mcmicro_to_scimap

Short Description

sm.pp.mcmicro_to_scimap: The function allows users to directly import the output from mcmicro into scimap.

Function

mcmicro_to_scimap(feature_table_path, remove_dna=True, remove_string_from_name=None, log=True, drop_markers=None, random_sample=None, unique_CellId=True, CellId='CellID', split='X_centroid', custom_imageid=None, min_cells=None, output_dir=None)

Parameters:

Name	Type	Description	Default
feature_table_path	list	This is a list of paths that lead to the single-cell spatial feature tables. Each image should have a unique path assigned to it.	required
remove_dna	bool	Remove the DNA channels from the final output. Looks for channels with the string 'dna' in it.	True
remove_string_from_name	string	Used to clean channel names. The given string will be removed from all marker names.	None
log	bool	Take Log of data (log1p transformation will be applied).	True
drop_markers	list	List of markers to drop from the analysis. e.g. ["CD3D", "CD20"].	None
random_sample	int	Randomly sub-sample data, new sample contains desired number of cells.	None
CellId	string	Name of the column that contains the cell ID.	'CellID'
unique_CellId	bool	By default, the function creates a unique name for each cell/row by combining the CellId and imageid. If you wish not to perform this operation please pass False. The function will use whatever is under CellId. In which case, please be careful to pass unique CellId especially when loading multiple datasets togeather.	True
split	string	To split the CSV into counts table and meta data, pass in the name of the column that immediately follows the marker quantification.	'X_centroid'
custom_imageid	string	Pass a user defined Image ID. By default the name of the CSV file is used.	None
min_cells	int	Images with less cells than int will be dropped Particulary useful when importing multiple images.	None
output_dir	string	Path to output directory.	None

Returns:

Type	Description
	AnnData Object

feature_table_path = ['/Users/aj/whole_sections/PTCL1_450.csv',
                  '/Users/aj/whole_sections/PTCL2_552.csv']
adata = sm.pp.mcmicro_to_scimap (feature_table_path, drop_markers= ['CD21', 'ACTIN'], random_sample=5000)

Source code in scimap/preprocessing/_mcmicro_to_scimap.py

def mcmicro_to_scimap (feature_table_path,
                       remove_dna=True,
                       remove_string_from_name=None,
                       log=True,
                       drop_markers=None,
                       random_sample=None, 
                       unique_CellId=True,
                       CellId='CellID',
                       split='X_centroid',
                       custom_imageid=None,
                       min_cells=None, 
                       output_dir=None):
    """
Parameters:

    feature_table_path (list):    
        This is a list of paths that lead to the single-cell spatial feature tables. Each image should have a unique path assigned to it.

    remove_dna (bool):    
        Remove the DNA channels from the final output. Looks for channels with the string 'dna' in it.

    remove_string_from_name (string):    
        Used to clean channel names. The given string will be removed from all marker names.

    log (bool):  
        Take Log of data (log1p transformation will be applied).

    drop_markers (list):   
        List of markers to drop from the analysis. e.g. ["CD3D", "CD20"].

    random_sample (int):  
        Randomly sub-sample data, new sample contains desired number of cells.

    CellId (string):  
        Name of the column that contains the cell ID.

    unique_CellId (bool):   
        By default, the function creates a unique name for each cell/row by combining the 
        `CellId` and `imageid`. If you wish not to perform this operation please pass `False`.
        The function will use whatever is under `CellId`. In which case, please be careful to pass unique `CellId`
        especially when loading multiple datasets togeather.  

    split (string):  
        To split the CSV into counts table and meta data, pass in the name of the column
        that immediately follows the marker quantification.

    custom_imageid (string):    
        Pass a user defined Image ID. By default the name of the CSV file is used.

    min_cells (int):   
        Images with less cells than int will be dropped
        Particulary useful when importing multiple images.

    output_dir (string):    
        Path to output directory. 

Returns:

    AnnData Object


Example:
```python
feature_table_path = ['/Users/aj/whole_sections/PTCL1_450.csv',
                  '/Users/aj/whole_sections/PTCL2_552.csv']
adata = sm.pp.mcmicro_to_scimap (feature_table_path, drop_markers= ['CD21', 'ACTIN'], random_sample=5000)
```
    """

    # feature_table_path list or string
    if isinstance(feature_table_path, str):
        feature_table_path = [feature_table_path]
    feature_table_path = [pathlib.Path(p) for p in feature_table_path]

    # Import data based on the location provided
    def load_process_data (image):
        # Print the data that is being processed
        print(f"Loading {image.name}")
        d = pd.read_csv(image)
        # If the data does not have a unique image ID column, add one.
        if 'imageid' not in d.columns:
            if custom_imageid is not None:
                imid = custom_imageid
            else:
                #imid = random.randint(1000000,9999999)
                imid = image.stem
            d['imageid'] = imid
        # Unique name for the data
        if unique_CellId is True:
            d.index = d['imageid'].astype(str)+'_'+d[CellId].astype(str)
        else:
            d.index = d[CellId]

        # move image id and cellID column to end
        cellid_col = [col for col in d.columns if col != CellId] + [CellId]; d = d[cellid_col]
        imageid_col = [col for col in d.columns if col != 'imageid'] + ['imageid']; d = d[imageid_col]
        # If there is INF replace with zero
        d = d.replace([np.inf, -np.inf], 0)
        # Return data
        return d
    # Apply function to all images and create a master dataframe
    r_load_process_data = lambda x: load_process_data(image=x) # Create lamda function
    all_data = list(map(r_load_process_data, list(feature_table_path))) # Apply function

    # Merge all the data into a single large dataframe
    for i in range(len(all_data)):
        all_data[i].columns = all_data[0].columns
    entire_data = pd.concat(all_data, axis=0, sort=False)

    # Randomly sample the data
    if random_sample is not None:
        entire_data = entire_data.sample(n=random_sample,replace=False)

    #Remove the images that contain less than a defined threshold of cells (min_cells)
    if min_cells is not None:
        to_drop = entire_data['imageid'].value_counts()[entire_data['imageid'].value_counts() < min_cells].index
        entire_data = entire_data[~entire_data['imageid'].isin(to_drop)]
        print('Removed Images that contained less than '+str(min_cells)+' cells: '+ str(to_drop.values))

    # Split the data into expression data and meta data
    # Step-1 (Find the index of the column with name Area)
    split_idx = entire_data.columns.get_loc(split)
    meta = entire_data.iloc [:,split_idx:]
    # Step-2 (select only the expression values)
    entire_data = entire_data.iloc [:,:split_idx]

    # Rename the columns of the data
    if remove_string_from_name is not None:
        entire_data.columns = entire_data.columns.str.replace(remove_string_from_name, '')

    # Save a copy of the column names in the uns space of ANNDATA
    markers = list(entire_data.columns)

    # Remove DNA channels
    if remove_dna is True:
        entire_data = entire_data.loc[:,~entire_data.columns.str.contains('dna', case=False)]

    # Drop unnecessary markers
    if drop_markers is not None:
        if isinstance(drop_markers, str):
            drop_markers = [drop_markers]
        entire_data = entire_data.drop(columns=drop_markers)

    # Create an anndata object
    adata = ad.AnnData(entire_data)
    adata.obs = meta
    adata.uns['all_markers'] = markers

    # Add log data
    if log is True:
        adata.raw = adata
        adata.X = np.log1p(adata.X)

    # Save data if requested
    if output_dir is not None:
        output_dir = pathlib.Path(output_dir)
        output_dir.mkdir(exist_ok=True, parents=True)
        imid = feature_table_path[0].stem
        adata.write(output_dir / f'{imid}.h5ad')
        #adata.write(str(output_dir) + '/' + imid + '.h5ad')
    else:    
        # Return data
        return adata