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 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 _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 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))
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) )
def test_parse_files(self):
filename = os.path.join(self.datadir_gui, "dataset3.csv")
filename_mzml = os.path.join(self.datadir_gui, "dataset3.mzML")
r = reader.SWATHScoringReader.newReader([filename], "openswath", readmethod="gui", errorHandling="loose")
new_exp = Experiment()
new_exp.runs = r.parse_files(True)
multipeptides = new_exp.get_all_multipeptides(1.0, verbose=False)
# Build map of the PeptideName/Charge to the individual multipeptide
peakgroup_map = {}
mapper.buildPeakgroupMap(multipeptides, peakgroup_map)
self.assertEqual(len(peakgroup_map.keys()), 2)
self.assertEqual(sorted(list(peakgroup_map.keys())), ['testpeptide/0', 'testpeptide/0_pr'])
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 test_parse_files(self):
filename = os.path.join(self.datadir_gui, "dataset3.csv")
filename_mzml = os.path.join(self.datadir_gui, "dataset3.mzML")
r = reader.SWATHScoringReader.newReader([filename],
"openswath",
readmethod="gui",
errorHandling="loose")
new_exp = Experiment()
new_exp.runs = r.parse_files(True)
multipeptides = new_exp.get_all_multipeptides(1.0, verbose=False)
# Build map of the PeptideName/Charge to the individual multipeptide
peakgroup_map = {}
mapper.buildPeakgroupMap(multipeptides, peakgroup_map)
self.assertEqual(len(peakgroup_map.keys()), 2)
self.assertEqual(sorted(list(peakgroup_map.keys())),
['testpeptide/0', 'testpeptide/0_pr'])
class TestAlignment(unittest.TestCase):
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 test_shortestDistance_1(self):
rid = "0_0"
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
dist_matrix = getDistanceMatrix(self.new_exp, self.multipeptides, spl_aligner)
# Select peakgroups, compute left/right border
selected_pg = [pg for p in self.current_mpep1.getAllPeptides() for pg in p.get_all_peakgroups() if pg.get_cluster_id() == 1]
rmap = dict([(r.get_id(),i) for i,r in enumerate(self.new_exp.runs) ])
border_l, border_r = integrationBorderShortestDistance(selected_pg,
rid, self.tr_data, dist_matrix, rmap)
# Direct transformation from 0_2 to 0_0
self.assertAlmostEqual(border_l, self.tr_data.getTrafo("0_2", "0_0").predict([ 240.0 ])[0])
self.assertAlmostEqual(border_r, self.tr_data.getTrafo("0_2", "0_0").predict([ 260.0 ])[0])
self.assertAlmostEqual(border_l, 77.992277992277934)
self.assertAlmostEqual(border_r, 84.1698841699)
def test_shortestPath_1(self):
rid = "0_0"
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
tree = MinimumSpanningTree(getDistanceMatrix(self.new_exp, self.multipeptides, spl_aligner))
tree_mapped = [(self.new_exp.runs[a].get_id(), self.new_exp.runs[b].get_id()) for a,b in tree]
# Select peakgroups, compute left/right border
selected_pg = [pg for p in self.current_mpep1.getAllPeptides() for pg in p.get_all_peakgroups() if pg.get_cluster_id() == 1]
border_l, border_r = integrationBorderShortestPath(selected_pg,
rid, self.tr_data, tree_mapped)
# Direct transformation from 0_2 to 0_0
self.assertAlmostEqual(border_l, self.tr_data.getTrafo("0_2", "0_0").predict([ 240.0 ])[0])
self.assertAlmostEqual(border_r, self.tr_data.getTrafo("0_2", "0_0").predict([ 260.0 ])[0])
self.assertAlmostEqual(border_l, 77.992277992277934)
self.assertAlmostEqual(border_r, 84.1698841699)
def test_reference_1(self):
rid = "0_0"
self.tr_data.reference = "0_2" # set reference run to 0_2
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
tree = MinimumSpanningTree(getDistanceMatrix(self.new_exp, self.multipeptides, spl_aligner))
tree_mapped = [(self.new_exp.runs[a].get_id(), self.new_exp.runs[b].get_id()) for a,b in tree]
# Select peakgroups, compute left/right border
selected_pg = [pg for p in self.current_mpep1.getAllPeptides() for pg in p.get_all_peakgroups() if pg.get_cluster_id() == 1]
border_l, border_r = integrationBorderReference(self.new_exp, selected_pg,
rid, self.tr_data, "median")
# Direct transformation from 0_2 to 0_0
self.assertAlmostEqual(border_l, self.tr_data.getTrafo("0_2", "0_0").predict([ 240.0 ])[0])
self.assertAlmostEqual(border_r, self.tr_data.getTrafo("0_2", "0_0").predict([ 260.0 ])[0])
self.assertAlmostEqual(border_l, 77.992277992277934)
self.assertAlmostEqual(border_r, 84.1698841699)
border_l, border_r = integrationBorderReference(self.new_exp, selected_pg,
rid, self.tr_data, "mean")
self.assertAlmostEqual(border_l, 77.992277992277934)
self.assertAlmostEqual(border_r, 84.1698841699)
border_l, border_r = integrationBorderReference(self.new_exp, selected_pg,
rid, self.tr_data, "max_width")
self.assertAlmostEqual(border_l, 77.992277992277934)
self.assertAlmostEqual(border_r, 84.1698841699)
self.assertRaises(Exception, integrationBorderReference, self.new_exp, selected_pg,
rid, self.tr_data, "dummy")
def test_shortestDistance_2(self):
rid = "0_1"
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
dist_matrix = getDistanceMatrix(self.new_exp, self.multipeptides, spl_aligner)
# Select peakgroups, compute left/right border
selected_pg = [pg for p in self.current_mpep1.getAllPeptides() for pg in p.get_all_peakgroups() if pg.get_cluster_id() == 1]
rmap = dict([(r.get_id(),i) for i,r in enumerate(self.new_exp.runs) ])
border_l, border_r = integrationBorderShortestDistance(selected_pg,
rid, self.tr_data, dist_matrix, rmap)
# Shortest distance means that we transformed directly from 0_2 to 0_1
self.assertAlmostEqual(border_l, self.tr_data.getTrafo("0_2", "0_1").predict([ 240.0 ])[0])
self.assertAlmostEqual(border_r, self.tr_data.getTrafo("0_2", "0_1").predict([ 260.0 ])[0])
self.assertAlmostEqual(border_l, 168.03088803088787)
self.assertAlmostEqual(border_r, 183.32046332)
def test_shortestPath_2(self):
rid = "0_1"
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
tree = MinimumSpanningTree(getDistanceMatrix(self.new_exp, self.multipeptides, spl_aligner))
tree_mapped = [(self.new_exp.runs[a].get_id(), self.new_exp.runs[b].get_id()) for a,b in tree]
# Select peakgroups, compute left/right border
selected_pg = [pg for p in self.current_mpep1.getAllPeptides() for pg in p.get_all_peakgroups() if pg.get_cluster_id() == 1]
border_l, border_r = integrationBorderShortestPath(selected_pg,
rid, self.tr_data, tree_mapped)
# Shortest path means that we transformed from 0_2 to 0_1
self.assertAlmostEqual(border_l, self.tr_data.getTrafo("0_2", "0_1").predict( [ 240.0 ] ))
self.assertAlmostEqual(border_r, self.tr_data.getTrafo("0_2", "0_1").predict( [ 260.0 ] ))
self.assertAlmostEqual(border_l, 168.03088803088787)
self.assertAlmostEqual(border_r, 183.32046332046318)
def test_shortestPath_3(self):
rid = "0_1"
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
tree = MinimumSpanningTree(getDistanceMatrix(self.new_exp, self.multipeptides, spl_aligner))
tree_mapped = [(self.new_exp.runs[a].get_id(), self.new_exp.runs[b].get_id()) for a,b in tree]
# Select peakgroups, compute left/right border
selected_pg = [pg for p in self.current_mpep2.getAllPeptides() for pg in p.get_all_peakgroups() if pg.get_cluster_id() == 1]
border_l, border_r = integrationBorderShortestPath(selected_pg,
rid, self.tr_data, tree_mapped)
# Shortest path means that we transformed from 0_0 to 0_2 and then to 0_1
self.assertAlmostEqual(border_l,
self.tr_data.getTrafo("0_2", "0_1").predict(
self.tr_data.getTrafo("0_0", "0_2").predict([ 600 ])
))
self.assertAlmostEqual(border_r,
self.tr_data.getTrafo("0_2", "0_1").predict(
self.tr_data.getTrafo("0_0", "0_2").predict([ 700 ])
))
self.assertAlmostEqual(border_l, 1452.355212355212)
self.assertAlmostEqual(border_r, 1696.9884169884167)
def test_reference_2(self):
rid = "0_1"
self.tr_data.reference = "0_0" # set reference run to 0_0
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
tree = MinimumSpanningTree(getDistanceMatrix(self.new_exp, self.multipeptides, spl_aligner))
tree_mapped = [(self.new_exp.runs[a].get_id(), self.new_exp.runs[b].get_id()) for a,b in tree]
# Select peakgroups, compute left/right border
selected_pg = [pg for p in self.current_mpep1.getAllPeptides() for pg in p.get_all_peakgroups() if pg.get_cluster_id() == 1]
border_l, border_r = integrationBorderReference(self.new_exp, selected_pg,
rid, self.tr_data, "median")
# Reference 0_0 means that we transformed from 0_2 to 0_0 and then to 0_1
self.assertAlmostEqual(border_l,
self.tr_data.getTrafo("0_0", "0_1").predict(
self.tr_data.getTrafo("0_2", "0_0").predict([ 240.0 ])
))
self.assertAlmostEqual(border_r,
self.tr_data.getTrafo("0_0", "0_1").predict(
self.tr_data.getTrafo("0_2", "0_0").predict([ 260.0 ])
))
self.assertAlmostEqual(border_l, 187.18146718146681)
self.assertAlmostEqual(border_r, 202.00772200772167)
class MatrixOutputWriters(unittest.TestCase):
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 test_matrix_out_1(self):
"""Test the output matrix writers"""
import msproteomicstoolslib.algorithms.alignment.AlignmentHelper as helper
tmpfile = "tmp.output.csv"
helper.write_out_matrix_file(tmpfile, self.exp.runs, self.multipeptides, 0.0,
style="full", write_requant=False)
os.remove(tmpfile)
tmpfile = "tmp.output.tsv"
helper.write_out_matrix_file(tmpfile, self.exp.runs, self.multipeptides, 0.0,
style="full", write_requant=False)
os.remove(tmpfile)
tmpfile = "tmp.output.xls"
helper.write_out_matrix_file(tmpfile, self.exp.runs, self.multipeptides, 0.0,
style="full", write_requant=False)
os.remove(tmpfile)
tmpfile = "tmp.output.xlsx"
helper.write_out_matrix_file(tmpfile, self.exp.runs, self.multipeptides, 0.0,
style="full", write_requant=False)
os.remove(tmpfile)
def test_matrix_out_2(self):
"""Test the output matrix writers"""
import msproteomicstoolslib.algorithms.alignment.AlignmentHelper as helper
runs = self.exp2.runs
multipeptides = self.multipeptides2
tmpfile = "tmp.output.csv"
helper.write_out_matrix_file(tmpfile, runs, multipeptides, 0.0,
style="full", write_requant=False)
os.remove(tmpfile)
tmpfile = "tmp.output.tsv"
helper.write_out_matrix_file(tmpfile, runs, multipeptides, 0.0,
style="full", write_requant=False)
os.remove(tmpfile)
tmpfile = "tmp.output.xls"
helper.write_out_matrix_file(tmpfile, runs, multipeptides, 0.0,
style="full", write_requant=False)
os.remove(tmpfile)
tmpfile = "tmp.output.xlsx"
helper.write_out_matrix_file(tmpfile, runs, multipeptides, 0.0,
style="full", write_requant=False)
os.remove(tmpfile)
def test_matrix_out_3(self):
"""Test the output matrix writers"""
import msproteomicstoolslib.algorithms.alignment.AlignmentHelper as helper
runs = self.exp2.runs
multipeptides = self.multipeptides2[0]
# Try to mess up the assumption of one selected peakgroup per run
pep1 = multipeptides.getAllPeptides()[0]
pep2 = multipeptides.getAllPeptides()[0]
for pg in pep1.peakgroups:
pg.select_this_peakgroup()
for pg in pep2.peakgroups:
pg.peptide = pep1
pg.select_this_peakgroup()
tmpfile = "tmp.output.csv"
self.assertRaises(Exception, helper.write_out_matrix_file, tmpfile, runs, [ multipeptides ] , 0.0,
style="full", write_requant=False)
os.remove(tmpfile)
