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Unify cochleae dictionary for subtypes
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-116
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6 files changed

+72
-116
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‎flamingo_tools/segmentation/sgn_subtype_utils.py‎

Lines changed: 17 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,21 @@
11
import os
22

3+
COCHLEAE = {
4+
"M_LR_000098_L": {"seg_data": "SGN_v2", "subtype_stains": ["CR", "Ntng1"], "intensity": "ratio",
5+
"component_list": [1, 2]},
6+
"M_LR_000099_L": {"seg_data": "PV_SGN_v2", "subtype_stains": ["Calb1", "Lypd1"], "intensity": "ratio"},
7+
"M_LR_000184_L": {"seg_data": "SGN_v2", "subtype_stains": ["Prph"], "output_seg": "SGN_v2b", "intensity": "ratio"},
8+
"M_LR_000184_R": {"seg_data": "SGN_v2", "subtype_stains": ["Prph"], "output_seg": "SGN_v2b", "intensity": "ratio"},
9+
"M_LR_000260_L": {"seg_data": "SGN_v2", "subtype_stains": ["Prph"], "intensity": "ratio"},
10+
"M_LR_N110_L": {"seg_data": "SGN_v2", "subtype_stains": ["Calb1", "Ntng1"], "intensity": "ratio"},
11+
"M_LR_N110_R": {"seg_data": "SGN_v2", "subtype_stains": ["Calb1", "Ntng1"], "intensity": "ratio"},
12+
"M_LR_N152_L": {"seg_data": "SGN_v2", "subtype_stains": ["CR", "Ntng1"], "intensity": "ratio",
13+
"component_list": [1, 2]},
14+
"M_AMD_N180_L": {"seg_data": "SGN_merged", "subtype_stains": ["CR", "Lypd1", "Ntng1"], "intensity": "absolute",
15+
"label_stains": {"subtype_label": ["CR", "Ntng1"], "subtype_label_Lypd1": ["CR", "Lypd1"]}},
16+
"M_AMD_N180_R": {"seg_data": "SGN_merged", "subtype_stains": ["CR", "Ntng1"], "intensity": "absolute"},
17+
}
18+
319
CUSTOM_THRESHOLDS = {
420
"M_LR_000098_L": {"Ntng1": {
521
"0492-0500-0581": {"manual": 2, "annotations": 1.41},
@@ -63,7 +79,7 @@
6379
"Prph+/Tuj1+": "Type II",
6480
"Prph+/Tuj1-": "Type II",
6581
"Prph-/Tuj1+": "Type I",
66-
"Prph-/Tuj1-": "inconclusive",
82+
"Prph-/Tuj1-": "Type I",
6783

6884
# Prph is isolated.
6985
"Prph+": "Type II",

‎scripts/assign_subtypes.py‎

Lines changed: 26 additions & 37 deletions
Original file line numberDiff line numberDiff line change
@@ -4,22 +4,9 @@
44
import pandas as pd
55

66
from flamingo_tools.s3_utils import get_s3_path, BUCKET_NAME, SERVICE_ENDPOINT
7-
from flamingo_tools.segmentation.sgn_subtype_utils import STAIN_TO_TYPE
7+
from flamingo_tools.segmentation.sgn_subtype_utils import STAIN_TO_TYPE, COCHLEAE
88
# from skimage.segmentation import relabel_sequential
99

10-
COCHLEA_DICT = {
11-
"M_LR_000098_L": {"seg_data": "SGN_v2", "subtype": ["CR", "Ntng1"]},
12-
"M_LR_000099_L": {"seg_data": "PV_SGN_v2", "subtype": ["Calb1", "Lypd1"]},
13-
"M_LR_000184_L": {"seg_data": "SGN_v2b", "subtype": ["Prph"]},
14-
"M_LR_000184_R": {"seg_data": "SGN_v2b", "subtype": ["Prph"]},
15-
"M_LR_000260_L": {"seg_data": "SGN_v2", "subtype": ["Prph", "Tuj1"]},
16-
"M_LR_N110_L": {"seg_data": "SGN_v2", "subtype": ["Calb1", "Ntng1"]},
17-
"M_LR_N110_R": {"seg_data": "SGN_v2", "subtype": ["Calb1", "Ntng1"]},
18-
"M_LR_N152_L": {"seg_data": "SGN_v2", "subtype": ["CR", "Ntng1"]},
19-
"M_AMD_N180_L": {"seg_data": "SGN_merged", "subtype": ["CR", "Lypd1"]},
20-
"M_AMD_N180_R": {"seg_data": "SGN_merged", "subtype": ["CR", "Ntng1"]},
21-
}
22-
2310

2411
def types_for_stain(stains):
2512
stains.sort()
@@ -93,45 +80,47 @@ def filter_subtypes(cochlea, seg_name, subtype, stains=None):
9380
return label_ids_subtype
9481

9582

96-
def export_lower_resolution(cochlea, output_folder, subtype_column="subtype_label"):
83+
def assign_subtypes(cochlea, output_folder, subtype_column="subtype_label"):
84+
if "label_stains" in COCHLEAE[cochlea].keys():
85+
for subtype_column, subtype_stains in COCHLEAE[cochlea]["label_stains"].items():
9786

98-
subtype_stains = COCHLEA_DICT[cochlea]["subtype"]
99-
subtype_stains.sort()
100-
seg_name = COCHLEA_DICT[cochlea]["seg_data"]
87+
subtype_stains.sort()
88+
if "output_seg" in list(COCHLEAE[cochlea].keys()):
89+
seg_name = COCHLEAE[cochlea]["output_seg"]
90+
else:
91+
seg_name = COCHLEAE[cochlea]["seg_data"]
10192

102-
out_path = os.path.join(output_folder, f"{cochlea}_subtypes.tsv")
93+
out_path = os.path.join(output_folder, f"{cochlea}_subtypes.tsv")
10394

104-
table_seg_path = f"{cochlea}/tables/{seg_name}/default.tsv"
105-
table_path_s3, fs = get_s3_path(table_seg_path)
106-
with fs.open(table_path_s3, "r") as f:
107-
table = pd.read_csv(f, sep="\t")
95+
table_seg_path = f"{cochlea}/tables/{seg_name}/default.tsv"
96+
table_path_s3, fs = get_s3_path(table_seg_path)
97+
with fs.open(table_path_s3, "r") as f:
98+
table = pd.read_csv(f, sep="\t")
10899

109-
print(f"Subtype stains: {subtype_stains}.")
110-
subtypes = types_for_stain(subtype_stains)
111-
subtypes.sort()
100+
print(f"Subtype stains: {subtype_stains}.")
101+
subtypes = types_for_stain(subtype_stains)
102+
subtypes.sort()
112103

113-
# Subtype labels
114-
subtype_labels = ["None" for _ in range(len(table))]
115-
table[subtype_column] = subtype_labels
116-
for subtype in subtypes:
104+
# Subtype labels
105+
subtype_labels = ["None" for _ in range(len(table))]
106+
table[subtype_column] = subtype_labels
107+
for subtype in subtypes:
117108

118-
label_ids_subtype = filter_subtypes(cochlea, seg_name=seg_name, subtype=subtype, stains=subtype_stains)
119-
print(f"Subtype '{subtype}' with {len(label_ids_subtype)} instances.")
120-
table.loc[table["label_id"].isin(label_ids_subtype), subtype_column] = subtype
109+
label_ids_subtype = filter_subtypes(cochlea, seg_name=seg_name, subtype=subtype, stains=subtype_stains)
110+
print(f"Subtype '{subtype}' with {len(label_ids_subtype)} instances.")
111+
table.loc[table["label_id"].isin(label_ids_subtype), subtype_column] = subtype
121112

122-
table.to_csv(out_path, sep="\t", index=False)
113+
table.to_csv(out_path, sep="\t", index=False)
123114

124115

125116
def main():
126117
parser = argparse.ArgumentParser()
127118
parser.add_argument("-c", "--cochlea", type=str, nargs="+", required=True, help="Cochlea(e) to process.")
128119
parser.add_argument("-o", "--output_folder", required=True)
129-
parser.add_argument("-s", "--subtype_column", default="subtype_label",
130-
help="Custom name for column in output which features subtypes.")
131120
args = parser.parse_args()
132121

133122
for cochlea in args.cochlea:
134-
export_lower_resolution(cochlea, args.output_folder, args.subtype_column)
123+
assign_subtypes(cochlea, args.output_folder)
135124

136125

137126
if __name__ == "__main__":

‎scripts/export_lower_resolution_subtypes.py‎

Lines changed: 7 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -7,17 +7,9 @@
77
import zarr
88

99
from flamingo_tools.s3_utils import get_s3_path, BUCKET_NAME, SERVICE_ENDPOINT
10-
from flamingo_tools.segmentation.sgn_subtype_utils import STAIN_TO_TYPE
10+
from flamingo_tools.segmentation.sgn_subtype_utils import STAIN_TO_TYPE, COCHLEAE
1111
# from skimage.segmentation import relabel_sequential
1212

13-
COCHLEA_DICT = {
14-
"M_LR_000099_L": {"seg_data": "PV_SGN_v2", "subtype": ["Calb1", "Lypd1"]},
15-
"M_LR_000184_L": {"seg_data": "SGN_v2b", "subtype": ["Prph"]},
16-
"M_LR_000184_R": {"seg_data": "SGN_v2b", "subtype": ["Prph"]},
17-
"M_LR_000260_L": {"seg_data": "SGN_v2", "subtype": ["Prph", "Tuj1"]},
18-
"M_LR_N152_L": {"seg_data": "SGN_v2", "subtype": ["CR", "Ntng1"]},
19-
}
20-
2113

2214
def types_for_stain(stains):
2315
stains.sort()
@@ -124,10 +116,13 @@ def export_lower_resolution(args):
124116
for scale in args.scale:
125117
output_folder = os.path.join(args.output_folder, cochlea, f"scale{scale}")
126118
os.makedirs(output_folder, exist_ok=True)
127-
if cochlea in COCHLEA_DICT.keys():
119+
if cochlea in COCHLEAE.keys():
128120
if subtype_stains is None:
129-
subtype_stains = COCHLEA_DICT[cochlea]["subtype"]
130-
seg_name = COCHLEA_DICT[cochlea]["seg_data"]
121+
subtype_stains = COCHLEAE[cochlea]["subtype_stains"]
122+
if "output_seg" in list(COCHLEAE[cochlea].keys()):
123+
seg_name = COCHLEAE[cochlea]["output_seg"]
124+
else:
125+
seg_name = COCHLEAE[cochlea]["seg_data"]
131126
else:
132127
raise ValueError(f"Cochlea {cochlea} is not in the dictionary. Check values.")
133128

‎scripts/figures/plot_SGNsub_thresholds.py‎

Lines changed: 1 addition & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -8,24 +8,10 @@
88
import pandas as pd
99

1010
from flamingo_tools.s3_utils import get_s3_path
11-
from flamingo_tools.segmentation.sgn_subtype_utils import CUSTOM_THRESHOLDS
11+
from flamingo_tools.segmentation.sgn_subtype_utils import CUSTOM_THRESHOLDS, COCHLEAE
1212

1313
png_dpi = 300
1414

15-
COCHLEAE = {
16-
"M_LR_000098_L": {"seg_data": "SGN_v2", "subtype": ["CR", "Ntng1"], "intensity": "ratio"},
17-
"M_LR_000099_L": {"seg_data": "PV_SGN_v2", "subtype": ["Calb1", "Lypd1"], "intensity": "ratio"},
18-
"M_LR_000184_L": {"seg_data": "SGN_v2", "subtype": ["Prph"], "output_seg": "SGN_v2b", "intensity": "ratio"},
19-
"M_LR_000184_R": {"seg_data": "SGN_v2", "subtype": ["Prph"], "output_seg": "SGN_v2b", "intensity": "ratio"},
20-
"M_LR_000214_L": {"seg_data": "PV_SGN_v2", "subtype": ["Calb1"], "intensity": "ratio"},
21-
"M_LR_000260_L": {"seg_data": "SGN_v2", "subtype": ["Prph", "Tuj1"], "intensity": "ratio"},
22-
"M_LR_N110_L": {"seg_data": "SGN_v2", "subtype": ["Calb1", "Ntng1"], "intensity": "ratio"},
23-
"M_LR_N110_R": {"seg_data": "SGN_v2", "subtype": ["Calb1", "Ntng1"], "intensity": "ratio"},
24-
"M_LR_N152_L": {"seg_data": "SGN_v2", "subtype": ["CR", "Ntng1"], "intensity": "ratio"},
25-
"M_AMD_N180_L": {"seg_data": "SGN_merged", "subtype": ["CR", "Lypd1", "Ntng1"], "intensity": "absolute"},
26-
"M_AMD_N180_R": {"seg_data": "SGN_merged", "subtype": ["CR", "Ntng1"], "intensity": "absolute"},
27-
}
28-
2915

3016
def plot_intensity_thresholds(input_dir, output_dir, cochlea, plot=False, sharex=True):
3117
"""Plot histograms for positive and negative populations of subtype markers based on thresholding.

‎scripts/measurements/evaluate_marker_annotations_subtype.py‎

Lines changed: 19 additions & 35 deletions
Original file line numberDiff line numberDiff line change
@@ -8,24 +8,9 @@
88
from flamingo_tools.s3_utils import get_s3_path
99
from flamingo_tools.file_utils import read_image_data
1010
from flamingo_tools.segmentation.chreef_utils import localize_median_intensities, find_annotations
11-
from flamingo_tools.segmentation.sgn_subtype_utils import CUSTOM_THRESHOLDS
11+
from flamingo_tools.segmentation.sgn_subtype_utils import CUSTOM_THRESHOLDS, COCHLEAE
1212

1313
MARKER_DIR_SUBTYPE = "/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/SGN_subtypes"
14-
# The cochlea for the CHReef analysis.
15-
16-
COCHLEAE = {
17-
"M_LR_000098_L": {"seg_data": "SGN_v2", "subtype": ["CR", "Ntng1"], "intensity": "ratio"},
18-
"M_LR_000099_L": {"seg_data": "PV_SGN_v2", "subtype": ["Calb1", "Lypd1"], "intensity": "ratio"},
19-
"M_LR_000184_L": {"seg_data": "SGN_v2", "subtype": ["Prph"], "output_seg": "SGN_v2b", "intensity": "ratio"},
20-
"M_LR_000184_R": {"seg_data": "SGN_v2", "subtype": ["Prph"], "output_seg": "SGN_v2b", "intensity": "ratio"},
21-
"M_LR_000214_L": {"seg_data": "PV_SGN_v2", "subtype": ["Calb1"], "intensity": "ratio"},
22-
"M_LR_000260_L": {"seg_data": "SGN_v2", "subtype": ["Prph", "Tuj1"], "intensity": "ratio"},
23-
"M_LR_N110_L": {"seg_data": "SGN_v2", "subtype": ["Calb1", "Ntng1"], "intensity": "ratio"},
24-
"M_LR_N110_R": {"seg_data": "SGN_v2", "subtype": ["Calb1", "Ntng1"], "intensity": "ratio"},
25-
"M_LR_N152_L": {"seg_data": "SGN_v2", "subtype": ["CR", "Ntng1"], "intensity": "ratio"},
26-
"M_AMD_N180_L": {"seg_data": "SGN_merged", "subtype": ["CR", "Lypd1", "Ntng1"], "intensity": "absolute"},
27-
"M_AMD_N180_R": {"seg_data": "SGN_merged", "subtype": ["CR", "Ntng1"], "intensity": "absolute"},
28-
}
2914

3015

3116
def get_length_fraction_from_center(table, center_str):
@@ -160,7 +145,7 @@ def get_annotation_table(annotation_dics, subtype):
160145
annotator = annotator_dir.split("_")[1]
161146
for center_str in annotation_dic["center_strings"]:
162147
row = {"annotator" : annotator}
163-
row["subtype"] = subtype
148+
row["subtype_stains"] = subtype
164149
row["center_str"] = center_str
165150
row["median_intensity"] = annotation_dic[center_str]["median_intensity"]
166151
row["inbetween_ids"] = len(annotation_dic[center_str]["inbetween_ids"])
@@ -179,13 +164,13 @@ def get_annotation_table(annotation_dics, subtype):
179164
return df
180165

181166

182-
def get_object_measures(annotation_dics, intensity_dic, intensity_mode, subtype):
167+
def get_object_measures(annotation_dics, intensity_dic, intensity_mode, subtype_stain):
183168
"""Get information to create table containing object measure information.
184169
"""
185170
om_dic = {}
186171
center_strings = list(intensity_dic.keys())
187172
om_dic["center_strings"] = center_strings
188-
om_dic["subtype"] = subtype
173+
om_dic["subtype_stains"] = subtype_stain
189174
om_dic["intensity_mode"] = intensity_mode
190175
for center_str in center_strings:
191176
crop_dic = {}
@@ -234,8 +219,8 @@ def evaluate_marker_annotation(
234219

235220
seg_string = "-".join(output_seg.split("_"))
236221
cochlea_str = "-".join(cochlea.split("_"))
237-
subtypes = COCHLEAE[cochlea]["subtype"]
238-
subtype_str = "_".join(subtypes)
222+
stains = COCHLEAE[cochlea]["subtype_stain"]
223+
subtype_str = "_".join(stains)
239224
out_path = os.path.join(output_dir, f"{cochlea_str}_{subtype_str}_{seg_string}.tsv")
240225
annot_out = os.path.join(output_dir, f"{cochlea_str}_{subtype_str}_{seg_string}_annotations.tsv")
241226
if os.path.exists(out_path) and os.path.exists(annot_out) and not force:
@@ -256,13 +241,12 @@ def evaluate_marker_annotation(
256241

257242
# iterate through subtypes
258243
annot_table = None
259-
for subtype in subtypes:
260-
pattern = subtype
244+
for stain in stains:
261245
if intensity_mode == "ratio":
262246
table_measurement_path = f"{cochlea}/tables/{data_name}/subtype_ratio.tsv"
263-
column = f"{subtype}_ratio_PV"
247+
column = f"{stain}_ratio_PV"
264248
elif intensity_mode == "absolute":
265-
table_measurement_path = f"{cochlea}/tables/{data_name}/{subtype}_{seg_string}_object-measures.tsv"
249+
table_measurement_path = f"{cochlea}/tables/{data_name}/{stain}_{seg_string}_object-measures.tsv"
266250
column = "median"
267251
else:
268252
raise ValueError("Choose either 'ratio' or 'median' as intensity mode.")
@@ -272,27 +256,27 @@ def evaluate_marker_annotation(
272256
table_measurement = pd.read_csv(f, sep="\t")
273257

274258
cochlea_annotations = [a for a in annotation_dirs
275-
if len(find_annotations(a, cochlea, subtype)["center_strings"]) != 0]
259+
if len(find_annotations(a, cochlea, stain)["center_strings"]) != 0]
276260
print(f"Evaluating data for cochlea {cochlea} in {cochlea_annotations}.")
277261

278262
# Find the thresholds from the annotated blocks and save them if specified.
279263
intensity_dic, annot_dic = find_thresholds(cochlea_annotations, cochlea, data_seg,
280-
table_measurement, column=column, pattern=pattern)
264+
table_measurement, column=column, pattern=stain)
281265

282266
if annot_table is None:
283-
annot_table = get_annotation_table(annot_dic, subtype)
267+
annot_table = get_annotation_table(annot_dic, stain)
284268
else:
285-
annot_table = pd.concat([annot_table, get_annotation_table(annot_dic, subtype)], ignore_index=True)
269+
annot_table = pd.concat([annot_table, get_annotation_table(annot_dic, stain)], ignore_index=True)
286270

287271
# create dictionary containing median intensity and segmentation ids for every crop
288-
om_dic = get_object_measures(annot_dic, intensity_dic, intensity_mode, subtype)
289-
om_out_path = os.path.join(output_dir, f"{cochlea_str}_{subtype}_om.json")
272+
om_dic = get_object_measures(annot_dic, intensity_dic, intensity_mode, stain)
273+
om_out_path = os.path.join(output_dir, f"{cochlea_str}_{stain}_om.json")
290274
with open(om_out_path, "w") as f:
291275
json.dump(om_dic, f, sort_keys=True, indent=4)
292276

293277
if threshold_save_dir is not None:
294278
os.makedirs(threshold_save_dir, exist_ok=True)
295-
threshold_out_path = os.path.join(threshold_save_dir, f"{cochlea_str}_{subtype}_{seg_string}.json")
279+
threshold_out_path = os.path.join(threshold_save_dir, f"{cochlea_str}_{stain}_{seg_string}.json")
296280
with open(threshold_out_path, "w") as f:
297281
json.dump(intensity_dic, f, sort_keys=True, indent=4)
298282

@@ -305,13 +289,13 @@ def evaluate_marker_annotation(
305289
table_measurement = pd.read_csv(f, sep="\t")
306290

307291
# Apply the threshold to all SGNs.
308-
if CUSTOM_THRESHOLDS.get(cochlea, {}).get(subtype) is not None:
309-
custom_threshold_dic = CUSTOM_THRESHOLDS[cochlea][subtype]
292+
if CUSTOM_THRESHOLDS.get(cochlea, {}).get(stain) is not None:
293+
custom_threshold_dic = CUSTOM_THRESHOLDS[cochlea][stain]
310294
else:
311295
custom_threshold_dic = None
312296

313297
table_seg = apply_nearest_threshold(
314-
intensity_dic, table_seg, table_measurement, column=column, suffix=subtype,
298+
intensity_dic, table_seg, table_measurement, column=column, suffix=stain,
315299
threshold_dic=custom_threshold_dic,
316300
)
317301

‎scripts/measurements/sgn_subtypes.py‎

Lines changed: 2 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -9,7 +9,7 @@
99

1010
from flamingo_tools.s3_utils import BUCKET_NAME, create_s3_target
1111
from flamingo_tools.measurements import compute_object_measures
12-
from flamingo_tools.segmentation.sgn_subtype_utils import STAIN_TO_TYPE
12+
from flamingo_tools.segmentation.sgn_subtype_utils import STAIN_TO_TYPE, COCHLEAE
1313

1414

1515
# Define the animal specific octave bands.
@@ -68,20 +68,6 @@ def frequency_mapping(frequencies, values, animal="mouse", transduction_efficien
6868
"M_AMD_Runx1_L": ["PV", "CR", "Ntng1"],
6969
}
7070

71-
COCHLEAE = {
72-
"M_LR_000098_L": {"seg_data": "SGN_v2", "subtype": ["CR", "Ntng1"], "component_list": [1, 2]},
73-
"M_LR_000099_L": {"seg_data": "PV_SGN_v2", "subtype": ["Calb1", "Lypd1"]},
74-
"M_LR_000184_L": {"seg_data": "SGN_v2", "subtype": ["Prph"], "output_seg": "SGN_v2b"},
75-
"M_LR_000184_R": {"seg_data": "SGN_v2", "subtype": ["Prph"], "output_seg": "SGN_v2b"},
76-
"M_LR_000260_L": {"seg_data": "SGN_v2", "subtype": ["Prph", "Tuj1"]},
77-
"M_LR_N110_L": {"seg_data": "SGN_v2", "subtype": ["Calb1", "Ntng1"]},
78-
"M_LR_N110_R": {"seg_data": "SGN_v2", "subtype": ["Calb1", "Ntng1"]},
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"M_LR_N152_L": {"seg_data": "SGN_v2", "subtype": ["CR", "Ntng1"], "component_list": [1, 2]},
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"M_AMD_N180_L": {"seg_data": "SGN_merged", "subtype": ["CR", "Ntng1"]},
81-
"M_AMD_N180_R": {"seg_data": "SGN_merged", "subtype": ["CR", "Ntng1"]},
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"M_AMD_Runx1_L": {"seg_data": "SGN_v2", "subtype": ["CR", "Ntng1"]},
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}
84-
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GROUPINGS = {
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"Type Ia;Type Ib;Type Ic": ["M_LR_000098_L", "M_LR_N152_L", "M_AMD_N180_L", "M_AMD_N180_R"],
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"Type I;Type II": ["M_LR_000184_L", "M_LR_000184_R", "M_LR_000260_L"],
@@ -101,7 +87,7 @@ def frequency_mapping(frequencies, values, animal="mouse", transduction_efficien
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COLORS = {
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"Type Ia": "#133374",
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"Type Ib": "#27339C",
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"Type Ib/Ic": "#67279C",
90+
"Type IbIc": "#67279C",
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"Type Ic": "#9C276F",
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"inconclusive": "#9C8227",
10793

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