Preprocessing API

The preprocessing module provides functions for preparing and transforming spatial omics data for analysis.

Overview

The preprocessing module includes the following key functionalities:

• split_large_ometiff: Split a large OME-TIFF file into smaller chunks
• extract_channels: Extract specific channels from an OME-TIFF file
• normalize_expression: Normalize expression data for analysis
• scale_data: Scale data to a specific range

Function Reference

def split_large_ometiff(file_path, output_dir, chunk_size=(2048, 2048)):
    """
    Split a large OME-TIFF file into smaller chunks.

    Parameters
    ----------
    file_path : str
        Path to the input OME-TIFF file
    output_dir : str
        Directory to save the output chunks
    chunk_size : tuple, optional
        Size of chunks (height, width)

    Returns
    -------
    dict
        Information about the chunking process
    """
    pass

def extract_channels(file_path, output_path, channels=None):
    """
    Extract specific channels from an OME-TIFF file.

    Parameters
    ----------
    file_path : str
        Path to the input OME-TIFF file
    output_path : str
        Path to save the extracted channels
    channels : list, optional
        List of channel indices to extract

    Returns
    -------
    dict
        Information about the extraction process
    """
    pass

def normalize_expression(data, method="log1p", scale_factor=10000):
    """
    Normalize expression data for analysis.

    Parameters
    ----------
    data : DataFrame or array-like
        Expression data to normalize
    method : str, optional
        Normalization method
    scale_factor : float, optional
        Scale factor for normalization

    Returns
    -------
    DataFrame or array-like
        Normalized data
    """
    pass

def scale_data(data, feature_range=(0, 1), axis=0):
    """
    Scale data to a specific range.

    Parameters
    ----------
    data : DataFrame or array-like
        Data to scale
    feature_range : tuple, optional
        Range to scale to
    axis : int, optional
        Axis along which to scale

    Returns
    -------
    DataFrame or array-like
        Scaled data
    """
    pass

Dependency Handling

The preprocessing module relies on several libraries for optimal performance, but can operate with limited functionality when some dependencies are missing:

Dependency	Required For	Fallback Behavior
tifffile	OME-TIFF reading/writing	Stub implementation with helpful error messages
scikit-image	Image transformations	Limited image processing capabilities
OpenCV (cv2)	Fast image I/O	Slower fallback implementation

When a dependency is missing, SMINT will log a warning but continue to operate with limited functionality.