def __init__(self, enrichment_path, out_path, settings_path):
settings.load(settings_path)
self.settings_path = settings_path
self.enrichment_path = Path(enrichment_path)
aa_label_df = pd.read_csv(settings.aa_label_path, sep='\t')
aa_label_df.set_index('study_type', inplace=True)
self.aa_labeling_dict = aa_label_df.loc[
settings.study_type, ].to_dict()
if self.enrichment_path.suffix == '.tsv':
self._enrichment_df = pd.read_csv(filepath_or_buffer=str(
self.enrichment_path),
sep='\t')
elif self.enrichment_path.suffix == '.csv':
self._enrichment_df = pd.read_csv(filepath_or_buffer=str(
self.enrichment_path),
sep=',')
if settings.recognize_available_cores is True:
self._n_processors = mp.cpu_count()
else:
self._n_processors = settings.n_processors
#$breaks windows/python interactions if too many cores are used. very niche application but still relevant
if self._n_processors > 60:
self.n_processors = 60
self._mp_pool = mp.Pool(self._n_processors)
self.out_path = out_path
self.model = None
def __init__(self, model_path, out_path, settings_path):
settings.load(settings_path)
self.settings_path = settings_path
itertuple_renamer = copy(protein_itertuple_renamer)
self.error = ""
#$get the number of cores we're using for multiprocessing
if settings.recognize_available_cores is True:
self._n_processors = mp.cpu_count()
else:
self._n_processors = settings.n_processors
#$breaks windows/python interactions if too many cores are used. very niche application but still relevant
if self._n_processors > 60:
self.n_processors = 60
self._mp_pool = mp.Pool(self._n_processors)
if model_path[-4:] == ".tsv":
self.model = pd.read_csv(model_path, sep='\t')
elif model_path[-4:] == ".csv":
self.model = pd.read_csv(model_path)
else: # $should never trigger unless we are fiddling with the gui
raise ValueError("invalid file extension")
self.model.rename(columns=itertuple_renamer, inplace=True)
self.out_path = out_path
def _mp_prepare(settings_path, args, aa_labeling_dict=None):
settings.load(settings_path)
#file_path, time, enrichment = args
file_path, time, enrichment, sample_group, biological_replicate = args
df = pd.read_csv(filepath_or_buffer=file_path, sep='\t')
if "mzs_list" in df.columns:
df.drop(inplace=True,
columns=[
"mzs_list", "intensities_list", "rt_list",
"baseline_list"
])
df = TheoryPreparer._apply_filters(df)
if aa_labeling_dict:
#$don't include an else for either if statement. no need to calculate if column exists
#$ and we don't want to add the column if we can't calculate it since checking for it is an error check for later steps
if literature_n_name not in df.columns:
if aa_labeling_dict != "":
df = df.apply(TheoryPreparer._calculate_literature_n,
axis=1,
args=(aa_labeling_dict, ))
df['time'] = time
df['enrichment'] = enrichment
df["sample_group"] = sample_group
df["bio_rep"] = biological_replicate
return df
def __init__(self, settings_path, id_path, mzml_path, out_path):
'''
Parameters
----------
ids : str
The name of the file containing the identifications. This data
will likely have been taken from an unlabeled run.
mzml : str
The name of the file containing mass spectrometry data in the
mzml format, labeled or not.
settings : str
The name of the file containing the settings for this instance
of the exctractor, which may contain the settings for the rest
of Deuterater as well. This file *should* be in ``.yaml`` format.
For addition information, see the settings file's documentation
'''
self.settings_path = settings_path
settings.load(self.settings_path)
self.id_path = id_path
self.mzml_path = mzml_path
self.out_path = out_path
self.ids = {}
self._id_chunks = ()
self._mzml_native_id_bounds = []
self.model = pd.DataFrame()
try:
if settings.recognize_available_cores is True:
self._n_processors = mp.cpu_count()
else:
self._n_processors = settings.n_processors
#$breaks windows/python interactions if too many cores are used. very niche application but still relevant
if self._n_processors > 60:
self.n_processors = 60
self._chunk_size = settings.chunk_size
self._chunking_threshold = mul(settings.chunking_method_threshold,
settings.chunk_size)
self._id_rt_unit = settings.id_file_rt_unit
self._trim_ids = settings.trim_ids_to_mzml_bounds
self._rt_window = settings.time_window
self._mp_pool = mp.Pool(self._n_processors)
if not os.path.exists(self.out_path):
open(self.out_path, 'w').close()
# TODO: this needs fixed, it doesn't catch open files
if not os.access(self.out_path, os.W_OK):
raise PermissionError('Output path not writeable.')
except Exception as e:
print(e)
traceback.print_tb(e.__traceback__)
raise
def __init__(self, model_path, out_path, graph_folder, settings_path):
settings.load(settings_path)
if model_path[-4:] == ".tsv":
self.model = pd.read_csv(model_path, sep='\t')
elif model_path[-4:] == ".csv":
self.model = pd.read_csv(model_path)
else: #$should never trigger unless we are fiddling with the gui
raise ValueError("invalid file extension")
self.out_path = out_path
self.rate_model = None
self.datapoint_model = None # CQ
self.graph_folder = graph_folder
self.settings_path = settings_path
#$get the number of cores we're using for multiprocessing
if settings.recognize_available_cores is True:
self._n_processors = mp.cpu_count()
else:
self._n_processors = settings.n_processors
#$breaks windows/python interactions if too many cores are used. very niche application but still relevant
if self._n_processors > 60:
self.n_processors = 60
self._mp_pool = mp.Pool(self._n_processors)
def extract(settings_path, mzml_path, index_to_ID, chunk):
'''Extract data from the mzml according to the identification information
'''
# A rough outline of how the logic flows.
# EXTRACT:
# for each scan in the reader
# in ms level 1
# for each id in the window
# isotope extraction specific logic
# Turn the warnings off so that it doesn't mess up the tqdm progress bar
warnings.filterwarnings("ignore")
# Load the settings into the settings module. This needs to be done in each
# process due to how python handles multiprocessing
settings.load(settings_path)
# Open a file pointer to the mzml file
mzml_fp = pymzml.run.Reader(path_or_file=mzml_path,
build_index_from_scratch=True)
mzml_bounds = dml.get_bounds(mzml_fp, index_to_ID)
# Check for an empty id file chunk
if len(chunk) <= 0:
warnings.warn(
EmptyIdChunkWarning('There are no identifications in this chunk'))
# Set the high and low retention time bounds, based on the chunk of the
# identification file
lo_rt_bound = chunk.at[0, 'rt'] - settings.time_window
if lo_rt_bound < 0:
lo_rt_bound = 0
hi_rt_bound = chunk.at[len(chunk) - 1, 'rt'] + settings.time_window
# Search for the scans at the high and low retention time bounds
lo_spec_idx = dml.retention_time_search(mzml_fp, index_to_ID, lo_rt_bound)
hi_spec_idx = dml.retention_time_search(mzml_fp, index_to_ID, hi_rt_bound)
## if mzml_fp[index_to_ID[hi_spec_idx]] > hi_rt_bound:
## hi_spec_idx = hi_spec_idx - 1
# Logic block for handling out-of-bounds indices
if lo_spec_idx != -1 and hi_spec_idx != -1:
# Do nothing if both indices are in bounds
pass
elif lo_spec_idx == -1 and hi_spec_idx != -1:
# If the just the higher index is found, assign the lowest index in
# the mzml to 'lo_spec_idx'
lo_spec_idx = mzml_bounds['idx_min']
elif lo_spec_idx != -1 and hi_spec_idx == -1:
# If the just the lower index is found, assign the highest index in
# the mzml to 'hi_spec_idx'
hi_spec_idx = mzml_bounds['idx_max']
elif lo_rt_bound < mzml_bounds['rt_min'] < \
mzml_bounds['rt_max'] < hi_rt_bound:
# If neither index is found but the time span covered by the chunk of
# the ID file encompasses that of the mzml, assign 'lo_spec_idx' and
# 'hi_spec_idx' the minimum and maximum index values given by the
# mzml file
lo_spec_idx = mzml_bounds['idx_min']
hi_spec_idx = mzml_bounds['idx_max']
else:
# Otherwise, there is no intersection between the ID file and the mzml
# in terms of retention time and no analysis can be made
return -1
ids = [] # initialize the list of identifications
# TODO: redefine this column as ionmass?
chunk['mass'] = chunk['mz'] * chunk['z']
# Instantiate all of the identifications in the chunk
for row in chunk.itertuples(index=True):
ids.append(
ID(
rt=row.rt,
mz=row.mz,
mass=row.mass,
z=row.z,
n_isos=row.n_isos,
#cf=row.cf
))
# Iterate through all of the relevent spectrums in the mzml
for spectrum_index in dmt.inclusive_range(lo_spec_idx, hi_spec_idx):
# apply the index_to_ID map in order to access the correct spectrum
native_id = index_to_ID[spectrum_index]
try:
# try to access this spectrum
spectrum = mzml_fp[native_id]
spec_rt = spectrum.scan_time_in_minutes()
spec_mzs = spectrum.mz
spec_abs = spectrum.i
except Exception:
# TODO: use a more specific Exception
# catch the exception and move on if the spectrum is not found
continue
# only deal with desired ms_level
if spectrum.ms_level != settings.ms_level:
continue
# determine id indices of peaks searches
# adding and subtracting the floating point error tolerance allows us
# to include the extremes of the range
local_window_min = \
spec_rt - (settings.time_window) # + settings.fpe_tolerance)
local_window_max = \
spec_rt + (settings.time_window) # + settings.fpe_tolerance)
try:
lo_slice_index = \
min(chunk[chunk['rt'] > local_window_min].axes[0].tolist())
hi_slice_index = \
max(chunk[chunk['rt'] < local_window_max].axes[0].tolist())
except:
continue
# iterate through relevant ids
for id in ids[dmt.inclusive_slice(lo_slice_index, hi_slice_index)]:
charge = id.z
# instatntiate an envelope
envelope = Envelope(peaks=[],
rt=spec_rt,
n_lookback=settings.peak_lookback,
n_lookahead=settings.peak_lookahead)
lo_baseline_lookback = None
hi_baseline_lookback = None
lo_baseline_lookahead = None
hi_baseline_lookahead = None
peak_range_start = 0 - settings.peak_lookback
peak_range_end = id.n_isos + settings.peak_lookahead
# Iterate through all of the peaks we want to look for
for peak_num in range(peak_range_start, peak_range_end):
# define the mz to search for in the spectrum
search_mz = id.mz + (peak_num * NEUTRON / charge)
# define the ppm error tolerance
reach = settings.ppm_window / 1_000_000.0 * search_mz
# find the index of the nearest data point in that spectrum's
# mz array
index = dmt.find_nearest_index(spec_mzs, search_mz)
if peak_num == 0:
# set the bounds for defining the baseline
lo_baseline_lookback = dmt.find_nearest_index(
spec_mzs, id.mz - settings.baseline_lookback)
hi_baseline_lookback = index
lo_baseline_lookahead = index
hi_baseline_lookahead = dmt.find_nearest_index(
spec_mzs, id.mz + settings.baseline_lookback)
# TODO: Do I need to speed this up by removing typecheck?
# TODO: Expand this to only one paren/bracket per line?
if abs(spec_mzs[index] - search_mz) < reach:
# If the value at that index is within the reach
envelope.append_peak(
Peak(mz=spec_mzs[index],
abundance=spec_abs[index],
i=peak_num))
else:
if 0 <= peak_num < id.n_isos:
# set the envelopes validity flag to false if no peak
# is found, then move on to the next identification
envelope.is_valid = False
envelope.append_peak(
Peak(
mz=search_mz,
# TODO: it might be better to set this to NA
abundance=0,
i=peak_num))
# TODO Do i need to speed this up by removing typecheck?
# If all of the peaks have been found, add it to the
# identification (after determining the baseline)
# NOTE: baseline is defined as the median abundance of the 100
# mz units preceding the m0 peak
# CQ: Changing baseline to be the MAD of 100 m/z datapoints ahead and behind m0 peak.
# Adapted from Marginean, I; Tang, K; Smith, RD.; Kelly, R; Picoelectrospray Ionization Mass Spectrometry
# Using Narrow-Bore Chemically Etched Emitters, ASMS, 2013
def mad(values):
m = median(values)
return median([abs(a - m) for a in values])
lookback_baseline = [
l for l in spec_abs[dmt.inclusive_slice(
lo_baseline_lookback, hi_baseline_lookback)] if l != 0
][-100:]
lookahead_baseline = [
l for l in spec_abs[dmt.inclusive_slice(
lo_baseline_lookahead, hi_baseline_lookahead)] if l != 0
][1:101]
normal_distribution_scale_factor = 1.4826
envelope.baseline = normal_distribution_scale_factor * mad(
lookback_baseline + lookahead_baseline)
id.append_envelope(envelope)
mzml_fp.close()
for id in ids:
id.aggregate_envelopes()
# TODO: Better variable naming here. obs? I can do better
# TODO: is there better way to initialize this?
# TODO: add lookback columns?
# Initialize the dataframe to send back to the main process
peak_out = pd.DataFrame(index=chunk.index.values,
columns=[
'mzs', 'abundances', 'lookback_mzs',
'lookback_abundances', 'lookahead_mzs',
'lookahead_abundances', 'rt_min', 'rt_max',
'baseline_signal', "mads", 'mzs_list',
'intensities_list', "rt_list", "baseline_list",
'num_scans_combined', 'mzml_path'
])
# Populate valid rows.
for row in peak_out.itertuples():
i = row.Index
id = ids[i]
if id.condensed_envelope:
mzs, abundances = id.condensed_envelope.to_obs()
lb_mzs, lb_abundances = id.condensed_envelope.lb_obs()
la_mzs, la_abundances = id.condensed_envelope.la_obs()
peak_out.at[i, 'mzs'] = mzs
peak_out.at[i, 'abundances'] = abundances
peak_out.at[i, 'rt_min'] = id.rt_min
peak_out.at[i, 'rt_max'] = id.rt_max
peak_out.at[i, 'baseline_signal'] = id.condensed_envelope.baseline
peak_out.at[i, 'lookback_mzs'] = lb_mzs
peak_out.at[i, 'lookback_abundances'] = lb_abundances
peak_out.at[i, 'lookahead_mzs'] = la_mzs
peak_out.at[i, 'lookahead_abundances'] = la_abundances
peak_out.at[i, 'mads'] = str(id.mads)
peak_out.at[i, 'num_scans_combined'] = len(id._envelopes)
peak_out.at[i, 'mzml_path'] = mzml_path
results = chunk.join(peak_out)
return results
def run_rate_workflow(self):
# $will need some settings
settings.load(rate_settings_file)
# $first we need to check which steps are checked
worklist = self.check_table_checklist()
# $ only proceed if we have a
if worklist == []:
QtWidgets.QMessageBox.information(
self, "Error",
("No options were "
"checked. Please check steps to performed and try again"))
return
elif type(worklist) == str:
QtWidgets.QMessageBox.information(self, "Error", worklist)
return
# $second we need to an output folder and check it for output folder
QtWidgets.QMessageBox.information(self, "Info",
("Please select folder "
"for output"))
output_folder = QtWidgets.QFileDialog.getExistingDirectory(
self, "Select an Output Folder", self.file_loc,
QtWidgets.QFileDialog.ShowDirsOnly)
if output_folder == "": return
# $change location we start asking for things at
# $don't change since all output is going in here
self.file_loc = output_folder
# MainGuiObject._make_folder(output_folder)
#don't care if overwrite rate_settings.yaml but should check if want to use the settings already in the folder
if os.path.exists(os.path.join(output_folder, "rate_settings.yaml")):
comp_result = settings.compare(
rate_settings_file,
os.path.join(output_folder, "rate_settings.yaml"))
if comp_result != "MATCH":
if comp_result == "Mismatched Keys":
qBox = QtWidgets.QMessageBox(self)
qBox.setWindowTitle("Question")
question = "A settings file already exists in this output folder. Would you like to use those settings,or overwrite them?"
qBox.setText(question)
qBox.setIcon(QtWidgets.QMessageBox.Question)
qBox.setStandardButtons(QtWidgets.QMessageBox.Yes
| QtWidgets.QMessageBox.No
| QtWidgets.QMessageBox.Cancel)
yButton = qBox.button(QtWidgets.QMessageBox.Yes)
yButton.setText("Use Settings")
nButton = qBox.button(QtWidgets.QMessageBox.No)
nButton.setText("Overwrite")
response = qBox.exec_()
if response == QtWidgets.QMessageBox.Yes:
settings.load(
os.path.join(output_folder, "rate_settings.yaml"))
settings.freeze(rate_settings_file)
elif response == QtWidgets.QMessageBox.No:
if self.check_file_removal([
os.path.join(output_folder,
"rate_settings.yaml")
],
ask_permission=False):
settings.freeze(
os.path.join(output_folder,
"rate_settings.yaml"))
else:
return
else:
return
else:
#$no point asking if we can delete the file if it is the same anyway. still want to overwrite as part of checking permissions.
#$may not overwrite later
if self.check_file_removal(
[os.path.join(output_folder, "rate_settings.yaml")],
ask_permission=False):
settings.freeze(
os.path.join(output_folder, "rate_settings.yaml"))
else:
return
else:
settings.freeze(os.path.join(output_folder, "rate_settings.yaml"))
# $then need to check if the files exist. if so warn the user. function
no_extract_list = [w for w in worklist if w != "Extract"]
outputs_to_check = []
#$deal with the extra detail file added for rate calculation
if "Rate Calculation" in no_extract_list:
outputs_to_check.append(
os.path.join(output_folder, extra_rate_file))
for worklist_step in no_extract_list:
step_object_dict[worklist_step].complete_filename(self.file_loc)
outputs_to_check.append(
step_object_dict[worklist_step].full_filename)
# $if should only fail if an extract only, but that may occur
if outputs_to_check != []:
proceed = self.check_file_removal(outputs_to_check)
if not proceed:
return
# $now we need to get input and do the work. each step can only occur
# $once and they occur in order. so we will write them in order
# todo$ see if we can compress the code and make sure it is readable
previous_output_file = ""
extracted_files = []
make_table_in_order = True
for analysis_step in worklist:
if analysis_step == "Extract":
# $no if for this one, if extract is here it is the start
id_file = self.collect_single_file("ID", "Extract",
"CSV (*.csv)")
if id_file == "": return
# $always check if is good since it is first
infile_is_good = self.check_input(
step_object_dict[analysis_step], id_file)
if not infile_is_good: return
#$infile_is_good is just a check for blanks in the input file
data_is_good = self.check_extractor_input(
id_file, required_data_extractor_data, autofill_columns)
if not data_is_good: return
mzml_files = self.collect_multiple_files(
"Centroided Data", analysis_step, "mzML (*.mzML)")
if mzml_files == []: return
mzml_filenames = [
os.path.basename(filename) for filename in mzml_files
]
extracted_files = [
filename.replace(".mzML", ".tsv")
for filename in mzml_filenames
]
extracted_files = [
os.path.join(output_folder, filename)
for filename in extracted_files
]
extracted_intermediate_files = extracted_files
needed_files = list(
set(extracted_files + extracted_intermediate_files))
proceed = self.check_file_removal(needed_files)
if not proceed: return
# $need to run the table if necessary. taken from the
# $"Provide Time and Enrichment" elif
if "Provide Time and Enrichment" in worklist:
previous_output_file = step_object_dict[
"Provide Time and Enrichment"].full_filename
self.get_data_table = TimeEnrichmentWindow(
self, extracted_files, previous_output_file)
self.get_data_table.exec_()
# $don't make the table twice
make_table_in_order = False
# $now that the table is done we need to confirm the user
# $hit the proceed button on the table (same check as in
# $elif analysis_step == "Theory Generation" )
if not os.path.exists(previous_output_file): return
# $ modified from the extract-dir argument from the command line
for m in tqdm(range(len(mzml_files)),
total=len(mzml_files),
desc="Extracting mzml files: "):
extractor = Extractor(
id_path=os.path.join(self.file_loc, id_file),
mzml_path=mzml_files[m],
out_path=extracted_intermediate_files[m],
settings_path=rate_settings_file,
)
extractor.load()
extractor.run()
extractor.write()
#$need to delete classes when they're done or they may linger in RAM
del extractor
elif analysis_step == "Provide Time and Enrichment" and make_table_in_order:
# $if coming right after a list
if extracted_files == []:
extracted_files = self.collect_multiple_files(
"Extracted Data", "Provide Time and Enrichment",
"TSV (*.tsv)")
if extracted_files == []: return
# $ensure the input files are good. only need to deal with
# $if the user just selected
for e_file in extracted_files:
infile_is_good = self.check_input(
step_object_dict[analysis_step], e_file)
if not infile_is_good: return
# $ now that we have the extracted files we can make a table
# $the talbe will handle the output
previous_output_file = step_object_dict[
analysis_step].full_filename
self.get_data_table = TimeEnrichmentWindow(
self, extracted_files, previous_output_file)
self.get_data_table.exec_()
elif analysis_step == "Theory Generation":
# $since the files are in the table can just read that in
if previous_output_file == "":
previous_output_file = self.collect_single_file(
"time and enrichment", analysis_step,
"spreadsheet (*.csv *.tsv)")
if previous_output_file == "": return
infile_is_good = self.check_input(
step_object_dict[analysis_step], previous_output_file)
if not infile_is_good: return
# $else is to deal with a failed write from the previous table
# $ don't need an error message just return
elif not os.path.exists(previous_output_file):
return
# $final check to see if all of the files in the input table
# $still exist. don't want to error out in the middle of
# $multiprocessing
final_proceed = self.check_files_from_files(
previous_output_file, 0)
if not final_proceed: return
theorist = TheoryPreparer(
enrichment_path=previous_output_file,
out_path=step_object_dict[analysis_step].full_filename,
settings_path=rate_settings_file)
theorist.prepare()
theorist.write()
del theorist
previous_output_file = step_object_dict[
analysis_step].full_filename
elif analysis_step == "Fraction New Calculation":
if previous_output_file == "":
previous_output_file = self.collect_single_file(
"theoretical output", analysis_step,
"spreadsheet (*.csv *.tsv)")
if previous_output_file == "": return
infile_is_good = self.check_input(
step_object_dict[analysis_step], previous_output_file)
if not infile_is_good: return
# $not sure why this would happen but we'll put it here
# $to avoid future error
elif not os.path.exists(previous_output_file):
return
fnewcalc = FractionNewCalculator(
model_path=previous_output_file,
out_path=step_object_dict[analysis_step].full_filename,
settings_path=rate_settings_file)
fnewcalc.generate()
if fnewcalc.error != "":
QtWidgets.QMessageBox.information(self, "Error",
fnewcalc.error)
return
fnewcalc.write()
del fnewcalc
previous_output_file = step_object_dict[
analysis_step].full_filename
elif analysis_step == "Rate Calculation":
if previous_output_file == "":
previous_output_file = self.collect_single_file(
"fraction new", analysis_step,
"spreadsheet (*.csv *.tsv)")
if previous_output_file == "": return
# $need to ensure that we have proper colmns which varies
# $by setting
needed_columns = [
settings.peptide_analyte_id_column,
settings.peptide_analyte_name_column, "sample_group"
]
if settings.use_abundance != "No":
needed_columns.extend(
["abund_fn", "frac_new_abunds_std_dev"])
if settings.use_neutromer_spacing:
needed_columns.extend(["nsfn", "frac_new_mzs_std_dev"])
if settings.use_abundance != "No" and settings.use_neutromer_spacing:
needed_columns.extend(
["cfn", "frac_new_combined_std_dev"])
step_object_dict[
analysis_step].required_columns = needed_columns
infile_is_good = self.check_input(
step_object_dict[analysis_step], previous_output_file)
if not infile_is_good: return
# $need to get a graph folder and ensure it exists
# $don't worry about overwriting files
GraphFolder = os.path.join(self.file_loc, "Graph_Folder")
MainGuiObject._make_folder(GraphFolder)
ratecalc = RateCalculator(
model_path=previous_output_file,
out_path=step_object_dict[analysis_step].full_filename,
graph_folder=GraphFolder,
settings_path=rate_settings_file)
ratecalc.calculate()
ratecalc.write()
del ratecalc
QtWidgets.QMessageBox.information(self, "Success",
"Analysis completed successfully")
def _mp_prepare(df, settings_path):
settings.load(settings_path)
#$can start with itertuples. if need be can swap to apply
for row in df.itertuples(index=True):
#$Do any initial filtering to save time calculating
if row.n_value < settings.min_allowed_n_values:
df = FractionNewCalculator._error_method(
df, row, "N value is less than {}".format(
settings.min_allowed_n_values))
continue
if len(row.Sequence) < settings.min_aa_sequence_length:
df = FractionNewCalculator._error_method(
df, row, "Fewer than {} amino acids".format(
settings.min_aa_sequence_length))
continue
#$if the user chooses 0 as enrichment it will cause divide by zero
#$error later, so we will use the settings to force it
#$result should be close to 0 change anyway
if row.enrichment != 0.0:
use_enrich = row.enrichment
else:
use_enrich = settings.enrichement_of_zero
#$not currently considering adducts other than H+ in peptide data
#try:
# num_h, parsed_cf = parse_cf(row.cf_w_adduct)
#except:
num_h, parsed_cf = parse_cf(row.cf)
_, enriched_results = emass(parsed_cf=parsed_cf,
n_list=[0, row.n_value],
n_H=num_h,
low_pct=0,
high_pct=use_enrich,
num_peaks=int(row.num_peaks),
testing=False)
e_mzs, e_abunds = enriched_results
if settings.use_abundance != "No":
FractionNewCalculator._prepare_row(df, row, "abunds", e_abunds)
normalized_empirical_abunds = \
FractionNewCalculator._normalize_abundances(
row.abundances[1:-1].split(", "))
df.at[row.Index, 'normalized_empirical_abundances']= \
normalized_empirical_abunds
theory_abund_deltas = FractionNewCalculator._calculate_deltas(
e_abunds.loc[1][1:], e_abunds.loc[0][1:])
empirical_abund_deltas = FractionNewCalculator._calculate_deltas(
[
float(x)
for x in normalized_empirical_abunds.split(", ")
], e_abunds.loc[0][1:])
theory_abund_deltas, empirical_abund_deltas, removed_peaks = \
FractionNewCalculator._trim_abunds(theory_abund_deltas, empirical_abund_deltas)
df.at[row.Index, "low_labeling_peaks"] = removed_peaks
# TODO: Should this be included?
# df.at[row.Index, 'delta_I'] = np.std(empirical_abund_deltas)
#$don't need to break if we only have one or zero. combined and
#$spacing are still fine, we just shouldn't do the other calculations
#$here
if len(theory_abund_deltas) < 2:
df.at[row.Index, "abund_fn"] = \
f"Insufficient peaks with theory above {settings.minimum_abund_change}"
all_frac_new_abunds = []
else:
all_frac_new_abunds = FractionNewCalculator._calculate_fractions(
empirical_abund_deltas, theory_abund_deltas)
df = FractionNewCalculator.final_calculations(
df, row, "abunds", all_frac_new_abunds, "abund_fn",
theory_abund_deltas[0])
if settings.use_neutromer_spacing:
FractionNewCalculator._prepare_row(df, row, "mzs", e_mzs)
theory_mz_deltas = FractionNewCalculator._calculate_deltas(
FractionNewCalculator._mz_deltas(e_mzs.loc[1][1:]),
FractionNewCalculator._mz_deltas(e_mzs.loc[0][1:]))
#need to trim the parenthesis from the row.mzs string
#also turn it into floats to do math on it
observed_mzs = [float(x) for x in row.mzs[1:-1].split(", ")]
observed_neutral_mass = [y * int(row.z) for y in observed_mzs]
df.at[row.Index, "observed_neutral_masses"] = \
", ".join([str(x) for x in observed_neutral_mass])
empirical_mz_deltas = FractionNewCalculator._calculate_deltas(
FractionNewCalculator._mz_deltas(observed_neutral_mass),
FractionNewCalculator._mz_deltas(e_mzs.loc[0][1:]))
all_frac_new_mzs = FractionNewCalculator._calculate_fractions(
empirical_mz_deltas, theory_mz_deltas)
df = FractionNewCalculator.final_calculations(
df, row, "mzs", all_frac_new_mzs, "nsfn")
if settings.use_neutromer_spacing and settings.use_abundance != "No":
all_frac_new_combined = all_frac_new_abunds + all_frac_new_mzs
df = FractionNewCalculator.final_calculations(
df, row, "combined", all_frac_new_combined, "cfn")
return df
def __init__(self, parent = None, current_setting_file = None):
super(Rate_Setting_Menu, self).__init__(parent)
settings.load(current_setting_file)
self.current_setting_file =current_setting_file
#$this is needed to slim things down a bit
self.setWindowTitle("Rate Settings Menu")
self.setupUi(self)
self.fill_study_type_combobox()
self.all_settings=[
setting_string_info(self.recognize_available_cores, "recognize_available_cores",
settings.recognize_available_cores, True),
setting_numerical_info(self.default_cores, "n_processors",
settings.n_processors, True),
setting_string_info(self.study_type_combobox, "study_type",
settings.study_type, False),
setting_string_info(self.rt_unit, "id_file_rt_unit",
settings.id_file_rt_unit, False),
setting_numerical_info(self.time_window, "time_window",
settings.time_window, False),
setting_numerical_info(self.ppm_error, "ppm_window",
settings.ppm_window, True),
setting_string_info(self.use_abundance, "use_abundance",
settings.use_abundance, False),
setting_string_info(self.use_neutromer_spacing, "use_neutromer_spacing",
settings.use_neutromer_spacing, True),
setting_numerical_info(self.minimum_nonzero_points, "minimum_nonzero_points",
settings.minimum_nonzero_points, True),
setting_string_info(self.roll_up_option,"roll_up_rate_calc",
settings.roll_up_rate_calc, True),
setting_string_info(self.asymptope_type, "asymptote",
settings.asymptote, False),
setting_numerical_info(self.fixed_asymptote_value, "fixed_asymptote_value",
settings.fixed_asymptote_value, False),
setting_numerical_info(self.proliferation_adjustment, "proliferation_adjustment",
settings.proliferation_adjustment, False),
setting_string_info(self.bias_selection_option, "bias_calculation",
settings.bias_calculation, False),
setting_numerical_info(self.abund_manual_bias, "abundance_manual_bias",
settings.abundance_manual_bias, False),
setting_numerical_info(self.spacing_manual_bias, "spacing_manual_bias",
settings.spacing_manual_bias, False),
setting_numerical_info(self.combined_manual_bias, "combined_manual_bias",
settings.combined_manual_bias, False),
setting_numerical_info(self.min_allowed_m0_change, "min_allowed_abund_max_delta",
settings.min_allowed_abund_max_delta, False),
setting_numerical_info(self.min_sequence_length, "min_aa_sequence_length",
settings.min_aa_sequence_length, True),
setting_numerical_info(self.min_n_value, "min_allowed_n_values",
settings.min_allowed_n_values, True),
setting_numerical_info(self.ms_level, "ms_level",
settings.ms_level, True),
setting_string_info(self.verbose_rate, "verbose_rate",
settings.verbose_rate, True),
setting_string_info(self.graph_save_file_type, "rate_output_format",
settings.rate_output_format, False)
]
for setting_object in self.all_settings:
setting_object.set_object_value()
self.SaveButton.clicked.connect(self.save_settings)
self.ExitButton.clicked.connect(self.close)
def _mp_function(data_tuple, settings_path, fn_col,
fn_std_dev, calc_type, manual_bias, std_dev_filter, graph_folder,
rate_eq, max_time, p0):
w.filterwarnings("error")
settings.load(settings_path)
pd.options.mode.chained_assignment = None
id_values, group = data_tuple[0], data_tuple[1]
id_name = id_values[0]
sample_group_name =id_values[1]
common_name = group[settings.peptide_analyte_name_column].iloc[0]
#$drop error string could do earlier for more speed, but this is
#$clearer and allows errors that affect only one calculation type
group = RateCalculator._error_trimmer(
group, [fn_col, fn_std_dev])
#$the copy is just to avoid a SettingWithCopy warning in a few
#$operations. if it causes problems remove and suppress warning
group = group[group[fn_std_dev] < std_dev_filter].copy()
if len(group) == 0:
result = RateCalculator._make_error_message(
"No Isotope Envelopes Agree","", id_name, common_name,
sample_group_name, calc_type, 0, 0, 0, 0)
return result, group
#offset all values by a certain amount (instrument bias)
if settings.bias_calculation == "calculated":
bias = RateCalculator._calc_bias(group, fn_col)
group[fn_col] = group[fn_col] - bias
elif settings.bias_calculation == "manual": #$ user designated bias
group[fn_col] = group[fn_col] - manual_bias
xs = np.concatenate(([0], group['time'].to_numpy()))
ys = np.concatenate(([settings.y_intercept_of_fit],
group[fn_col].to_numpy()))
if settings.roll_up_rate_calc:
xs, ys, devs = RateCalculator._roll(xs, ys)
else:
devs = np.concatenate((
[settings.error_of_zero],
group[fn_std_dev].to_numpy()))
# Get the number of unique time points, and continue if not enough
num_unique_times = len(set(group['time']))
unique_length = len(set(group[settings.unique_sequence_column]))
num_measurements = len(group.index)
#TODO$ this is not ideal but this is a good first attempt
num_files = len(set(group["mzml_path"]))
#TODO: Handle Num Bio Reps in Stuff
# I think this fixes the issues with technical replicates.
#$need to use astype(str) on all or if someone uses numbers for group names or replicate names issues result
num_bio_reps = len(set(group["time"].astype(str) + group["sample_group"].astype(str) + group["bio_rep"].astype(str)))
if num_unique_times < settings.minimum_nonzero_points:
result = RateCalculator._make_error_message(
"Insufficient Timepoints","", id_name, common_name, sample_group_name,
calc_type, num_measurements, num_unique_times, unique_length,
num_files)
return result, group
# perform fit
try:
#$DO NOT use std dev as the Sigma because it creates influential outliers
#$don't use sigma unless we have a different
popt, pcov = curve_fit(
f=rate_eq, xdata=xs, ydata=ys,
p0=p0)
# pull results of fit into variables
rate = popt[0]
asymptote = \
popt[1] if len(popt) > 1 else settings.fixed_asymptote_value
#TODO$ ci uses degrees of freedom = n-k where n is the number of points and k is the number of parameters estimated
#$including intercept in linear regression. if asymptote is fixed k =1 otherwise k =2 (intercept is fit by equation, not data)
#$not counting charge states and different peptides as unique measurements.
#$despite the claim in the documentation, acorrding to statistics consultation and every site I checked, the np.sqrt(np.diag(pcov))[0]
#$is standard error, not std dev, so don't divide by sqrt of n
confint = \
t.ppf(.975, num_files - len(popt)) * \
np.sqrt(np.diag(pcov))[0]
y_predicted = dur.simple(xs, rate, asymptote, settings.proliferation_adjustment)
r_2 = dur.calculate_r2(ys, y_predicted)
result = {
'analyte_id': id_name,
'analyte_name': common_name,
'group_name': sample_group_name,
'{} rate'.format(calc_type) : rate,
'{} asymptote'.format(calc_type) : asymptote,
'{} std_error'.format(calc_type): np.sqrt(np.diag(pcov))[0],
'{} 95pct_confidence'.format(calc_type): confint,
'{} half life'.format(calc_type): RateCalculator._halflife(rate),
'{} R2'.format(calc_type): r_2,
"{} files observed in".format(calc_type): num_files,
'{} num_measurements'.format(calc_type):
num_measurements,
'{} num_time_points'.format(calc_type):
num_unique_times,
'{} uniques'.format(calc_type): unique_length,
'{} exceptions'.format(calc_type): "",
#$'calculation_type': calc_type
}
#$ if there is an asymptote need to provide it
graph_name = "{}_{}_{}".format(id_name, sample_group_name,
fn_col)
graph_title = "{}_{}_{}\nk={}, a={}".format(common_name,
sample_group_name, fn_col, result[f'{calc_type} rate'],
1.0)
if settings.roll_up_rate_calc:
graph_rate(graph_name, xs, ys, rate, asymptote, confint,
rate_eq, graph_folder, max_time,
settings.asymptote, devs, title=graph_title)
else:
graph_rate(graph_name, xs, ys, rate, asymptote, confint,
rate_eq, graph_folder, max_time,
settings.asymptote, title=graph_title)
except Exception as c:
#$"we have a guess but are unsure" warning
if type(c).__name__ == "OptimizeWarning":
current_exception = \
'OptimizeWarning: optimal fit could not be found'
#$couldn't find the minimum
elif type(c).__name__ == "RuntimeError":
current_exception = \
'fit could not be found'
else:
raise c #$will stop here so don't need to consider further
result = RateCalculator._make_error_message(
"value could not be determined", current_exception, id_name,
common_name, sample_group_name, calc_type,num_measurements,
num_unique_times, unique_length, num_files)
return result, group
