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merge_adata_obs

Short Description

sm.pl.merge_adata_obs: This function is designed to consolidate multiple AnnData objects originating from the same image or dataset into a single cohesive unit. It is particularly useful when each object contains distinct metadata in .obs, such as results from various clustering algorithms executed in parallel. By merging these objects, users can streamline analyses and comparisons within a unified framework. It's important to note that this function is intended for merging objects from the same source and may not be suitable for combining data across different images or datasets.

Function

merge_adata_obs(adata, output_dir=None, verbose=True)

Parameters:

Name Type Description Default
adata list of anndata.AnnData or list of str

A list containing AnnData objects to be merged or paths to AnnData files. Each item in the list should either be an AnnData object already loaded into memory or a string representing the path to an AnnData file.

 required
output_dir str

The directory where the merged AnnData object should be saved. If specified, the merged object is saved to this directory as 'merged_adata.h5ad'. If not specified, the merged AnnData object is not automatically saved to disk.

 None
verbose bool

If True, prints messages about the renaming process.

True

Returns:

Name Type Description
adata AnnData

A single AnnData object resulting from the merger of input AnnData objects or files.
Example 
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# Example 1: Merge AnnData objects already loaded in memory
combined_adata = sm.hl.merge_adata_obs(adata=[adata1, adata2])

# Example 2: Merge AnnData objects from file paths
combined_adata = sm.hl.merge_adata_obs(adata=['./data/adata1.h5ad', './data/adata2.h5ad'])

# Example 3: Merge AnnData objects and save the combined object to a specified directory
combined_adata = sm.hl.merge_adata_obs(adata=[adata1, adata2], output_dir='./merged_data')
Source code in scimap/helpers/merge_adata_obs.py 
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def merge_adata_obs (adata, 
                     output_dir=None,
                     verbose=True):


    """
Parameters:
        adata (list of anndata.AnnData or list of str):  
            A list containing AnnData objects to be merged or paths to AnnData files. 
            Each item in the list should either be an AnnData object already loaded into memory 
            or a string representing the path to an AnnData file.

        output_dir (str, optional):  
            The directory where the merged AnnData object should be saved. 
            If specified, the merged object is saved to this directory as 'merged_adata.h5ad'.
            If not specified, the merged AnnData object is not automatically saved to disk.

        verbose (bool, optional):  
            If True, prints messages about the renaming process. 

Returns:
        adata (anndata.AnnData):  
            A single AnnData object resulting from the merger of input AnnData objects or files.

Example:
        ```python

        # Example 1: Merge AnnData objects already loaded in memory
        combined_adata = sm.hl.merge_adata_obs(adata=[adata1, adata2])

        # Example 2: Merge AnnData objects from file paths
        combined_adata = sm.hl.merge_adata_obs(adata=['./data/adata1.h5ad', './data/adata2.h5ad'])

        # Example 3: Merge AnnData objects and save the combined object to a specified directory
        combined_adata = sm.hl.merge_adata_obs(adata=[adata1, adata2], output_dir='./merged_data')

        ```
    """

    #adata = ["/Users/aj/Downloads/mcmicro_output.h5ad", "/Users/aj/Downloads/mcmicro_output_1.h5ad"]


    # Convert to list of anndata objects
    if isinstance (adata, list):
        adata = adata
    else:
        adata = [adata]

    # Resoluve the OBS and UNS section
    if isinstance(adata[0], str):
        df = pd.DataFrame()
        uns_count = []
        for i in adata:
            tmp = ad.read_h5ad(i)
            # OBS
            tmp_df = tmp.obs
            df = pd.concat([df, tmp_df], axis=1)
            # UNS
            uns_count.append(len(tmp.uns))
        # Keep non-duplicate obs columns
        df = df.loc[:,~df.columns.duplicated()]
        # Highest UNS
        uns_index = [i for i, j in enumerate(uns_count) if j == max(uns_count)][0]
    else:
        df = pd.DataFrame()
        uns_count = []
        for i in adata:
            # OBS
            tmp_df = i.obs
            df = pd.concat([df, tmp_df], axis=1)
            # UNS
            uns_count.append(len(i.uns))
        # Keep non-duplicate obs columns
        df = df.loc[:,~df.columns.duplicated()]
        # Highest UNS
        uns_index = [i for i, j in enumerate(uns_count) if j == max(uns_count)][0]


    # create the final anndata object
    # Load the data 
    if isinstance(adata[0], str):
        final_adata = ad.read_h5ad(adata[uns_index])
    else:
        final_adata = adata[uns_index]

    # reindex
    df = df.reindex (final_adata.obs.index)

    # replace obs
    final_adata.obs = df


    # Save data if requested
    if output_dir is not None:
        output_dir = pathlib.Path(output_dir)
        output_dir.mkdir(exist_ok=True, parents=True)
        final_adata.write(output_dir / 'combined_adata.h5ad')
    else:    
        # Return data
        return final_adata