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 |
required | |
remove_dna |
bool, optional |
True |
|
remove_string_from_name |
string, optional |
None |
|
log |
bool, optional |
True |
|
drop_markers |
list, optional |
None |
|
random_sample |
int, optional |
None |
|
CellId |
string, optional |
'CellID' |
|
unique_CellId |
bool, optional |
True |
|
split |
string, optional |
'X_centroid' |
|
custom_imageid |
string, optional |
None |
|
min_cells |
int, optional |
None |
|
output_dir |
string, optional |
None |
Returns:
| Type | Description |
|---|---|
AnnData Object |
Examples:
1 2 3 | |
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