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 _read_peakgroup_files(self, aligned_pg_files, swathfiles):
"""
The peakgroup files have to have the following columns:
- FullPeptideName
- Charge
- leftWidth
- rightWidth
- m_score
- Intensity
- align_runid
- transition_group_id
"""
# Read in the peakgroup files, parse them and map across runs
reader = SWATHScoringReader.newReader(aligned_pg_files,
"openswath",
readmethod="gui",
errorHandling="loose")
new_exp = Experiment()
new_exp.runs = reader.parse_files(REALIGN_RUNS)
multipeptides = new_exp.get_all_multipeptides(FDR_CUTOFF,
verbose=False)
# Build map of the PeptideName/Charge to the individual multipeptide
peakgroup_map = {}
for m in multipeptides:
pg = m.find_best_peptide_pg()
identifier = pg.get_value("FullPeptideName") + "/" + pg.get_value(
"Charge")
peakgroup_map[identifier] = m
for swathrun in swathfiles.getSwathFiles():
if ONLY_SHOW_QUANTIFIED:
intersection = set(
swathrun.get_all_precursor_ids()).intersection(
peakgroup_map.keys())
todelete = set(
swathrun.get_all_precursor_ids()).difference(intersection)
if len(intersection) == 0:
print "Could not find any intersection between identifiers in your transition file and the provided chromatograms"
print len(intersection)
swathrun.remove_precursors(todelete)
# for each precursor in this run, identify the best peakgroup and store the value
for precursor_id in swathrun.get_all_precursor_ids():
if not peakgroup_map.has_key(precursor_id):
continue
m = peakgroup_map[precursor_id]
if m.hasPrecursorGroup(swathrun.runid):
for pg in m.getPrecursorGroup(
swathrun.runid).getAllPeakgroups():
l, r = [
float(pg.get_value("leftWidth")),
float(pg.get_value("rightWidth"))
]
fdrscore = float(pg.get_value("m_score"))
intensity = float(pg.get_value("Intensity"))
swathrun.add_peakgroup_data(precursor_id, l, r,
fdrscore, intensity)
def _read_peakgroup_files(self, aligned_pg_files, swathfiles):
"""
The peakgroup files have to have the following columns:
- FullPeptideName
- Charge
- leftWidth
- rightWidth
- m_score
- Intensity
- align_runid
- transition_group_id
"""
# Read in the peakgroup files, parse them and map across runs
reader = SWATHScoringReader.newReader(aligned_pg_files, "openswath", readmethod="gui", errorHandling="loose")
new_exp = Experiment()
new_exp.runs = reader.parse_files(REALIGN_RUNS)
multipeptides = new_exp.get_all_multipeptides(FDR_CUTOFF, verbose=False)
# Build map of the PeptideName/Charge to the individual multipeptide
peakgroup_map = {}
for m in multipeptides:
pg = m.find_best_peptide_pg()
identifier = pg.get_value("FullPeptideName") + "/" + pg.get_value("Charge")
peakgroup_map[ identifier ] = m
for swathrun in swathfiles.getSwathFiles():
if ONLY_SHOW_QUANTIFIED:
intersection = set(swathrun.get_all_precursor_ids()).intersection( peakgroup_map.keys() )
todelete = set(swathrun.get_all_precursor_ids()).difference(intersection)
if len(intersection) == 0:
print "Could not find any intersection between identifiers in your transition file and the provided chromatograms"
print len(intersection)
swathrun.remove_precursors(todelete)
# for each precursor in this run, identify the best peakgroup and store the value
for precursor_id in swathrun.get_all_precursor_ids():
if not peakgroup_map.has_key(precursor_id):
continue
m = peakgroup_map[ precursor_id ]
if m.hasPrecursorGroup(swathrun.runid):
for pg in m.getPrecursorGroup(swathrun.runid).getAllPeakgroups():
l,r = [ float(pg.get_value("leftWidth")), float(pg.get_value("rightWidth")) ]
fdrscore = float(pg.get_value("m_score"))
intensity = float(pg.get_value("Intensity"))
swathrun.add_peakgroup_data(precursor_id,l,r, fdrscore, intensity)
def setUpClass(cls):
from msproteomicstoolslib.format.SWATHScoringReader import SWATHScoringReader
cls.dirname = os.path.dirname(os.path.abspath(__file__))
cls.topdir = os.path.join(os.path.join(cls.dirname, ".."), "..")
cls.datadir = os.path.join(os.path.join(cls.topdir, "test"), "data")
cls.datadir_DIAlign = os.path.join(cls.datadir, "DIAlign")
filename = os.path.join(cls.datadir_DIAlign, "merged.osw")
r = SWATHScoringReader.newReader([filename], "openswath", "minimal")
runs = r.parse_files(read_exp_RT=False)
from analysis.alignment.feature_alignment import Experiment
this_exp = Experiment()
this_exp.set_runs(runs)
cls.best_run = this_exp.determine_best_run(
alignment_fdr_threshold=0.05)
cls.mp = this_exp.get_all_multipeptides(0.05, verbose=False)
