def getSynapseDetectionSSA(synapticVolumes, query, kernelLength=2, edge_win = 3,
search_win = 2):
"""
This function calls the functions needed to run probabilistic synapse detection
Parameters
----------
synapticVolumes : dict
has two keys (presynaptic,postsynaptic) which contain lists of 3D numpy arrays
query : dict
contains the minumum slice information for each channel
kernelLength : int
Minimum 2D Blob Size (default 2)
edge_win: int
Edge window (default 8)
search_win: int
Search windows (default 2)
Returns
----------
resultVol : 3D numpy array - final probability map
"""
# Data
presynapticVolumes = synapticVolumes['presynaptic']
postsynapticVolumes = synapticVolumes['postsynaptic']
# Number of slices each blob should span
preIF_z = query['preIF_z']
postIF_z = query['postIF_z']
for n in range(0, len(presynapticVolumes)):
#presynapticVolumes[n] = getProbMap(presynapticVolumes[n]) # Step 1
presynapticVolumes[n] = syn.convolveVolume(presynapticVolumes[n], kernelLength) # Step 2
if preIF_z[n] > 1:
factorVol = syn.computeFactor(presynapticVolumes[n], int(preIF_z[n])) # Step 3
presynapticVolumes[n] = presynapticVolumes[n] * factorVol
for n in range(0, len(postsynapticVolumes)):
#postsynapticVolumes[n] = getProbMap(postsynapticVolumes[n]) # Step 1
postsynapticVolumes[n] = syn.convolveVolume(postsynapticVolumes[n], kernelLength) # Step 2
if postIF_z[n] > 1:
factorVol = syn.computeFactor(postsynapticVolumes[n], int(postIF_z[n])) # Step 3
postsynapticVolumes[n] = postsynapticVolumes[n] * factorVol
# combinePrePostVolumes(base, adjacent)
# Step 4
#print(len(presynapticVolumes))
#print(len(postsynapticVolumes))
if len(postsynapticVolumes) == 0:
resultVol = syn.combinePrePostVolumes(presynapticVolumes, postsynapticVolumes, edge_win, search_win)
else:
resultVol = syn.combinePrePostVolumes(postsynapticVolumes, presynapticVolumes, edge_win, search_win)
return resultVol;
def run_ab_analysis_rayleigh(synaptic_volumes, query, thresh, resolution,
target_antibody_name):
"""
Run AB Analysis - special case
MEASURES
- Puncta Density
- Average punctum size
- Standard deviation of the size
- Synapse density
- Target Specificity Ratio (tsr)
Parameters
-----------
synaptic_volumes : dict
query : dict
thresh : float
resolution : dict
Returns
-----------
antibody_measure : AntibodyAnalysis()
"""
antibody_measure = AntibodyAnalysis(query)
# Get data volume
antibody_measure.volume_um3 = getdatavolume(synaptic_volumes, resolution)
print('data volume: ', antibody_measure.volume_um3)
# Check to see if user supplied blobsize
if 'punctumSize' in query.keys():
blobsize = query['punctumSize']
edge_win = int(np.ceil(blobsize * 1.5))
# Data
presynaptic_volumes = synaptic_volumes['presynaptic']
postsynaptic_volumes = synaptic_volumes['postsynaptic']
# Number of slices each blob should span
preIF_z = query['preIF_z']
postIF_z = query['postIF_z']
for n in range(0, len(presynaptic_volumes)):
presynaptic_volumes[n] = syn.getProbMap_rayleigh(
presynaptic_volumes[n]) # Step 1
presynaptic_volumes[n] = syn.convolveVolume(presynaptic_volumes[n],
blobsize) # Step 2
if preIF_z[n] > 1:
factor_vol = syn.computeFactor(presynaptic_volumes[n],
int(preIF_z[n])) # Step 3
presynaptic_volumes[n] = presynaptic_volumes[n] * factor_vol
# Compute single channel measurements
antibody_measure = compute_single_channel_measurements(
presynaptic_volumes, antibody_measure, thresh, 'presynaptic')
print('Computed presynaptic single channel measurements')
for n in range(0, len(postsynaptic_volumes)):
postsynaptic_volumes[n] = syn.getProbMap_rayleigh(
postsynaptic_volumes[n]) # Step 1
postsynaptic_volumes[n] = syn.convolveVolume(postsynaptic_volumes[n],
blobsize) # Step 2
if postIF_z[n] > 1:
factor_vol = syn.computeFactor(postsynaptic_volumes[n],
int(postIF_z[n])) # Step 3
postsynaptic_volumes[n] = postsynaptic_volumes[n] * factor_vol
# Compute single channel measurements
antibody_measure = compute_single_channel_measurements(
postsynaptic_volumes, antibody_measure, thresh, 'postsynaptic')
print('Computed postsynaptic single channel measurements')
if len(postsynaptic_volumes) == 0:
resultVol = syn.combinePrePostVolumes(presynaptic_volumes,
postsynaptic_volumes, edge_win,
blobsize)
else:
resultVol = syn.combinePrePostVolumes(postsynaptic_volumes,
presynaptic_volumes, edge_win,
blobsize)
# Compute whole statistics
label_vol = measure.label(resultVol > thresh)
stats = measure.regionprops(label_vol)
antibody_measure.synapse_density = len(stats) / antibody_measure.volume_um3
antibody_measure.synapse_count = len(stats)
antibody_measure = calculuate_target_ratio(antibody_measure,
target_antibody_name)
return antibody_measure
def run_SACT(synaptic_volumes, query, thresh, resolution,
target_antibody_name):
"""
Run SACT.
MEASURES
- Puncta Density
- Average punctum size
- Standard deviation of the size
- Synapse density
- Target Specificity Ratio (tsr)
- Raw data mean/std
Parameters
-----------
synaptic_volumes : dict - has two keys, 'postsynaptic' and 'presynaptic.' Each key contains a list of volumes.
query : dict
thresh : float
resolution : dict
Returns
-----------
antibody_measure : AntibodyAnalysis()
"""
antibody_measure = AntibodyAnalysis(query)
# Get data volume
antibody_measure.volume_um3 = getdatavolume(synaptic_volumes, resolution)
print('data volume: ', antibody_measure.volume_um3, 'um3')
# Check to see if user supplied blobsize
if 'punctumSize' in query.keys():
blobsize = query['punctumSize']
edge_win = int(np.ceil(blobsize * 1.5))
# Data
presynaptic_volumes = synaptic_volumes['presynaptic']
postsynaptic_volumes = synaptic_volumes['postsynaptic']
# Number of slices each blob should span
preIF_z = query['preIF_z']
postIF_z = query['postIF_z']
# Compute raw mean and standard deviation
antibody_measure = compute_raw_measures(presynaptic_volumes,
antibody_measure, 'presynaptic')
# SNR test
raw_presynaptic_volumes = []
for vol in presynaptic_volumes:
raw_presynaptic_volumes.append(np.copy(vol))
for n in range(0, len(presynaptic_volumes)):
presynaptic_volumes[n] = syn.getProbMap(
presynaptic_volumes[n]) # Step 1
presynaptic_volumes[n] = syn.convolveVolume(presynaptic_volumes[n],
blobsize) # Step 2
if preIF_z[n] > 1:
factor_vol = syn.computeFactor(presynaptic_volumes[n],
int(preIF_z[n])) # Step 3
presynaptic_volumes[n] = presynaptic_volumes[n] * factor_vol
# Compute single channel measurements
antibody_measure = compute_single_channel_measurements(
presynaptic_volumes, antibody_measure, thresh, 'presynaptic')
# SNR test
antibody_measure = compute_SNR_synapticside(raw_presynaptic_volumes,
presynaptic_volumes, thresh,
antibody_measure,
'presynaptic')
print('Computed presynaptic single channel measurements')
# Compute raw mean and standard deviation
antibody_measure = compute_raw_measures(postsynaptic_volumes,
antibody_measure, 'postsynaptic')
# SNR test
raw_postsynaptic_volumes = []
for vol in postsynaptic_volumes:
raw_postsynaptic_volumes.append(np.copy(vol))
for n in range(0, len(postsynaptic_volumes)):
postsynaptic_volumes[n] = syn.getProbMap(
postsynaptic_volumes[n]) # Step 1
postsynaptic_volumes[n] = syn.convolveVolume(postsynaptic_volumes[n],
blobsize) # Step 2
if postIF_z[n] > 1:
factor_vol = syn.computeFactor(postsynaptic_volumes[n],
int(postIF_z[n])) # Step 3
postsynaptic_volumes[n] = postsynaptic_volumes[n] * factor_vol
# Compute single channel measurements
antibody_measure = compute_single_channel_measurements(
postsynaptic_volumes, antibody_measure, thresh, 'postsynaptic')
# SNR test
antibody_measure = compute_SNR_synapticside(raw_postsynaptic_volumes,
postsynaptic_volumes, thresh,
antibody_measure,
'postsynaptic')
print('Computed postsynaptic single channel measurements')
#"""
if len(postsynaptic_volumes) == 0:
resultVol = syn.combinePrePostVolumes(presynaptic_volumes,
postsynaptic_volumes, edge_win,
blobsize)
else:
resultVol = syn.combinePrePostVolumes(postsynaptic_volumes,
presynaptic_volumes, edge_win,
blobsize)
# Compute whole statistics
label_vol = measure.label(resultVol > thresh)
stats = measure.regionprops(label_vol)
antibody_measure.synapse_density = len(stats) / antibody_measure.volume_um3
antibody_measure.synapse_count = len(stats)
antibody_measure = calculuate_target_ratio(antibody_measure,
target_antibody_name)
#"""
return antibody_measure