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373 | class Wang2024Reader(ConnectomeDataset):
"""
Reader of data from multiple connectomes...
"""
verbose = False
spreadsheet_location = os.path.dirname(os.path.abspath(__file__)) + "/data/"
reader_description = "Not set..."
dx1_set = False
ef1_set = False
dx3_set = False
ef3_set = False
def map_cell_name(self, cell_name: str) -> str:
if cell_name == "DB1/3":
return "DB1"
elif cell_name == "DB3/1":
return "DB3"
elif cell_name == "DX1/2":
if not self.dx1_set:
self.dx1_set = True
return "DX1"
else:
return "DX2"
elif cell_name == "EF1/2":
if not self.ef1_set:
self.ef1_set = True
return "EF1"
else:
return "EF2"
elif cell_name == "DX3/4":
if not self.dx3_set:
self.dx3_set = True
return "DX3"
else:
return "DX4"
elif cell_name == "EF3/4":
if not self.ef3_set:
self.ef3_set = True
return "EF3"
else:
return "EF4"
elif len(cell_name) == 3 and cell_name[0:2] == "CA":
return cell_name[0:2] + "0" + cell_name[2]
elif len(cell_name) == 3 and cell_name[0:2] == "CP":
return cell_name[0:2] + "0" + cell_name[2]
else:
if not is_any_neuron(cell_name):
raise ValueError("Unknown cell name: %s" % cell_name)
return cell_name
def map_neurotransmitter(
self, neurotransmitter: str, cat_1_present: bool, snf_3_present: bool
) -> str:
"""
Maps neurotransmitter names to a standard format.
"""
if neurotransmitter is None:
return None
neurotransmitter = neurotransmitter.strip()
if (
neurotransmitter == "ACh"
or neurotransmitter == "ACh - NEW"
or neurotransmitter == "*ACh"
or neurotransmitter == "*ACh - NEW"
or neurotransmitter == "ACh (new)"
or neurotransmitter == "*ACh (new)"
):
return ACETYLCHOLINE
elif neurotransmitter == "DA":
return DOPAMINE
elif neurotransmitter == "GABA" or neurotransmitter == "*GABA":
return GABA
elif (
neurotransmitter == "Glu"
or neurotransmitter == "Glu - NEW"
or neurotransmitter == "*Glu - NEW"
or neurotransmitter == "*Glu (new)"
or neurotransmitter == "Glu (new)"
or neurotransmitter == "*Glu"
):
return GLUTAMATE
elif neurotransmitter == "octopamine":
return OCTOPAMINE
elif (
neurotransmitter == "tyramine"
or neurotransmitter == "tyramine (new)"
or neurotransmitter == "tyramine (synthesis + uptake) - NEW"
):
return TYRAMINE
elif (
neurotransmitter == "betaine (uptake)"
or neurotransmitter == "betaine (uptake) - NEW"
or neurotransmitter == "*betaine (uptake) - NEW"
or neurotransmitter == "betaine (uptake) (new)"
or neurotransmitter == "*betaine (uptake) (new)"
):
if cat_1_present and snf_3_present:
return BETAINE
else:
print_(
" Note: Betaine neurotransmitter found without cat_1 or snf_3 present, returning None."
)
return None
elif (
neurotransmitter == "5-HT"
or neurotransmitter == "5-HT (synthesis + uptake)"
or neurotransmitter == "5-HT (synthesis + uptake)"
or neurotransmitter == "5-HT (new)"
or neurotransmitter == "*5-HT (new)"
or neurotransmitter == "5-HT (alternative synthesis/uptake mechanism)"
or neurotransmitter
== "male - 5-HT (alternative synthesis/uptake mechanism?)"
):
return SEROTONIN
elif neurotransmitter == "5-HTP - NEW" or neurotransmitter == "5-HTP (new)":
return FIVE_HTP
elif neurotransmitter == "5-HTP (synthesis) and 5-HT (uptake) (new)":
return FIVE_HTP_FIVE_HT
elif (
neurotransmitter == "5-HT (uptake)" or neurotransmitter == "*5-HT (uptake)"
):
return SEROTONIN_UPTAKE
elif neurotransmitter == "GABA (uptake)":
return GABA_UPTAKE
elif neurotransmitter == "PEOH? (new)":
return PEOH
elif (
neurotransmitter == "unknown (orphan)"
or neurotransmitter.lower() == "unknown (orphan, unc-47 expression)"
or neurotransmitter.lower() == "unknown (orphan; unc-47 positive)"
or neurotransmitter == "unknown (orphan, unc-46 expression)"
):
return UNKNOWN_ORPHAN_NEUROTRANSMITTER
elif (
neurotransmitter == "unknown monoamine? - NEW"
or neurotransmitter == "unknown monoamine (new)"
or neurotransmitter == "unknown monoamine? (new)"
or neurotransmitter == "bas-1-depen unknown monoamine? - NEW"
or neurotransmitter == "bas-1-depen unknown monoamine? (new)"
or neurotransmitter == "*bas-1-depen unknown monoamine? (new)"
):
return UNKNOWN_MONOAMINERGIC_NEUROTRANSMITTER
else:
raise ValueError("Unknown neurotransmitter: %s" % neurotransmitter)
# return "NT_not_yet_supported__%s" % neurotransmitter.replace(' ', '_').replace('(', '_').replace(')', '_') # neurotransmitter
def __init__(self, sex):
ConnectomeDataset.__init__(self)
sources = []
self.all_neurotransmitters = {}
if sex == "Hermaphrodite" or sex == "Male":
sources.append(
[
"%selife-95402-supp2-v1.xlsx" % self.spreadsheet_location,
"Supp File 2",
("Cook et al. 2019 Hermaphrodite connectome", "Cook2019HermReader"),
("Bentley et al. 2015", "WormNeuroAtlasMAReader"),
]
)
if sex == "Male":
sources.append(
[
"%selife-95402-supp3-v1.xlsx" % self.spreadsheet_location,
"Supp File 3",
("Cook et al. 2019 Male connectome", "Cook2019MaleReader"),
("Bentley et al. 2015", "WormNeuroAtlasMAReader"),
]
)
for source in sources:
filename = source[0]
sheet = source[1]
BASIS_ANATOMICAL_CONN, BASIS_MONOAMINERGIC_CONN = source[2], source[3]
wb = load_workbook(filename)
print_("Opened the Excel file: " + filename)
neurotransmitters = {}
sheet = wb.get_sheet_by_name(sheet)
rows = range(5, 307)
col_offset = 0
if "supp3" in filename:
rows = range(6, 103)
col_offset = 1
for i in rows:
print("Reading row %d" % i)
cell_wang = sheet.cell(row=i, column=3).value
if cell_wang is not None:
cell = self.map_cell_name(cell_wang)
cat_1_present = (
str(sheet.cell(row=i, column=9).fill.patternType) == "solid"
)
snf_3_present = "NEW" in str(sheet.cell(row=i, column=16).value)
nt_1 = self.map_neurotransmitter(
sheet.cell(row=i, column=21 + col_offset).value,
cat_1_present,
snf_3_present,
)
nt_2 = self.map_neurotransmitter(
sheet.cell(row=i, column=22 + col_offset).value,
cat_1_present,
snf_3_present,
)
nt_3 = self.map_neurotransmitter(
sheet.cell(row=i, column=23 + col_offset).value,
cat_1_present,
snf_3_present,
)
print_(
f"Reading row {i}: {cell}, NTs: {nt_1}, {nt_2}, {nt_3}; cat_1_present: {cat_1_present}, snf_3_present: {snf_3_present}"
)
nts = [nt_1]
if nt_2 is not None:
nts.append(nt_2)
if nt_3 is not None:
nts.append(nt_3)
print_(" - Cell: %s, nts: %s" % (cell, nts))
neurotransmitters[cell] = nts
anatomical_conn_reader = load_connectome_dataset_file(
get_cache_filename(BASIS_ANATOMICAL_CONN[1])
)
monoaminergic_conn_reader = load_connectome_dataset_file(
get_cache_filename(BASIS_MONOAMINERGIC_CONN[1])
)
# neurons, muscles, other_cells, conns = self.read_all_data()
anat_conns = anatomical_conn_reader.get_current_connection_info_list()
print_("Adding %i conns from %s" % (len(anat_conns), BASIS_ANATOMICAL_CONN))
for conn in anat_conns[:]:
print_("Original conn: %s" % conn)
if is_any_neuron(conn.pre_cell) and conn.pre_cell in neurotransmitters:
if conn.synclass in ALL_KNOWN_CHEMICAL_NEUROTRANSMITTERS + [
GENERIC_CHEM_SYN
]:
conn.number = 1.0
for nt in neurotransmitters[conn.pre_cell]:
if nt in ALL_KNOWN_CHEMICAL_NEUROTRANSMITTERS:
conn.synclass = nt
print_(" Adding new conn: %s" % conn)
self.add_connection_info(conn)
else:
print_(
" Not a known chemical neurotransmitter: %s"
% conn.synclass
)
else:
print_(
" Not a neuron, or not in cells with known neurotransmitters..."
)
monoamine_conns = (
monoaminergic_conn_reader.get_current_connection_info_list()
)
print_(
"Adding %i conns from %s"
% (len(monoamine_conns), BASIS_MONOAMINERGIC_CONN)
)
for conn in monoamine_conns[:]:
print_("Original conn: %s" % conn)
if is_any_neuron(conn.pre_cell) and conn.pre_cell in neurotransmitters:
conn.number = 1.0
for nt in neurotransmitters[conn.pre_cell]:
if nt in MONOAMINERGIC_SYN_CLASSES:
conn.synclass = nt
print_(" Adding new conn: %s" % conn)
self.add_connection_info(conn)
else:
print_(
" Not a neuron, or not in cells with known neurotransmitters..."
)
self.all_neurotransmitters.update(neurotransmitters)
self.reader_description = (
"""A reader combining neurotransmitter atlas values from Wang et al. 2024 (source: %s) with basic anatomical connectivity information from %s, and monoaminergic receptor expression information from %s"""
% (
"; ".join(
[get_dataset_source_on_github(f[0].split("/")[-1]) for f in sources]
),
BASIS_ANATOMICAL_CONN[0],
BASIS_MONOAMINERGIC_CONN[0],
)
)
def read_data(self):
return self._read_data()
def read_muscle_data(self):
return self._read_muscle_data()
|