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336 | def rescale (adata,
gate=None,
log=True,
imageid='imageid',
failed_markers=None,
method='all',
verbose=True,
random_state=0):
"""
Parameters:
adata (AnnData Object, required):
An annotated data object that contains single-cell expression data.
gate (DataFrame, optional):
A pandas DataFrame where the first column lists markers, and subsequent columns contain gate values for each image in the dataset. Column names must correspond to unique `imageid` identifiers. If a single column of gate values is provided for a dataset with multiple images, the same gate will be uniformly applied to all. If no gates are provided for specific markers, the function attempts to automatically determine gates using a Gaussian Mixture Model (GMM).
log (bool, optional):
If `True`, the data in `adata.raw.X` will be log-transformed (using log1p) before gate application. This transformation is recommended when automatic gate identification through GMM is performed, as it helps in normalizing data distributions.
imageid (str, optional):
The name of the column in `adata` that contains Image IDs. This is necessary for matching manual gates specified in the `gate` DataFrame to their respective images.
failed_markers (dict, optional):
A dictionary mapping `imageid` to markers that failed quality control. This allows for the exclusion of specific markers from the analysis based on prior visual inspection or other criteria. The dictionary can use `all` as a key to specify markers that failed across all images.
method (str, optional):
Specifies the gating strategy: `all` to pool data from all images for GMM application, or `by_image` to apply GMM separately for each image. `all` may introduce batch effects, while `by_image` requires sufficient variation within each image to distinguish negative from positive populations effectively.
random_state (int, optional):
The seed used by the random number generator for GMM. Ensures reproducibility of results.
verbose (bool, optional):
If `True`, detailed progress updates and diagnostic messages will be printed during the function's execution.
Returns:
Modified AnnData Object (AnnData):
Returns the input `adata` object with updated expression data (`adata.X`) after rescaling. The gates applied, either provided manually or determined automatically, are stored within `adata.uns['gates']`.
Example:
```python
# Example with manual gates
manual_gate = pd.DataFrame({'marker': ['CD3D', 'KI67'], 'gate': [7, 8]})
adata = sm.pp.rescale(adata, gate=manual_gate, failed_markers={'all': ['CD20', 'CD21']})
# Importing gates from a CSV
manual_gate = pd.read_csv('manual_gates.csv')
adata = sm.pp.rescale(adata, gate=manual_gate, failed_markers={'all': ['CD20', 'CD21']})
# Running without manual gates to use GMM for automatic gate determination
adata = sm.pp.rescale(adata, gate=None, failed_markers={'all': ['CD20', 'CD21']})
```
"""
#log=True; imageid='imageid'; failed_markers=None; method='all'; random_state=0
# make a copy to raw data if raw is none
if adata.raw is None:
adata.raw = adata
# Mapping between markers and gates in the given dataset
dataset_markers = adata.var.index.tolist()
dataset_images = adata.obs[imageid].unique().tolist()
m= pd.DataFrame(index=dataset_markers, columns=dataset_images).reset_index()
m= pd.melt(m, id_vars=[m.columns[0]])
m.columns = ['markers', 'imageid', 'gate']
# Manipulate m with and without provided manual fates
if gate is None:
gate_mapping = m.copy()
elif bool(set(list(gate.columns)) & set(dataset_images)) is False:
global_manual_m = pd.melt(gate, id_vars=[gate.columns[0]])
global_manual_m.columns = ['markers', 'imageid', 'm_gate']
gate_mapping = m.copy()
gate_mapping.gate = gate_mapping.gate.fillna(gate_mapping.markers.map(dict(zip(global_manual_m.markers, global_manual_m.m_gate))))
else:
manual_m = pd.melt(gate, id_vars=[gate.columns[0]])
manual_m.columns = ['markers', 'imageid', 'm_gate']
gate_mapping = pd.merge(m, manual_m, how='left', left_on=['markers','imageid'], right_on = ['markers','imageid'])
gate_mapping['gate'] = gate_mapping['gate'].fillna(gate_mapping['m_gate'])
gate_mapping = gate_mapping.drop(columns='m_gate')
# Addressing failed markers
def process_failed (adata_subset, foramted_failed_markers):
if verbose:
print('Processing Failed Marker in ' + str(adata_subset.obs[imageid].unique()[0]))
# prepare data
data_subset = pd.DataFrame(adata_subset.raw.X, columns=adata_subset.var.index, index=adata_subset.obs.index)
if log is True:
data_subset = np.log1p(data_subset)
# subset markers in the subset
fm_sub = foramted_failed_markers[adata_subset.obs[imageid].unique()].dropna()
def process_failed_internal (fail_mark, data_subset):
return data_subset[fail_mark].max()
r_process_failed_internal = lambda x: process_failed_internal (fail_mark=x,data_subset=data_subset)
f_g = list(map(r_process_failed_internal, [ x[0] for x in fm_sub.values]))
subset_gate = pd.DataFrame( {'markers': [ x[0] for x in fm_sub.values],
'imageid': adata_subset.obs[imageid].unique()[0],
'gate': f_g,})
# return
return subset_gate
# Identify the failed markers
if failed_markers is not None:
# check if failed marker is a dict
if isinstance(failed_markers, dict) is False:
raise ValueError ('`failed_markers` should be a python dictionary, please refer documentation')
# create a copy
fm = failed_markers.copy()
# seperate all from the rest
if 'all' in failed_markers:
all_failed = failed_markers['all']
if isinstance(all_failed, str):
all_failed = [all_failed]
failed_markers.pop('all', None)
df = pd.DataFrame(columns = adata.obs[imageid].unique())
for i in range(len(all_failed)):
df.loc[i] = np.repeat(all_failed[i], len(df.columns))
#for i in range(len(df.columns)):
# df.loc[i] = all_failed[i]
# rest of the failed markers
#fail = pd.DataFrame.from_dict(failed_markers)
fail = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in failed_markers.items() ]))
# merge
if 'all' in fm:
foramted_failed_markers = pd.concat([fail, df], axis=0)
else:
foramted_failed_markers = fail
# send the adata objects that need to be processed
# Check if any image needs to pass through the GMM protocol
adata_list = [adata[adata.obs[imageid] == i] for i in foramted_failed_markers.columns]
# apply the process_failed function
r_process_failed = lambda x: process_failed (adata_subset=x,foramted_failed_markers=foramted_failed_markers)
failed_gates = list(map(r_process_failed, adata_list))
# combine the results and merge with gate_mapping
result = []
for i in range(len(failed_gates)):
result.append(failed_gates[i])
result = pd.concat(result, join='outer')
# use this to merge with gate_mapping
x1 = gate_mapping.set_index(['markers', 'imageid'])['gate']
x2 = result.set_index(['markers', 'imageid'])['gate']
x1.update(x2)
gate_mapping = x1.reset_index()
# trim the data before applying GMM
def clipping (x):
clip = x.clip(lower =np.percentile(x,0.01), upper=np.percentile(x,99.99)).tolist()
return clip
# Find GMM based gates
def gmm_gating (marker, data):
if verbose:
print('Finding the optimal gate by GMM for ' + str(marker))
data_gm = data[marker].values.reshape(-1, 1)
gmm = GaussianMixture(n_components=2, random_state=random_state).fit(data_gm)
gate = np.mean(gmm.means_)
return gate
# Running gmm_gating on the dataset
def gmm_gating_internal (adata_subset, gate_mapping, method):
if verbose:
print('GMM for ' + str(adata_subset.obs[imageid].unique()))
data_subset = pd.DataFrame(adata_subset.raw.X, columns=adata_subset.var.index, index=adata_subset.obs.index)
# find markers
if method == 'all':
image_specific = gate_mapping.copy()
marker_to_gate = list(gate_mapping[gate_mapping.gate.isnull()].markers.unique())
else:
image_specific = gate_mapping[gate_mapping['imageid'].isin(adata_subset.obs[imageid].unique())]
marker_to_gate = image_specific[image_specific.gate.isnull()].markers.values
# Apply clipping
data_subset_clipped = data_subset.apply(clipping)
# log transform data
if log is True:
data_subset_clipped = np.log1p(data_subset_clipped)
# identify the gates for the markers
r_gmm_gating = lambda x: gmm_gating(marker=x, data=data_subset_clipped)
gates = list(map(r_gmm_gating, marker_to_gate))
# create a df with results
result = image_specific[image_specific.gate.isnull()]
mapping = dict(zip(marker_to_gate, gates))
for i in result.index:
result.loc[i, 'gate'] = mapping[result.loc[i, 'markers']]
#result['gate'] = result['gate'].fillna(result['markers'].map(dict(zip(marker_to_gate, gates))))
# return
return result
# Create a list of image IDs that need to go through the GMM
gmm_images = gate_mapping[gate_mapping.gate.isnull()].imageid.unique()
# Check if any image needs to pass through the GMM protocol
if len(gmm_images) > 0 :
# Create a list of adata that need to go through the GMM
if method == 'all':
adata_list = [adata]
else:
adata_list = [adata[adata.obs[imageid] == i] for i in gmm_images]
# run function
r_gmm_gating_internal = lambda x: gmm_gating_internal (adata_subset=x,
gate_mapping=gate_mapping,
method=method)
all_gates = list(map(r_gmm_gating_internal, adata_list))
# combine the results and merge with gate_mapping
result = []
for i in range(len(all_gates)):
result.append(all_gates[i])
result = pd.concat(result, join='outer')
# use this to merge with gate_mapping
gate_mapping.gate = gate_mapping.gate.fillna(gate_mapping.markers.map(dict(zip(result.markers, result.gate))))
# Rescaling function
def data_scaler (adata_subset, gate_mapping):
if verbose:
print('Scaling Image ' + str(adata_subset.obs[imageid].unique()[0]))
# Organise data
data_subset = pd.DataFrame(adata_subset.raw.X, columns=adata_subset.var.index, index=adata_subset.obs.index)
if log is True:
data_subset = np.log1p(data_subset)
# subset markers in the subset
gate_mapping_sub = gate_mapping[gate_mapping['imageid'] == adata_subset.obs[imageid].unique()[0]]
# organise gates
def data_scaler_internal (marker, gate_mapping_sub):
if verbose:
print('Scaling ' + str(marker))
# find the gate
moi = gate_mapping_sub[gate_mapping_sub.markers == marker]['gate'].values[0]
# Find the closest value to the gate
absolute_val_array = np.abs(data_subset[marker].values - float(moi))
# throw error if the array has nan values
if np.isnan(absolute_val_array).any():
raise ValueError ("An exception occurred: " + str(marker) + ' has nan values')
# smallest diff
smallest_difference_index = absolute_val_array.argmin()
closest_element = data_subset[marker].values[smallest_difference_index]
# rescale the data based on the identified gate
marker_study = data_subset[marker]
marker_study = marker_study.sort_values(axis=0)
# Find the index of the gate
# account for 0
if all(marker_study == 0):
gate_index = pd.DataFrame(marker_study).tail(2).index[0]
else:
gate_index = marker_study.index[marker_study == closest_element][0]
# Split into high and low groups
high = marker_study[gate_index:]
low = marker_study[:gate_index]
# Prepare for scaling the high and low dataframes
scaler_high = MinMaxScaler(feature_range=(0.5, 1))
scaler_low = MinMaxScaler(feature_range=(0, 0.5))
# Scale it
h = pd.DataFrame(scaler_high.fit_transform(high.values.reshape(-1, 1)), index = high.index)
l = pd.DataFrame(scaler_low.fit_transform(low.values.reshape(-1, 1)), index = low.index)
# Merge the high and low and resort it
scaled_data = pd.concat([l,h])
scaled_data = scaled_data.loc[~scaled_data.index.duplicated(keep='first')]
scaled_data = scaled_data.reindex(data_subset.index)
#scaled_data[scaled_data > 0.5].count(axis=1).sum()
# return
return scaled_data
# run internal function
r_data_scaler_internal = lambda x: data_scaler_internal (marker=x, gate_mapping_sub=gate_mapping_sub)
scaled_subset = list(map(r_data_scaler_internal, gate_mapping_sub.markers.values))
# combine the results and merge with gate_mapping
scaled_subset_result = []
for i in range(len(scaled_subset)):
scaled_subset_result.append(scaled_subset[i])
scaled_subset_result = pd.concat(scaled_subset_result, join='outer', axis=1)
scaled_subset_result.columns = gate_mapping_sub.markers.values
#scaled_subset_result[scaled_subset_result['CD3E'] > 0.5]['CD3E'].count(axis=1).sum()
# return
return scaled_subset_result
# pass each dataset seperately
adata_list = [adata[adata.obs[imageid] == i] for i in adata.obs[imageid].unique()]
# Run the scaler function
r_data_scaler = lambda x: data_scaler (adata_subset=x, gate_mapping=gate_mapping)
scaled_subset = list(map(r_data_scaler, adata_list))
# combine the results and merge with gate_mapping
final_result = []
for i in range(len(scaled_subset)):
final_result.append(scaled_subset[i])
final_result = pd.concat(final_result, join='outer')
# reindex the final_results
final_result = final_result.reindex(adata.obs.index)
# save final gates
adata.uns['gates'] = gate_mapping.pivot_table(index=['markers'], columns=['imageid']).droplevel(0, axis=1)#.reset_index()
# add to the anndata
adata.X = final_result
# return adata
return adata
|