def nist_ms_comparison(sample_name, mass_list, mass_spec):
data_dict = {}
try:
pynist.generate_ini(nist_path, "mainlib", 5)
def remove_chars(input_string):
return input_string.replace("Hit 1 : ","").replace("Hit 2 : ","")\
.replace("Hit 3","").replace("Hit 4","")\
.replace("Hit 5","").replace("MF:","")\
.replace(":","").replace("<","").replace(">","")\
.replace(" ","").replace(sample_name,"")
raw_output = pynist.nist_db_connector(nist_path,"MSP/{}.MSP".format(sample_name))
# Process output
for i in range(1,6):
raw_output = raw_output.replace("Hit {} : ".format(i),"Hit{};".format(i))
raw_output = raw_output.replace("<<",'"').replace(">>",'"').split("\n")
#for row in raw_output:
# print(row)
matches_dict = {}
for i in range(1,6):
row = raw_output[i].split(";")
matches_dict[row[0]] = {"Name":row[1], "MF":(row[3].replace("MF:",'').replace(" ",'')), "RMF":(row[4].replace("RMF:",'').replace(" ",'')), "CAS":(row[6].replace("CAS:",'').replace(" ",'')),"Lib":(row[8].replace("Lib:",'').replace(" ",''))}
#for match in matches_dict:
# print(match)
# print(matches_dict[match])
except:
traceback.print_exc() #print the error
pynist.reload_ini(nist_path)
sys.exit(1)
print("\n")
pynist.reload_ini(nist_path)
return matches_dict
def nist_ms_comparison(self, sample_name, n_hits=5):
"""
:param sample_name:
:type sample_name:
:param n_hits:
:type n_hits:
:return:
:rtype:
"""
# data_dict = {}
try:
pynist.generate_ini(internal_config.nist_path, "mainlib", n_hits)
# def remove_chars(input_string):
# for i in range(n_hits + 1):
# input_string = input_string.replace("Hit {} : ", "")
#
# return input_string.replace("MF:", "") \
# .replace(":", "").replace("<", "").replace(">", "") \
# .replace(" ", "").replace(self.name, "")
raw_output = pynist.nist_db_connector(
internal_config.nist_path,
os.path.join(internal_config.msp_dir,
"{}.MSP".format(sample_name)))
# Process output
for i in range(n_hits + 1):
raw_output = raw_output.replace("Hit {} : ".format(i), "Hit{};".format(i)) \
.replace("Hit {} : ".format(i), "Hit{};".format(i)) \
.replace("Hit {}: ".format(i), "Hit{};".format(i))
raw_output = raw_output.replace("<<", '"').replace(">>",
'"').split("\n")
matches_dict = {}
for i in range(1, n_hits + 1):
row = list(
csv.reader([raw_output[i]], delimiter=";",
quotechar='"'))[0]
matches_dict[row[0]] = {
"Name": row[1],
"MF": (row[3].replace("MF:", '').replace(" ", '')),
"RMF": (row[4].replace("RMF:", '').replace(" ", '')),
"CAS": (row[6].replace("CAS:", '').replace(" ", '')),
# "Lib": (row[8].replace("Lib:", '').replace(" ", ''))
}
except:
traceback.print_exc() # print the error
pynist.reload_ini(internal_config.nist_path)
sys.exit(1)
print("\r\033[KSearch Complete") # , end='')
pynist.reload_ini(internal_config.nist_path)
return matches_dict
def run(self):
# Indicate which steps to perform
print(f"do_qualitative: {self.config.do_qualitative}")
print(f"do_merge: {self.config.do_merge}")
print(f"do_counter: {self.config.do_counter}")
print(f"do_spectra: {self.config.do_spectra}")
print(f"do_charts: {self.config.do_charts}")
# Loads the experiment file created during Quantitative Processing
for prefix in self.config.prefixList:
file_name = os.path.join(self.config.expr_dir, prefix + ".expr")
self.expr_list.append(load_expr(file_name))
if self.config.do_qualitative:
print("Qualitative Processing in Progress...")
for prefix in self.config.prefixList:
# print(list(rt_alignment[prefix]))
self.qualitative_processing(prefix, list(rt_alignment[prefix]))
if self.config.do_merge:
self.merge()
if self.config.do_counter:
chart_data = self.match_counter(self.ms_comparisons(ms_alignment))
chart_data = chart_data.set_index("Compound", drop=True)
# remove duplicate compounds:
# chart_data_count = Counter(chart_data["Compound"])
chart_data_count = Counter(chart_data.index)
replacement_data = {
"Compound": [],
f"{self.lot_name} Peak Area": [],
f"{self.lot_name} Standard Deviation": []
}
for prefix in self.config.prefixList:
replacement_data[prefix] = []
for compound in chart_data_count:
if chart_data_count[compound] > 1:
replacement_data["Compound"].append(compound)
replacement_data[f"{self.lot_name} Peak Area"].append(
sum(chart_data.loc[compound,
f"{self.lot_name} Peak Area"]))
peak_data = []
for prefix in self.config.prefixList:
replacement_data[prefix].append(
sum(chart_data.loc[compound, prefix]))
peak_data.append(sum(chart_data.loc[compound, prefix]))
replacement_data[
f"{self.lot_name} Standard Deviation"].append(
numpy.std(peak_data))
chart_data = chart_data.drop(compound, axis=0)
replacement_data = pandas.DataFrame(replacement_data)
replacement_data = replacement_data.set_index("Compound",
drop=False)
chart_data = chart_data.append(replacement_data, sort=False)
chart_data.sort_index(inplace=True)
chart_data = chart_data.drop("Compound", axis=1)
chart_data['Compound Names'] = chart_data.index
chart_data.to_csv(os.path.join(
self.config.csv_dir,
"{}_CHART_DATA.csv".format(self.lot_name)),
sep=";")
else:
chart_data = pandas.read_csv(os.path.join(
self.config.csv_dir,
"{}_CHART_DATA.csv".format(self.lot_name)),
sep=";",
index_col=0)
# chart_data = chart_data.set_index("Compound", drop=True)
if self.config.do_spectra:
self.generate_spectra_from_alignment(rt_alignment, ms_alignment)
# Write Mass Spectra to OpenChrom-like CSV files
def generate_spectra_csv(rt_data, ms_data, name):
# Write Mass Spectra to OpenChrom-like CSV files
ms = ms_data[0] # first mass spectrum
spectrum_csv_file = os.path.join(self.config.spectra_dir,
self.lot_name,
f"{name}_data.csv")
spectrum_csv = open(spectrum_csv_file, 'w')
spectrum_csv.write(
'RT(milliseconds);RT(minutes) - NOT USED BY IMPORT;RI;')
spectrum_csv.write(';'.join(str(mz) for mz in ms.mass_list))
spectrum_csv.write("\n")
for rt, ms in zip(rt_data, ms_data):
spectrum_csv.write(
f"{int(rt * 60000)};{rounders(rt, '0.0000000000')};0;")
spectrum_csv.write(';'.join(
str(intensity) for intensity in ms.mass_spec))
spectrum_csv.write('\n')
spectrum_csv.close()
for prefix in self.config.prefixList:
print(prefix)
# print(rt_alignment[prefix])
# print(ms_alignment[prefix])
generate_spectra_csv(rt_alignment[prefix],
ms_alignment[prefix], prefix)
if self.config.do_charts:
print("\nGenerating Charts")
chart_data.to_csv(os.path.join(
self.config.csv_dir,
"{}_CHART_DATA.csv".format(self.lot_name)),
sep=";")
maybe_make(os.path.join(self.config.charts_dir, self.lot_name))
if chart_data.empty:
print("ALERT: No peaks were found for compounds that have")
print(" previously been reported in literature.")
print(" Check the results for more information\n")
else:
from GSMatch.GSMatch_Core.charts import box_whisker_wrapper, radar_chart_wrapper, \
mean_peak_area_wrapper, \
peak_area_wrapper
# from GSMatch.GSMatch_Core.charts import peak_area_wrapper, radar_chart_wrapper
radar_chart_wrapper(chart_data, [self.lot_name],
use_log=10,
legend=False,
mode=os.path.join(self.config.charts_dir,
self.lot_name,
"radar_log10_peak_area"))
radar_chart_wrapper(chart_data, [self.lot_name],
use_log=False,
legend=False,
mode=os.path.join(self.config.charts_dir,
self.lot_name,
"radar_peak_area"))
mean_peak_area_wrapper(chart_data, [self.lot_name],
mode=os.path.join(
self.config.charts_dir,
self.lot_name, "mean_peak_area"))
peak_area_wrapper(chart_data,
self.lot_name,
self.config.prefixList,
mode=os.path.join(self.config.charts_dir,
self.lot_name,
"peak_area_percentage"))
peak_area_wrapper(chart_data,
self.lot_name,
self.config.prefixList,
percentage=False,
mode=os.path.join(self.config.charts_dir,
self.lot_name,
"peak_area"))
peak_area_wrapper(chart_data,
self.lot_name,
self.config.prefixList,
use_log=10,
mode=os.path.join(
self.config.charts_dir, self.lot_name,
"log10_peak_area_percentage"))
samples_to_compare = [(self.lot_name, self.config.prefixList)]
box_whisker_wrapper(chart_data,
samples_to_compare,
mode=os.path.join(self.config.charts_dir,
self.lot_name,
"box_whisker"))
with open(
os.path.join(self.config.results_dir, f"{self.lot_name}.info"),
"w") as info_file:
for prefix in self.config.prefixList:
info_file.write(f"{prefix}\n")
# TODO: self.make_archive()
pynist.reload_ini(self.config.nist_path)
print("\nComplete.")