to only load data within the specified bounding box (x_min, x_max, y_min, y_max)
    filters = [
        (self.keys.BOUNDARIES_VERTEX_X.value, '>=', x_min),
        (self.keys.BOUNDARIES_VERTEX_X.value, '<=', x_max),
        (self.keys.BOUNDARIES_VERTEX_Y.value, '>=', y_min),
        (self.keys.BOUNDARIES_VERTEX_Y.value, '<=', y_max)
    ]

    # Load the dataset lazily with filters applied for the bounding box
    boundaries_df = dd.read_parquet(path, columns=columns_to_read, filters=filters)

    # Convert the cell IDs to strings lazily
    boundaries_df[self.keys.CELL_ID.value] = boundaries_df[self.keys.CELL_ID.value].apply(
        lambda x: str(x) if pd.notnull(x) else None,
        meta=(self.keys.CELL_ID.value, 'str')
    )

    if self.verbose: print(f"Loaded boundaries from '{path}' within bounding box ({x_min}, {x_max}, {y_min}, {y_max}).")

    return boundaries_df

load_transcripts ¶

load_transcripts(base_path=None, sample=None, transcripts_filename=None, path=None, file_format='parquet', x_min=None, x_max=None, y_min=None, y_max=None)

Load transcripts from a Parquet file using Dask for efficient chunked processing, only within the specified bounding box, and return the filtered DataFrame with integer token embeddings.

Parameters:

Name	Type	Description	Default
`base_path`	`Path`	The base directory path where samples are stored.	`None`
`sample`	`str`	The sample name or identifier.	`None`
`transcripts_filename`	`str`	The filename of the transcripts file (default is derived from the dataset keys).	`None`
`path`	`Path`	Specific path to the transcripts file.	`None`
`file_format`	`str`	Format of the file to load (default is 'parquet').	`'parquet'`
`x_min`	`float`	Minimum X-coordinate for the bounding box.	`None`
`x_max`	`float`	Maximum X-coordinate for the bounding box.	`None`
`y_min`	`float`	Minimum Y-coordinate for the bounding box.	`None`
`y_max`	`float`	Maximum Y-coordinate for the bounding box.	`None`

Returns:

Type	Description
`DataFrame`	dd.DataFrame: The filtered transcripts DataFrame.

Source code in src/segger/data/io.py

def load_transcripts(
    self,
    base_path: Path = None,
    sample: str = None,
    transcripts_filename: str = None,
    path: Path = None,
    file_format: str = "parquet",
    x_min: float = None,
    x_max: float = None,
    y_min: float = None,
    y_max: float = None,
    # additional_embeddings: Optional[Dict[str, pd.DataFrame]] = None,
) -> dd.DataFrame:
    """
    Load transcripts from a Parquet file using Dask for efficient chunked processing,
    only within the specified bounding box, and return the filtered DataFrame with integer token embeddings.

    Parameters:
        base_path (Path, optional): The base directory path where samples are stored.
        sample (str, optional): The sample name or identifier.
        transcripts_filename (str, optional): The filename of the transcripts file (default is derived from the dataset keys).
        path (Path, optional): Specific path to the transcripts file.
        file_format (str, optional): Format of the file to load (default is 'parquet').
        x_min (float, optional): Minimum X-coordinate for the bounding box.
        x_max (float, optional): Maximum X-coordinate for the bounding box.
        y_min (float, optional): Minimum Y-coordinate for the bounding box.
        y_max (float, optional): Maximum Y-coordinate for the bounding box.

    Returns:
        dd.DataFrame: The filtered transcripts DataFrame.
    """
    if file_format != "parquet":
        raise ValueError("This version only supports parquet files with Dask.")

    # Set the file path for transcripts
    transcripts_filename = transcripts_filename or self.keys.TRANSCRIPTS_FILE.value
    file_path = path or (base_path / sample / transcripts_filename)
    self.transcripts_path = file_path

    # Set metadata
    # self.set_metadata()

    # Use bounding box values from set_metadata if not explicitly provided
    x_min = x_min or self.x_min
    x_max = x_max or self.x_max
    y_min = y_min or self.y_min
    y_max = y_max or self.y_max

    # Check for available columns in the file's metadata (without loading the data)
    parquet_metadata = dd.read_parquet(file_path, meta_only=True)
    available_columns = parquet_metadata.columns

    # Define the list of columns to read
    columns_to_read = [
        self.keys.TRANSCRIPTS_ID.value,
        self.keys.TRANSCRIPTS_X.value,
        self.keys.TRANSCRIPTS_Y.value,
        self.keys.FEATURE_NAME.value,
        self.keys.CELL_ID.value
    ]

    # Check if the QUALITY_VALUE key exists in the dataset, and add it to the columns list if present
    if self.keys.QUALITY_VALUE.value in available_columns:
        columns_to_read.append(self.keys.QUALITY_VALUE.value)

    if self.keys.OVERLAPS_BOUNDARY.value in available_columns:
        columns_to_read.append(self.keys.OVERLAPS_BOUNDARY.value)

    # Use filters to only load data within the specified bounding box (x_min, x_max, y_min, y_max)
    filters = [
        (self.keys.TRANSCRIPTS_X.value, '>=', x_min),
        (self.keys.TRANSCRIPTS_X.value, '<=', x_max),
        (self.keys.TRANSCRIPTS_Y.value, '>=', y_min),
        (self.keys.TRANSCRIPTS_Y.value, '<=', y_max)
    ]

    # Load the dataset lazily with filters applied for the bounding box
    transcripts_df = dd.read_parquet(file_path, columns=columns_to_read, filters=filters).compute()

    # Convert transcript and cell IDs to strings lazily
    transcripts_df[self.keys.TRANSCRIPTS_ID.value] = transcripts_df[self.keys.TRANSCRIPTS_ID.value].apply(
        lambda x: str(x) if pd.notnull(x) else None,
    )
    transcripts_df[self.keys.CELL_ID.value] = transcripts_df[self.keys.CELL_ID.value].apply(
        lambda x: str(x) if pd.notnull(x) else None,
    )

    # Convert feature names from bytes to strings if necessary
    if pd.api.types.is_object_dtype(transcripts_df[self.keys.FEATURE_NAME.value]):
        transcripts_df[self.keys.FEATURE_NAME.value] = transcripts_df[self.keys.FEATURE_NAME.value].astype(str)

    # Apply dataset-specific filtering (e.g., quality filtering for Xenium)
    transcripts_df = self.filter_transcripts(transcripts_df)

    # Handle additional embeddings if provided
    if self.embedding_df:
        valid_genes = self.embedding_df.index
        # Lazily count the number of rows in the DataFrame before filtering
        initial_count = delayed(lambda df: df.shape[0])(transcripts_df)
        # Filter the DataFrame lazily based on valid genes from embeddings
        transcripts_df = transcripts_df[
            transcripts_df[self.keys.FEATURE_NAME.value].isin(valid_genes)
        ]
        final_count = delayed(lambda df: df.shape[0])(transcripts_df)
        if self.verbose: print(f"Dropped {initial_count - final_count} transcripts not found in {key} embedding.")

    # Ensure that the 'OVERLAPS_BOUNDARY' column is boolean if it exists
    if self.keys.OVERLAPS_BOUNDARY.value in transcripts_df.columns:
        transcripts_df[self.keys.OVERLAPS_BOUNDARY.value] = transcripts_df[self.keys.OVERLAPS_BOUNDARY.value].astype(bool)

    return transcripts_df

save_dataset_for_segger ¶

save_dataset_for_segger(processed_dir, x_size=1000, y_size=1000, d_x=900, d_y=900, margin_x=None, margin_y=None, compute_labels=True, r_tx=5, k_tx=3, val_prob=0.1, test_prob=0.2, neg_sampling_ratio_approx=5, sampling_rate=1, num_workers=1, scale_boundaries=1.0, method='kd_tree', gpu=False, workers=1)

Saves the dataset for Segger in a processed format using Dask for parallel and lazy processing.

Parameters:

Name	Type	Description	Default
`processed_dir`	`Path`	Directory to save the processed dataset.	required
`x_size`	`float`	Width of each tile.	`1000`
`y_size`	`float`	Height of each tile.	`1000`
`d_x`	`float`	Step size in the x direction for tiles.	`900`
`d_y`	`float`	Step size in the y direction for tiles.	`900`
`margin_x`	`float`	Margin in the x direction to include transcripts.	`None`
`margin_y`	`float`	Margin in the y direction to include transcripts.	`None`
`compute_labels`	`bool`	Whether to compute edge labels for tx_belongs_bd edges.	`True`
`r_tx`	`float`	Radius for building the transcript-to-transcript graph.	`5`
`k_tx`	`int`	Number of nearest neighbors for the tx-tx graph.	`3`
`val_prob`	`float`	Probability of assigning a tile to the validation set.	`0.1`
`test_prob`	`float`	Probability of assigning a tile to the test set.	`0.2`
`neg_sampling_ratio_approx`	`float`	Approximate ratio of negative samples.	`5`
`sampling_rate`	`float`	Rate of sampling tiles.	`1`
`num_workers`	`int`	Number of workers to use for parallel processing.	`1`
`scale_boundaries`	`float`	The factor by which to scale the boundary polygons. Default is 1.0.	`1.0`
`method`	`str`	Method for computing edge indices (e.g., 'kd_tree', 'faiss').	`'kd_tree'`
`gpu`	`bool`	Whether to use GPU acceleration for edge index computation.	`False`
`workers`	`int`	Number of workers to use to compute the neighborhood graph (per tile).	`1`

Source code in src/segger/data/io.py

def save_dataset_for_segger(
    self,
    processed_dir: Path,
    x_size: float = 1000,
    y_size: float = 1000,
    d_x: float = 900,
    d_y: float = 900,
    margin_x: float = None,
    margin_y: float = None,
    compute_labels: bool = True,
    r_tx: float = 5,
    k_tx: int = 3,
    val_prob: float = 0.1,
    test_prob: float = 0.2,
    neg_sampling_ratio_approx: float = 5,
    sampling_rate: float = 1,
    num_workers: int = 1,
    scale_boundaries: float = 1.0,
    method: str = 'kd_tree',
    gpu: bool = False,
    workers: int = 1
) -> None:
    """
    Saves the dataset for Segger in a processed format using Dask for parallel and lazy processing.

    Parameters:
        processed_dir (Path): Directory to save the processed dataset.
        x_size (float, optional): Width of each tile.
        y_size (float, optional): Height of each tile.
        d_x (float, optional): Step size in the x direction for tiles.
        d_y (float, optional): Step size in the y direction for tiles.
        margin_x (float, optional): Margin in the x direction to include transcripts.
        margin_y (float, optional): Margin in the y direction to include transcripts.
        compute_labels (bool, optional): Whether to compute edge labels for tx_belongs_bd edges.
        r_tx (float, optional): Radius for building the transcript-to-transcript graph.
        k_tx (int, optional): Number of nearest neighbors for the tx-tx graph.
        val_prob (float, optional): Probability of assigning a tile to the validation set.
        test_prob (float, optional): Probability of assigning a tile to the test set.
        neg_sampling_ratio_approx (float, optional): Approximate ratio of negative samples.
        sampling_rate (float, optional): Rate of sampling tiles.
        num_workers (int, optional): Number of workers to use for parallel processing.
        scale_boundaries (float, optional): The factor by which to scale the boundary polygons. Default is 1.0.
        method (str, optional): Method for computing edge indices (e.g., 'kd_tree', 'faiss').
        gpu (bool, optional): Whether to use GPU acceleration for edge index computation.
        workers (int, optional): Number of workers to use to compute the neighborhood graph (per tile).

    """
    # Prepare directories for storing processed tiles
    self._prepare_directories(processed_dir)

    # Get x and y coordinate ranges for tiling
    x_range, y_range = self._get_ranges(d_x, d_y)

    # Generate parameters for each tile
    tile_params = self._generate_tile_params(
        x_range, y_range, x_size, y_size, margin_x, margin_y, compute_labels, 
        r_tx, k_tx, val_prob, test_prob, neg_sampling_ratio_approx, sampling_rate, 
        processed_dir, scale_boundaries, method, gpu, workers
    )

    # Process each tile using Dask to parallelize the task
    if self.verbose: print("Starting tile processing...")
    tasks = [delayed(self._process_tile)(params) for params in tile_params]

    with ProgressBar():
    # Use Dask to process all tiles in parallel
        dask.compute(*tasks, num_workers=num_workers)
    if self.verbose: print("Tile processing completed.")

set_embedding ¶

set_embedding(embedding_name)

Set the current embedding type for the transcripts.

Parameters:

Name	Type	Description	Default
`embedding_name`		str The name of the embedding to use.	required

Source code in src/segger/data/io.py

def set_embedding(self, embedding_name: str) -> None:
    """
    Set the current embedding type for the transcripts.

    Parameters:
        embedding_name : str
            The name of the embedding to use.

    """
    if embedding_name in self.embeddings_dict:
        self.current_embedding = embedding_name
    else:
        raise ValueError(f"Embedding {embedding_name} not found in embeddings_dict.")

set_file_paths ¶

set_file_paths(transcripts_path, boundaries_path)

Set the paths for the transcript and boundary files.

Parameters:

Name	Type	Description	Default
`transcripts_path`	`Path`	Path to the Parquet file containing transcripts data.	required
`boundaries_path`	`Path`	Path to the Parquet file containing boundaries data.	required

Source code in src/segger/data/io.py

def set_file_paths(self, transcripts_path: Path, boundaries_path: Path) -> None:
    """
    Set the paths for the transcript and boundary files.

    Parameters:
        transcripts_path (Path): Path to the Parquet file containing transcripts data.
        boundaries_path (Path): Path to the Parquet file containing boundaries data.
    """
    self.transcripts_path = transcripts_path
    self.boundaries_path = boundaries_path

    if self.verbose: print(f"Set transcripts file path to {transcripts_path}")
    if self.verbose: print(f"Set boundaries file path to {boundaries_path}")

set_metadata ¶

set_metadata()

Set metadata for the transcript dataset, including bounding box limits and unique gene names, without reading the entire Parquet file. Additionally, return integer tokens for unique gene names instead of one-hot encodings and store the lookup table for later mapping.

Source code in src/segger/data/io.py

def set_metadata(self) -> None:
    """
    Set metadata for the transcript dataset, including bounding box limits and unique gene names,
    without reading the entire Parquet file. Additionally, return integer tokens for unique gene names 
    instead of one-hot encodings and store the lookup table for later mapping.
    """
    # Load the Parquet file metadata
    parquet_file = pq.ParquetFile(self.transcripts_path)

    # Get the column names for X, Y, and feature names from the class's keys
    x_col = self.keys.TRANSCRIPTS_X.value
    y_col = self.keys.TRANSCRIPTS_Y.value
    feature_col = self.keys.FEATURE_NAME.value

    # Initialize variables to track min/max values for X and Y
    x_min, x_max, y_min, y_max = float('inf'), float('-inf'), float('inf'), float('-inf')

    # Extract unique gene names and ensure they're strings
    gene_set = set()

    # Define the filter for unwanted codewords
    filter_codewords = (
        "NegControlProbe_",
        "antisense_",
        "NegControlCodeword_",
        "BLANK_",
        "DeprecatedCodeword_",
    )

    # Iterate over row groups to extract statistics and unique gene names
    for row_group_idx in range(parquet_file.num_row_groups):
        row_group_table = parquet_file.read_row_group(row_group_idx, columns=[x_col, y_col, feature_col])

        # Update the bounding box values (min/max)
        x_values = row_group_table[x_col].to_pandas()
        y_values = row_group_table[y_col].to_pandas()

        x_min = min(x_min, x_values.min())
        x_max = max(x_max, x_values.max())
        y_min = min(y_min, y_values.min())
        y_max = max(y_max, y_values.max())

        # Convert feature values (gene names) to strings and filter out unwanted codewords
        feature_values = row_group_table[feature_col].to_pandas().apply(
            lambda x: x.decode('utf-8') if isinstance(x, bytes) else str(x),
        )

        # Filter out unwanted codewords
        filtered_genes = feature_values[~feature_values.str.startswith(filter_codewords)]

        # Update the unique gene set
        gene_set.update(filtered_genes.unique())

    # Set bounding box limits
    self.x_min = x_min
    self.x_max = x_max
    self.y_min = y_min
    self.y_max = y_max

    if self.verbose: print(f"Bounding box limits set: x_min={self.x_min}, x_max={self.x_max}, y_min={self.y_min}, y_max={self.y_max}")

    # Convert the set of unique genes into a sorted list for consistent ordering
    self.unique_genes = sorted(gene_set)
    if self.verbose: print(f"Extracted {len(self.unique_genes)} unique gene names for integer tokenization.")

    # Initialize a LabelEncoder to convert unique genes into integer tokens
    self.tx_encoder = LabelEncoder()

    # Fit the LabelEncoder on the unique genes
    self.tx_encoder.fit(self.unique_genes)

    # Store the integer tokens mapping to gene names
    self.gene_to_token_map = dict(zip(self.tx_encoder.classes_, self.tx_encoder.transform(self.tx_encoder.classes_)))


    if self.verbose: print("Integer tokens have been computed and stored based on unique gene names.")

    # Optional: Create a reverse mapping for lookup purposes (token to gene)
    self.token_to_gene_map = {v: k for k, v in self.gene_to_token_map.items()}


    if self.verbose: print("Lookup tables (gene_to_token_map and token_to_gene_map) have been created.")

XeniumSample ¶

XeniumSample(transcripts_df=None, transcripts_radius=10, boundaries_graph=False, verbose=True)

Bases: SpatialTranscriptomicsSample

Source code in src/segger/data/io.py

def __init__(self, transcripts_df: dd.DataFrame = None, transcripts_radius: int = 10, boundaries_graph: bool = False, verbose: bool = True):
    super().__init__(transcripts_df, transcripts_radius, boundaries_graph, XeniumKeys, verbose=verbose)

filter_transcripts ¶

filter_transcripts(transcripts_df, min_qv=20.0)

Filters transcripts based on quality value and removes unwanted transcripts for Xenium using Dask.

Parameters:

Name	Type	Description	Default
`transcripts_df`	`DataFrame`	The Dask DataFrame containing transcript data.	required
`min_qv`	`float`	The minimum quality value threshold for filtering transcripts.	`20.0`

Returns:

Type	Description
`DataFrame`	dd.DataFrame: The filtered Dask DataFrame.

Source code in src/segger/data/io.py

def filter_transcripts(self, transcripts_df: dd.DataFrame, min_qv: float = 20.0) -> dd.DataFrame:
    """
    Filters transcripts based on quality value and removes unwanted transcripts for Xenium using Dask.

    Parameters:
        transcripts_df (dd.DataFrame): The Dask DataFrame containing transcript data.
        min_qv (float, optional): The minimum quality value threshold for filtering transcripts.

    Returns:
        dd.DataFrame: The filtered Dask DataFrame.
    """
    filter_codewords = (
        "NegControlProbe_",
        "antisense_",
        "NegControlCodeword_",
        "BLANK_",
        "DeprecatedCodeword_",
    )

    # Ensure FEATURE_NAME is a string type for proper filtering (compatible with Dask)
    # Handle potential bytes to string conversion for Dask DataFrame
    if pd.api.types.is_object_dtype(transcripts_df[self.keys.FEATURE_NAME.value]):
        transcripts_df[self.keys.FEATURE_NAME.value] = transcripts_df[self.keys.FEATURE_NAME.value].apply(
            lambda x: x.decode('utf-8') if isinstance(x, bytes) else x,
            meta=('feature_name', 'str'),
        )

    # Apply the quality value filter using Dask
    mask_quality = transcripts_df[self.keys.QUALITY_VALUE.value] >= min_qv

    # Apply the filter for unwanted codewords using Dask string functions
    mask_codewords = ~transcripts_df[self.keys.FEATURE_NAME.value].str.startswith(filter_codewords)

    # Combine the filters and return the filtered Dask DataFrame
    mask = mask_quality & mask_codewords

    # Return the filtered DataFrame lazily
    return transcripts_df[mask]

calculate_gene_celltype_abundance_embedding ¶

calculate_gene_celltype_abundance_embedding(adata, celltype_column)

Calculate the cell type abundance embedding for each gene based on the percentage of cells in each cell type that express the gene (non-zero expression).

Parameters:

Name	Type	Description	Default
`adata`	`AnnData`	An AnnData object containing gene expression data and cell type information.	required
`celltype_column`	`str`	The column name in `adata.obs` that contains the cell type information.	required

Returns:

Type	Description
`DataFrame`	pd.DataFrame: A DataFrame where rows are genes and columns are cell types, with each value representing the percentage of cells in that cell type expressing the gene.

Example

adata = AnnData(...) # Load your scRNA-seq AnnData object celltype_column = 'celltype_major' abundance_df = calculate_gene_celltype_abundance_embedding(adata, celltype_column) abundance_df.head()

Source code in src/segger/data/utils.py

def calculate_gene_celltype_abundance_embedding(adata: ad.AnnData, celltype_column: str) -> pd.DataFrame:
    """Calculate the cell type abundance embedding for each gene based on the percentage of cells in each cell type 
    that express the gene (non-zero expression).

    Parameters:
        adata (ad.AnnData): An AnnData object containing gene expression data and cell type information.
        celltype_column (str): The column name in `adata.obs` that contains the cell type information.

    Returns:
        pd.DataFrame: A DataFrame where rows are genes and columns are cell types, with each value representing 
            the percentage of cells in that cell type expressing the gene.

    Example:
        >>> adata = AnnData(...)  # Load your scRNA-seq AnnData object
        >>> celltype_column = 'celltype_major'
        >>> abundance_df = calculate_gene_celltype_abundance_embedding(adata, celltype_column)
        >>> abundance_df.head()
    """
    # Extract expression data (cells x genes) and cell type information (cells)
    expression_data = adata.X.toarray() if hasattr(adata.X, "toarray") else adata.X
    cell_types = adata.obs[celltype_column].values
    # Create a binary matrix for gene expression (1 if non-zero, 0 otherwise)
    gene_expression_binary = (expression_data > 0).astype(int)
    # Convert the binary matrix to a DataFrame
    gene_expression_df = pd.DataFrame(gene_expression_binary, index=adata.obs_names, columns=adata.var_names)
    # Perform one-hot encoding on the cell types
    encoder = OneHotEncoder(sparse_output=False)
    cell_type_encoded = encoder.fit_transform(cell_types.reshape(-1, 1))
    # Calculate the percentage of cells expressing each gene per cell type
    cell_type_abundance_list = []
    for i in range(cell_type_encoded.shape[1]):
        # Extract cells of the current cell type
        cell_type_mask = cell_type_encoded[:, i] == 1
        # Calculate the abundance: sum of non-zero expressions in this cell type / total cells in this cell type
        abundance = gene_expression_df[cell_type_mask].mean(axis=0) * 100
        cell_type_abundance_list.append(abundance)
    # Create a DataFrame for the cell type abundance with gene names as rows and cell types as columns
    cell_type_abundance_df = pd.DataFrame(cell_type_abundance_list, 
                                            columns=adata.var_names, 
                                            index=encoder.categories_[0]).T
    return cell_type_abundance_df

compute_transcript_metrics ¶

compute_transcript_metrics(df, qv_threshold=30, cell_id_col='cell_id')

Computes various metrics for a given dataframe of transcript data filtered by quality value threshold.

Parameters:

Name	Type	Description	Default
`df`	`DataFrame`	The dataframe containing transcript data.	required
`qv_threshold`	`float`	The quality value threshold for filtering transcripts.	`30`
`cell_id_col`	`str`	The name of the column representing the cell ID.	`'cell_id'`

Returns:

Type Description

Dict[str, Any]

Dict[str, Any]: A dictionary containing various transcript metrics: - 'percent_assigned' (float): The percentage of assigned transcripts. - 'percent_cytoplasmic' (float): The percentage of cytoplasmic transcripts among assigned transcripts. - 'percent_nucleus' (float): The percentage of nucleus transcripts among assigned transcripts. - 'percent_non_assigned_cytoplasmic' (float): The percentage of non-assigned cytoplasmic transcripts. - 'gene_metrics' (pd.DataFrame): A dataframe containing gene-level metrics.

Source code in src/segger/data/utils.py

def compute_transcript_metrics(
    df: pd.DataFrame,
    qv_threshold: float = 30,
    cell_id_col: str = 'cell_id'
) -> Dict[str, Any]:
    """
    Computes various metrics for a given dataframe of transcript data filtered by quality value threshold.

    Parameters:
        df (pd.DataFrame): The dataframe containing transcript data.
        qv_threshold (float): The quality value threshold for filtering transcripts.
        cell_id_col (str): The name of the column representing the cell ID.

    Returns:
        Dict[str, Any]: A dictionary containing various transcript metrics:
            - 'percent_assigned' (float): The percentage of assigned transcripts.
            - 'percent_cytoplasmic' (float): The percentage of cytoplasmic transcripts among assigned transcripts.
            - 'percent_nucleus' (float): The percentage of nucleus transcripts among assigned transcripts.
            - 'percent_non_assigned_cytoplasmic' (float): The percentage of non-assigned cytoplasmic transcripts.
            - 'gene_metrics' (pd.DataFrame): A dataframe containing gene-level metrics.
    """
    df_filtered = df[df['qv'] > qv_threshold]
    total_transcripts = len(df_filtered)
    assigned_transcripts = df_filtered[df_filtered[cell_id_col] != -1]
    percent_assigned = len(assigned_transcripts) / total_transcripts * 100
    cytoplasmic_transcripts = assigned_transcripts[assigned_transcripts['overlaps_nucleus'] != 1]
    percent_cytoplasmic = len(cytoplasmic_transcripts) / len(assigned_transcripts) * 100
    percent_nucleus = 100 - percent_cytoplasmic
    non_assigned_transcripts = df_filtered[df_filtered[cell_id_col] == -1]
    non_assigned_cytoplasmic = non_assigned_transcripts[non_assigned_transcripts['overlaps_nucleus'] != 1]
    percent_non_assigned_cytoplasmic = len(non_assigned_cytoplasmic) / len(non_assigned_transcripts) * 100

    gene_group_assigned = assigned_transcripts.groupby('feature_name')
    gene_group_all = df_filtered.groupby('feature_name')
    gene_percent_assigned = (gene_group_assigned.size() / gene_group_all.size() * 100).reset_index(name='percent_assigned')
    cytoplasmic_gene_group = cytoplasmic_transcripts.groupby('feature_name')
    gene_percent_cytoplasmic = (cytoplasmic_gene_group.size() / len(cytoplasmic_transcripts) * 100).reset_index(name='percent_cytoplasmic')
    gene_metrics = pd.merge(gene_percent_assigned, gene_percent_cytoplasmic, on='feature_name', how='outer').fillna(0)

    results = {
        'percent_assigned': percent_assigned,
        'percent_cytoplasmic': percent_cytoplasmic,
        'percent_nucleus': percent_nucleus,
        'percent_non_assigned_cytoplasmic': percent_non_assigned_cytoplasmic,
        'gene_metrics': gene_metrics
    }
    return results

create_anndata ¶

create_anndata(df, panel_df=None, min_transcripts=5, cell_id_col='cell_id', qv_threshold=30, min_cell_area=10.0, max_cell_area=1000.0)

Generates an AnnData object from a dataframe of segmented transcriptomics data.

Parameters:

Name	Type	Description	Default
`df`	`DataFrame`	The dataframe containing segmented transcriptomics data.	required
`panel_df`	`Optional[DataFrame]`	The dataframe containing panel information.	`None`
`min_transcripts`	`int`	The minimum number of transcripts required for a cell to be included.	`5`
`cell_id_col`	`str`	The column name representing the cell ID in the input dataframe.	`'cell_id'`
`qv_threshold`	`float`	The quality value threshold for filtering transcripts.	`30`
`min_cell_area`	`float`	The minimum cell area to include a cell.	`10.0`
`max_cell_area`	`float`	The maximum cell area to include a cell.	`1000.0`

Returns:

Type	Description
`AnnData`	ad.AnnData: The generated AnnData object containing the transcriptomics data and metadata.

Source code in src/segger/data/utils.py

def create_anndata(
    df: pd.DataFrame, 
    panel_df: Optional[pd.DataFrame] = None, 
    min_transcripts: int = 5, 
    cell_id_col: str = 'cell_id', 
    qv_threshold: float = 30, 
    min_cell_area: float = 10.0, 
    max_cell_area: float = 1000.0
) -> ad.AnnData:
    """
    Generates an AnnData object from a dataframe of segmented transcriptomics data.

    Parameters:
        df (pd.DataFrame): The dataframe containing segmented transcriptomics data.
        panel_df (Optional[pd.DataFrame]): The dataframe containing panel information.
        min_transcripts (int): The minimum number of transcripts required for a cell to be included.
        cell_id_col (str): The column name representing the cell ID in the input dataframe.
        qv_threshold (float): The quality value threshold for filtering transcripts.
        min_cell_area (float): The minimum cell area to include a cell.
        max_cell_area (float): The maximum cell area to include a cell.

    Returns:
        ad.AnnData: The generated AnnData object containing the transcriptomics data and metadata.
    """
    df_filtered = filter_transcripts(df, min_qv=qv_threshold)
    metrics = compute_transcript_metrics(df_filtered, qv_threshold, cell_id_col)
    df_filtered = df_filtered[df_filtered[cell_id_col].astype(str) != '-1']
    pivot_df = df_filtered.rename(columns={
        cell_id_col: "cell",
        "feature_name": "gene"
    })[['cell', 'gene']].pivot_table(index='cell', columns='gene', aggfunc='size', fill_value=0)
    pivot_df = pivot_df[pivot_df.sum(axis=1) >= min_transcripts]
    cell_summary = []
    for cell_id, cell_data in df_filtered.groupby(cell_id_col):
        if len(cell_data) < min_transcripts:
            continue
        cell_convex_hull = ConvexHull(cell_data[['x_location', 'y_location']])
        cell_area = cell_convex_hull.area
        if cell_area < min_cell_area or cell_area > max_cell_area:
            continue
        if 'nucleus_distance' in cell_data:
            nucleus_data = cell_data[cell_data['nucleus_distance'] == 0]
        else:
            nucleus_data = cell_data[cell_data['overlaps_nucleus'] == 1]
        if len(nucleus_data) >= 3:
            nucleus_convex_hull = ConvexHull(nucleus_data[['x_location', 'y_location']])
        else:
            nucleus_convex_hull = None
        cell_summary.append({
            "cell": cell_id,
            "cell_centroid_x": cell_data['x_location'].mean(),
            "cell_centroid_y": cell_data['y_location'].mean(),
            "cell_area": cell_area,
            "nucleus_centroid_x": nucleus_data['x_location'].mean() if len(nucleus_data) > 0 else cell_data['x_location'].mean(),
            "nucleus_centroid_y": nucleus_data['x_location'].mean() if len(nucleus_data) > 0 else cell_data['x_location'].mean(),
            "nucleus_area": nucleus_convex_hull.area if nucleus_convex_hull else 0,
            "percent_cytoplasmic": len(cell_data[cell_data['overlaps_nucleus'] != 1]) / len(cell_data) * 100,
            "has_nucleus": len(nucleus_data) > 0
        })
    cell_summary = pd.DataFrame(cell_summary).set_index("cell")
    if panel_df is not None:
        panel_df = panel_df.sort_values('gene')
        genes = panel_df['gene'].values
        for gene in genes:
            if gene not in pivot_df:
                pivot_df[gene] = 0
        pivot_df = pivot_df[genes.tolist()]
    if panel_df is None:
        var_df = pd.DataFrame([{
            "gene": i, 
            "feature_types": 'Gene Expression', 
            'genome': 'Unknown'
        } for i in np.unique(pivot_df.columns.values)]).set_index('gene')
    else:
        var_df = panel_df[['gene', 'ensembl']].rename(columns={'ensembl':'gene_ids'})
        var_df['feature_types'] = 'Gene Expression'
        var_df['genome'] = 'Unknown'
        var_df = var_df.set_index('gene')
    gene_metrics = metrics['gene_metrics'].set_index('feature_name')
    var_df = var_df.join(gene_metrics, how='left').fillna(0)
    cells = list(set(pivot_df.index) & set(cell_summary.index))
    pivot_df = pivot_df.loc[cells,:]
    cell_summary = cell_summary.loc[cells,:]
    adata = ad.AnnData(pivot_df.values)
    adata.var = var_df
    adata.obs['transcripts'] = pivot_df.sum(axis=1).values
    adata.obs['unique_transcripts'] = (pivot_df > 0).sum(axis=1).values
    adata.obs_names = pivot_df.index.values.tolist()
    adata.obs = pd.merge(adata.obs, cell_summary.loc[adata.obs_names,:], left_index=True, right_index=True)
    adata.uns['metrics'] = {
        'percent_assigned': metrics['percent_assigned'],
        'percent_cytoplasmic': metrics['percent_cytoplasmic'],
        'percent_nucleus': metrics['percent_nucleus'],
        'percent_non_assigned_cytoplasmic': metrics['percent_non_assigned_cytoplasmic']
    }
    return adata

filter_transcripts ¶

filter_transcripts(transcripts_df, min_qv=20.0)

Filters transcripts based on quality value and removes unwanted transcripts.

Parameters:

Name	Type	Description	Default
`transcripts_df`	`DataFrame`	The dataframe containing transcript data.	required
`min_qv`	`float`	The minimum quality value threshold for filtering transcripts.	`20.0`

Returns:

Type	Description
`DataFrame`	pd.DataFrame: The filtered dataframe.

Source code in src/segger/data/utils.py

def filter_transcripts(
    transcripts_df: pd.DataFrame,
    min_qv: float = 20.0,
) -> pd.DataFrame:
    """
    Filters transcripts based on quality value and removes unwanted transcripts.

    Parameters:
        transcripts_df (pd.DataFrame): The dataframe containing transcript data.
        min_qv (float): The minimum quality value threshold for filtering transcripts.

    Returns:
        pd.DataFrame: The filtered dataframe.
    """
    filter_codewords = (
        "NegControlProbe_",
        "antisense_",
        "NegControlCodeword_",
        "BLANK_",
        "DeprecatedCodeword_",
    )
    mask = transcripts_df["qv"].ge(min_qv)
    mask &= ~transcripts_df["feature_name"].str.startswith(filter_codewords)
    return transcripts_df[mask]

get_edge_index ¶

get_edge_index(coords_1, coords_2, k=5, dist=10, method='kd_tree', gpu=False, workers=1)

Computes edge indices using various methods (KD-Tree, FAISS, RAPIDS cuML, cuGraph, or cuSpatial).

Parameters:

Name	Type	Description	Default
`coords_1`	`ndarray`	First set of coordinates.	required
`coords_2`	`ndarray`	Second set of coordinates.	required
`k`	`int`	Number of nearest neighbors.	`5`
`dist`	`int`	Distance threshold.	`10`
`method`	`str`	The method to use ('kd_tree', 'faiss', 'rapids', 'cugraph', 'cuspatial').	`'kd_tree'`
`gpu`	`bool`	Whether to use GPU acceleration (applicable for FAISS).	`False`

Returns:

Type	Description
`Tensor`	torch.Tensor: Edge indices.

Source code in src/segger/data/utils.py

def get_edge_index(coords_1: np.ndarray, coords_2: np.ndarray, k: int = 5, dist: int = 10, method: str = 'kd_tree',
                   gpu: bool = False, workers: int = 1) -> torch.Tensor:
    """
    Computes edge indices using various methods (KD-Tree, FAISS, RAPIDS cuML, cuGraph, or cuSpatial).

    Parameters:
        coords_1 (np.ndarray): First set of coordinates.
        coords_2 (np.ndarray): Second set of coordinates.
        k (int, optional): Number of nearest neighbors.
        dist (int, optional): Distance threshold.
        method (str, optional): The method to use ('kd_tree', 'faiss', 'rapids', 'cugraph', 'cuspatial').
        gpu (bool, optional): Whether to use GPU acceleration (applicable for FAISS).

    Returns:
        torch.Tensor: Edge indices.
    """
    if method == 'kd_tree':
        return get_edge_index_kdtree(coords_1, coords_2, k=k, dist=dist, workers=workers)
    elif method == 'faiss':
        return get_edge_index_faiss(coords_1, coords_2, k=k, dist=dist, gpu=gpu)
    elif method == 'rapids':
        return get_edge_index_rapids(coords_1, coords_2, k=k, dist=dist)
    elif method == 'cugraph':
        return get_edge_index_cugraph(coords_1, coords_2, k=k, dist=dist)
    elif method == 'cuspatial':
        return get_edge_index_cuspatial(coords_1, coords_2, k=k, dist=dist)
    elif method == 'hnsw':
        return get_edge_index_hnsw(coords_1, coords_2, k=k, dist=dist)
    else:
        raise ValueError(f"Unknown method {method}")

segger.data¶

Submodules¶

Key Features¶

API Documentation¶

MerscopeSample ¶

filter_transcripts ¶

SpatialTranscriptomicsDataset ¶

processed_file_names property ¶

raw_file_names property ¶

download ¶

get ¶

len ¶

process ¶

SpatialTranscriptomicsSample ¶

build_pyg_data_from_tile ¶

compute_boundaries_geometries ¶

compute_transcript_overlap_with_boundaries ¶

create_scaled_polygon staticmethod ¶

filter_transcripts abstractmethod ¶

generate_and_scale_polygons ¶

load_boundaries ¶

load_transcripts ¶

save_dataset_for_segger ¶

set_embedding ¶

set_file_paths ¶

set_metadata ¶

XeniumSample ¶

filter_transcripts ¶

calculate_gene_celltype_abundance_embedding ¶

compute_transcript_metrics ¶

create_anndata ¶

filter_transcripts ¶

get_edge_index ¶

processed_file_names `property` ¶

raw_file_names `property` ¶

create_scaled_polygon `staticmethod` ¶

filter_transcripts `abstractmethod` ¶