def setUp(self):
# Set up dirs
self.dirname = os.path.dirname(os.path.abspath(__file__))
self.topdir = os.path.join(os.path.join(self.dirname, ".."), "..")
self.datadir = os.path.join(os.path.join(self.topdir, "test"), "data")
self.scriptdir = os.path.join(self.topdir, "analysis")
# Set up files
peakgroups_file = os.path.join(self.datadir, "imputeValues/imputeValues_5_input.csv")
fdr_cutoff_all_pg = 1.0
# Read input
reader = SWATHScoringReader.newReader([peakgroups_file], "openswath", readmethod="complete")
self.exp = MRExperiment()
self.exp.runs = reader.parse_files()
self.multipeptides = self.exp.get_all_multipeptides(fdr_cutoff_all_pg, verbose=False)
# Set up files nr2
peakgroups_file = os.path.join(self.datadir, "feature_alignment_7_openswath_input.csv")
reader = SWATHScoringReader.newReader([peakgroups_file], "openswath", readmethod="complete")
self.exp2 = MRExperiment()
self.exp2.runs = reader.parse_files()
self.multipeptides2 = self.exp2.get_all_multipeptides(fdr_cutoff_all_pg, verbose=False)
# Select the best peakgroup per peptide and select it for writing out
fdr_cutoff = 0.01
for mpep in self.multipeptides2:
for prgr in mpep.getAllPeptides():
minpg = min( [(pg.get_fdr_score(), pg) for pg in prgr.peakgroups] )
if minpg[0] < fdr_cutoff:
minpg[1].select_this_peakgroup()
def setUp(self):
# Set up dirs
self.dirname = os.path.dirname(os.path.abspath(__file__))
self.topdir = os.path.join(os.path.join(self.dirname, ".."), "..")
self.datadir = os.path.join(os.path.join(self.topdir, "test"), "data")
self.scriptdir = os.path.join(self.topdir, "analysis")
# Set up files
peakgroups_file = os.path.join(self.datadir, "imputeValues/imputeValues_5_input.csv")
mzml_file = os.path.join(self.datadir, "imputeValues/r004_small/split_olgas_otherfile.chrom.mzML")
# Parameters
self.initial_alignment_cutoff = 0.0001
fdr_cutoff_all_pg = 1.0
max_rt_diff = 30
# Read input
reader = SWATHScoringReader.newReader([peakgroups_file], "openswath", readmethod="complete")
self.new_exp = MRExperiment()
self.new_exp.runs = reader.parse_files()
self.multipeptides = self.new_exp.get_all_multipeptides(fdr_cutoff_all_pg, verbose=False)
# Align all against all
self.tr_data = transformations.LightTransformationData()
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
for run_0 in self.new_exp.runs:
for run_1 in self.new_exp.runs:
helper.addDataToTrafo(self.tr_data, run_0, run_1, spl_aligner, self.multipeptides, "linear", 30)
# Select two interesting peptides
pepname = "21517_C[160]NVVISGGTGSGK/2_run0 0 0"
self.current_mpep1 = [m for m in self.multipeptides if m.getAllPeptides()[0].get_id() == pepname][0]
pepname = "26471_GYEDPPAALFR/2_run0 0 0"
self.current_mpep2 = [m for m in self.multipeptides if m.getAllPeptides()[0].get_id() == pepname][0]
def main(options):
import time
# Read the files
start = time.time()
reader = SWATHScoringReader.newReader(options.infiles, options.file_format,
options.readmethod)
runs = reader.parse_files(True)
# Create experiment
this_exp = MRExperiment()
this_exp.set_runs(runs)
print("Reading the input files took %ss" % (time.time() - start))
# Fix input filenames
fix_input_fnames(options, runs)
# Map the precursors across multiple runs, determine the number of
# precursors in all runs without alignment.
start = time.time()
multipeptides = this_exp.get_all_multipeptides(1.0, verbose=True)
print("Mapping the precursors took %ss" % (time.time() - start))
for m in multipeptides:
# Error handling if somehow more than one peakgroup was selected ...
for p in m.getAllPeptides():
p._fixSelectedPGError(fixMethod="BestScore")
if len(m.get_selected_peakgroups()) > 0:
continue
for p in m.get_peptides():
if len(list(p.get_all_peakgroups())) != 1:
print(p)
print(dir(p))
print(p.get_run_id())
for pg in p.get_all_peakgroups():
print(pg.print_out())
print(len(list(p.get_all_peakgroups())))
assert len(list(p.get_all_peakgroups())) == 1
for pg in p.get_all_peakgroups():
pg.select_this_peakgroup()
start = time.time()
if len(options.matrix_outfile) > 0:
write_out_matrix_file(
options.matrix_outfile,
this_exp.runs,
multipeptides,
options.min_frac_selected,
style=options.output_method,
write_requant=not options.remove_requant_values,
aligner_mscore_treshold=options.aligner_mscore_threshold)
print("Writing output took %ss" % (time.time() - start))
