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Make matching to an external library optional #297

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301 changes: 169 additions & 132 deletions active_plugins/callbarcodes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,13 +99,13 @@
# The number of settings per metric
METRIC_SETTING_COUNT = 3

FIXED_SETTING_COUNT = 14
FIXED_SETTING_COUNT = 15

class CallBarcodes(cellprofiler_core.module.Module):

module_name = "CallBarcodes"
category = "Data Tools"
variable_revision_number = 2
variable_revision_number = 3

def create_settings(self):
self.csv_directory = cellprofiler_core.setting.text.Directory(
Expand Down Expand Up @@ -214,6 +214,16 @@ def set_directory_fn(path):
Enter the name to be given to the barcode score image.""",
)

self.do_library_match = cellprofiler_core.setting.Binary(
"Do you want to match to an external library of barcodes?",
True,
doc="""\
Select "*{YES}*" to match the result read by CellProfiler to a provided barcode list.
Select "*{NO}*") to simply call bases, and do the matching downstream.""".format(
**{"YES": "Yes", "NO":"No"}
),
)

self.has_empty_vector_barcode = cellprofiler_core.setting.Binary(
"Do you have an empty vector barcode you would like to add to the barcode list?",
False,
Expand Down Expand Up @@ -344,6 +354,7 @@ def settings(self):
self.has_empty_vector_barcode,
self.empty_vector_barcode_sequence,
self.n_colors,
self.do_library_match,
]

for eachbase in self.base_measurements.values():
Expand Down Expand Up @@ -376,24 +387,30 @@ def visible_settings(self):
if base_meas.removable:
result += [base_meas.remover]
result += add_buttons[base]

if self.do_library_match:
result += [
self.csv_directory,
self.csv_file_name,
self.metadata_field_barcode,
self.metadata_field_tag,
self.has_empty_vector_barcode
]

if self.has_empty_vector_barcode:
result += [self.empty_vector_barcode_sequence]

result += [
self.csv_directory,
self.csv_file_name,
self.metadata_field_barcode,
self.metadata_field_tag,
self.wants_call_image,
self.wants_score_image,
]

if self.wants_call_image:
result += [self.outimage_calls_name]
result += [
self.wants_call_image,
self.wants_score_image,
]

if self.wants_score_image:
result += [self.outimage_score_name]
if self.wants_call_image:
result += [self.outimage_calls_name]

if self.has_empty_vector_barcode:
result += [self.empty_vector_barcode_sequence]
if self.wants_score_image:
result += [self.outimage_score_name]

return result

Expand Down Expand Up @@ -554,89 +571,97 @@ def run(self, workspace):
"_".join([C_CALL_BARCODES, "BarcodeCalled"]),
calledbarcodes,
)
if self.do_library_match.value:
barcodes = self.barcodeset(
self.metadata_field_barcode.value, self.metadata_field_tag.value
)

barcodes = self.barcodeset(
self.metadata_field_barcode.value, self.metadata_field_tag.value
)
cropped_barcode_dict = {
y[: self.ncycles.value]: y for y in list(barcodes.keys())
}

cropped_barcode_dict = {
y[: self.ncycles.value]: y for y in list(barcodes.keys())
}

scorelist = []
matchedbarcode = []
matchedbarcodecode = []
matchedbarcodeid = []
if self.wants_call_image or self.wants_score_image:
objects = workspace.object_set.get_objects(self.input_object_name.value)
labels = objects.segmented
pixel_data_call = objects.segmented
pixel_data_score = objects.segmented
count = 1
for eachbarcode in calledbarcodes:
eachscore, eachmatch = self.queryall(cropped_barcode_dict, eachbarcode)
scorelist.append(eachscore)
matchedbarcode.append(eachmatch)
m_id, m_code = barcodes[eachmatch]
matchedbarcodeid.append(m_id)
matchedbarcodecode.append(m_code)
if self.wants_call_image:
pixel_data_call = numpy.where(
labels == count, barcodes[eachmatch][0], pixel_data_call
)
if self.wants_score_image:
pixel_data_score = numpy.where(
labels == count, 65535 * eachscore, pixel_data_score
)
count += 1
scorelist = []
matchedbarcode = []
matchedbarcodecode = []
matchedbarcodeid = []
if self.wants_call_image or self.wants_score_image:
objects = workspace.object_set.get_objects(self.input_object_name.value)
labels = objects.segmented
pixel_data_call = objects.segmented
pixel_data_score = objects.segmented
count = 1
for eachbarcode in calledbarcodes:
eachscore, eachmatch = self.queryall(cropped_barcode_dict, eachbarcode)
scorelist.append(eachscore)
matchedbarcode.append(eachmatch)
m_id, m_code = barcodes[eachmatch]
matchedbarcodeid.append(m_id)
matchedbarcodecode.append(m_code)
if self.wants_call_image:
pixel_data_call = numpy.where(
labels == count, barcodes[eachmatch][0], pixel_data_call
)
if self.wants_score_image:
pixel_data_score = numpy.where(
labels == count, 65535 * eachscore, pixel_data_score
)
count += 1

imagemeanscore = numpy.mean(scorelist)
imagemeanscore = numpy.mean(scorelist)

workspace.measurements.add_measurement(
"Image", "_".join([C_CALL_BARCODES, "MeanBarcodeScore"]), imagemeanscore
)
workspace.measurements.add_measurement(
"Image", "_".join([C_CALL_BARCODES, "MeanBarcodeScore"]), imagemeanscore
)

workspace.measurements.add_measurement(
self.input_object_name.value,
"_".join([C_CALL_BARCODES, "MatchedTo_Barcode"]),
matchedbarcode,
)
workspace.measurements.add_measurement(
self.input_object_name.value,
"_".join([C_CALL_BARCODES, "MatchedTo_ID"]),
matchedbarcodeid,
)
workspace.measurements.add_measurement(
self.input_object_name.value,
"_".join([C_CALL_BARCODES, "MatchedTo_GeneCode"]),
matchedbarcodecode,
)
workspace.measurements.add_measurement(
self.input_object_name.value,
"_".join([C_CALL_BARCODES, "MatchedTo_Score"]),
scorelist,
)
if self.wants_call_image:
workspace.image_set.add(
self.outimage_calls_name.value,
cellprofiler_core.image.Image(
pixel_data_call.astype("uint16"), convert=False
),
workspace.measurements.add_measurement(
self.input_object_name.value,
"_".join([C_CALL_BARCODES, "MatchedTo_Barcode"]),
matchedbarcode,
)
if self.wants_score_image:
workspace.image_set.add(
self.outimage_score_name.value,
cellprofiler_core.image.Image(
pixel_data_score.astype("uint16"), convert=False
),
workspace.measurements.add_measurement(
self.input_object_name.value,
"_".join([C_CALL_BARCODES, "MatchedTo_ID"]),
matchedbarcodeid,
)

if self.show_window:
workspace.display_data.col_labels = (
"Image Mean Score",
"Image Mean Quality Score",
workspace.measurements.add_measurement(
self.input_object_name.value,
"_".join([C_CALL_BARCODES, "MatchedTo_GeneCode"]),
matchedbarcodecode,
)
workspace.measurements.add_measurement(
self.input_object_name.value,
"_".join([C_CALL_BARCODES, "MatchedTo_Score"]),
scorelist,
)
workspace.display_data.statistics = [imagemeanscore, imagemeanquality]
if self.wants_call_image:
workspace.image_set.add(
self.outimage_calls_name.value,
cellprofiler_core.image.Image(
pixel_data_call.astype("uint16"), convert=False
),
)
if self.wants_score_image:
workspace.image_set.add(
self.outimage_score_name.value,
cellprofiler_core.image.Image(
pixel_data_score.astype("uint16"), convert=False
),
)

if self.show_window:
workspace.display_data.col_labels = (
"Image Mean Score",
"Image Mean Quality Score",
)
workspace.display_data.statistics = [imagemeanscore, imagemeanquality]

else:
if self.show_window:
workspace.display_data.col_labels = (
"Number barcodes",
"Number of unique calls",
)
workspace.display_data.statistics = [len(calledbarcodes), len(list(set([calledbarcodes])))]

def display(self, workspace, figure):
statistics = workspace.display_data.statistics
Expand Down Expand Up @@ -787,46 +812,52 @@ def get_measurement_columns(self, pipeline):

input_object_name = self.input_object_name.value

result = [
(
"Image",
"_".join([C_CALL_BARCODES, "MeanBarcodeScore"]),
cellprofiler_core.constants.measurement.COLTYPE_FLOAT,
),
(
"Image",
"_".join([C_CALL_BARCODES, "MeanQualityScore"]),
cellprofiler_core.constants.measurement.COLTYPE_FLOAT,
),
]


result += [
result = [
(
input_object_name,
"_".join([C_CALL_BARCODES, "BarcodeCalled"]),
cellprofiler_core.constants.measurement.COLTYPE_VARCHAR,
),
(
input_object_name,
"_".join([C_CALL_BARCODES, "MatchedTo_Barcode"]),
cellprofiler_core.constants.measurement.COLTYPE_VARCHAR,
),
(
input_object_name,
"_".join([C_CALL_BARCODES, "MatchedTo_ID"]),
cellprofiler_core.constants.measurement.COLTYPE_INTEGER,
),
(
input_object_name,
"_".join([C_CALL_BARCODES, "MatchedTo_GeneCode"]),
cellprofiler_core.constants.measurement.COLTYPE_VARCHAR,
),
(
input_object_name,
"_".join([C_CALL_BARCODES, "MatchedTo_Score"]),
cellprofiler_core.constants.measurement.COLTYPE_FLOAT,
),
]
if self.do_library_match.value:
result += [
(
"Image",
"_".join([C_CALL_BARCODES, "MeanBarcodeScore"]),
cellprofiler_core.constants.measurement.COLTYPE_FLOAT,
),
(
"Image",
"_".join([C_CALL_BARCODES, "MeanQualityScore"]),
cellprofiler_core.constants.measurement.COLTYPE_FLOAT,
),
]

result += [
(
input_object_name,
"_".join([C_CALL_BARCODES, "MatchedTo_Barcode"]),
cellprofiler_core.constants.measurement.COLTYPE_VARCHAR,
),
(
input_object_name,
"_".join([C_CALL_BARCODES, "MatchedTo_ID"]),
cellprofiler_core.constants.measurement.COLTYPE_INTEGER,
),
(
input_object_name,
"_".join([C_CALL_BARCODES, "MatchedTo_GeneCode"]),
cellprofiler_core.constants.measurement.COLTYPE_VARCHAR,
),
(
input_object_name,
"_".join([C_CALL_BARCODES, "MatchedTo_Score"]),
cellprofiler_core.constants.measurement.COLTYPE_FLOAT,
),
]

if self.n_colors.value == ENCODING_TYPES[0]:
result += [
(
Expand All @@ -848,21 +879,25 @@ def get_measurements(self, pipeline, object_name, category):
if object_name == self.input_object_name and category == C_CALL_BARCODES:
result = [
"BarcodeCalled",
"MatchedTo_Barcode",
"MatchedTo_ID",
"MatchedTo_GeneCode",
"MatchedTo_Score",
]
if self.do_library_match.value:
result += [
"MatchedTo_Barcode",
"MatchedTo_ID",
"MatchedTo_GeneCode",
"MatchedTo_Score",
]
if self.n_colors.value == ENCODING_TYPES[0]:
result.append("MeanQualityScore")

return result

elif object_name == object_name == "Image":
return [
"MeanBarcodeScore",
"MeanQualityScore",
]
if self.do_library_match.value:
return [
"MeanBarcodeScore",
"MeanQualityScore",
]

return []

Expand All @@ -874,4 +909,6 @@ def upgrade_settings(self, setting_values, variable_revision_number, module_name
setting_values += ["G", "AreaShape_Area", True] #G
setting_values += ["T", "AreaShape_Area", True] #T
variable_revision_number = 2
if variable_revision_number == 2:
setting_values = setting_values[:14]+["True"]+setting_values[14:]
return setting_values, variable_revision_number