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Sm.pp.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
List of path to the single-cell spatial feature tables. Each Image should have a unique path supplied.

required
remove_dna

bool, optional
Remove the DNA channels from the final output. Looks for channels with the string 'dna' in it.

True
remove_string_from_name

string, optional
Used to celan up channel names. If a string is given, that particular string will be removed from all marker names. If multiple images are passed, just use the string that appears in the first image.

None
log

bool, optional
Log the data (log1p transformation will be applied).

True
drop_markers

list, optional
List of markers to drop from the analysis. e.g. ["CD3D", "CD20"].

None
random_sample

int, optional
Randomly sub-sample the data with the desired number of cells.

None
CellId

string, optional
Name of the column that contains the cell ID.

'CellID'
unique_CellId

bool, optional
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, optional
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, optional
Pass a user defined Image ID. By default the name of the CSV file is used.

None
min_cells

int, optional
If these many cells are not in the image, the image will be dropped. Particulary useful when importing multiple images.

None
output_dir

string, optional
Path to output directory.

None

Returns:

Type Description

AnnData Object

Examples:

1
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3
    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  
        List of path to the single-cell spatial feature tables. Each Image should have a unique path supplied.

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

    remove_string_from_name : string, optional  
        Used to celan up channel names. If a string is given, that particular string will be removed from all marker names.
        If multiple images are passed, just use the string that appears in the first image.

    log : bool, optional  
        Log the data (log1p transformation will be applied).

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

    random_sample : int, optional  
        Randomly sub-sample the data with the desired number of cells.

    CellId : string, optional  
        Name of the column that contains the cell ID.

    unique_CellId: bool, optional  
        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, optional  
        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, optional  
        Pass a user defined Image ID. By default the name of the CSV file is used.

    min_cells: int, optional  
        If these many cells are not in the image, the image will be dropped.
        Particulary useful when importing multiple images.

    output_dir: string, optional  
        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
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