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336 | def rescale (adata, gate=None, log=True,
imageid='imageid', failed_markers=None,
method='all',random_state=0):
"""
Parameters:
adata : AnnData Object
gate : dataframe, optional
DataFrame with first column as markers and subsequent column with gate values for each image in the dataset.
The column names should correspond to the unique `imageid`. If only one column of gate is provied
to a dataset with multiple images, the same gate will be applied to all images.
Note: If gates are not provided or left out for a particular marker, the function will try to
automatically identify a gate based on applying gaussian mixture modeling algorithm (GMM). The default is None.
log : bool, optional
By default the data stored in `adata.raw.X` is extracted for scaling. If the user wishes to log transform (log1p)
it before applying the gates, this parameter can be set to True. Please note if the function is used to
identify gates based on GMM, it is recommended for the data to be log transformed. The default is True.
imageid : string, optional
The column containing the Image IDs. When passing manual gates the columns of the dataframe need to match
to the elements within the passed `imageid` column. The default is 'imageid'.
failed_markers : dict, optional
Markers that were deemed to have failed based on prior visual inspection. This parameter accepts a python
dictionary with `key` as `imageid` and `value` as markers that failed in that particular `imageid`.
Example: `failed_markers = {'image_1': ['failed_marker_1'], 'image_2' : ['failed_marker_1', 'failed_marker_2']}`.
To make it easier to allow specifying markers that failed in `all` images within the dataset, the parameter also
recognizes the special keyword `all`. For example, `failed_markers = {'all': ['failed_marker_X'], 'image_2' : ['failed_marker_1', 'failed_marker_2']}`.
The default is None.
method : string, optional
Two avialble option are- 'all' or 'by_image'. In the event that multiple images were loaded in with distinct 'imageid',
users have the option to apply GMM by pooling all data togeather or to apply it to each image independently.
Please be aware of batch effects when passing 'all' to multiple images. In contrast, if there are not enough variation
within individual images, the GMM cannot reliably distinguish between the negative and positive populations as well.
random_state : int, optional
Seed for GMM. The default is 0.
Returns:
Modified AnnData Object
The values in `adata.X` are replaced with the scaled data.
The final gates used for saving the data is also stored in `adata.uns['gates']`
Example:
```python
# create a df 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']})
# you could also import the gates as a pandas dataframe without index
manual_gate = pd.read_csv('manual_gates.csv')
adata = sm.pp.rescale (adata, gate=manual_gate, failed_markers={'all':['CD20', 'CD21']})
# The function can also be run without providing manual gates. This will trigger the GMM mode
adata = sm.pp.rescale (adata, gate=None, failed_markers={'all':['CD20', 'CD21']})
```
"""
# 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 = list(adata.var.index)
dataset_images = list(adata.obs[imageid].unique())
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):
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):
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):
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):
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):
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
|