def spectrum_match_info(self, glycopeptide):
spectrum_match_ref = glycopeptide.best_spectrum_match
scan_id = spectrum_match_ref.scan.scan_id
scan = self.scan_loader.get_scan_by_id(scan_id)
try:
mass_shift = spectrum_match_ref.mass_shift
except Exception:
mass_shift = Unmodified
if mass_shift.name != Unmodified.name:
mass_shift = mass_shift.convert()
else:
mass_shift = Unmodified
match = CoverageWeightedBinomialScorer.evaluate(
scan,
glycopeptide.structure.convert(),
error_tolerance=self.analysis.
parameters["fragment_error_tolerance"],
mass_shift=mass_shift)
specmatch_artist = TidySpectrumMatchAnnotator(match, ax=figax())
specmatch_artist.draw(fontsize=10, pretty=True)
annotated_match_ax = specmatch_artist.ax
scan_title = scan.id
if len(scan_title) > 60:
scan_title = '\n'.join(textwrap.wrap(scan_title, 60))
annotated_match_ax.set_title(scan_title, fontsize=18)
annotated_match_ax.set_ylabel(annotated_match_ax.get_ylabel(),
fontsize=16)
annotated_match_ax.set_xlabel(annotated_match_ax.get_xlabel(),
fontsize=16)
sequence_logo_plot = glycopeptide_match_logo(match, ax=figax())
xlim = list(sequence_logo_plot.get_xlim())
xlim[0] += 1
sequence_logo_plot.set_xlim(xlim[0], xlim[1])
spectrum_plot = png_plot(annotated_match_ax,
svg_width="100%",
bbox_inches='tight',
height=3 * 1.5,
width=8 * 1.5,
img_width="100%",
patchless=True)
logo_plot = png_plot(sequence_logo_plot,
svg_width="100%",
img_width="100%",
xml_transform=scale_fix_xml_transform,
bbox_inches='tight',
height=2,
width=6 * 1.5,
patchless=True)
return dict(spectrum_plot=spectrum_plot,
logo_plot=logo_plot,
precursor_mass_accuracy=match.precursor_mass_accuracy(),
spectrum_match=match)
def chromatogram_figures(chroma):
figures = []
plot = SmoothingChromatogramArtist([chroma],
colorizer=lambda *a, **k: 'green',
ax=figax()).draw(
label_function=lambda *a, **k: "",
legend=False).ax
plot.set_title("Aggregated\nExtracted Ion Chromatogram", fontsize=24)
chroma_svg = svguri_plot(plot,
bbox_inches='tight',
height=5,
width=9,
svg_width="100%")
figures.append(chroma_svg)
if len(chroma.adducts) > 1:
adducts = list(chroma.adducts)
labels = {}
rest = chroma
for adduct in adducts:
with_adduct, rest = rest.bisect_adduct(adduct)
labels[adduct] = with_adduct
adduct_plot = SmoothingChromatogramArtist(
labels.values(), colorizer=lambda *a, **k: 'green',
ax=figax()).draw(
label_function=lambda *a, **k: tuple(a[0].adducts)[0].name,
legend=False).ax
adduct_plot.set_title("Adduct-Separated\nExtracted Ion Chromatogram",
fontsize=24)
adduct_separation = svguri_plot(adduct_plot,
bbox_inches='tight',
height=5,
width=9,
svg_width="100%")
figures.append(adduct_separation)
if len(chroma.charge_states) > 1:
charge_separating_plot = ChargeSeparatingSmoothingChromatogramArtist(
[chroma], ax=figax()).draw(label_function=lambda x, *a, **kw: str(
tuple(x.charge_states)[0]),
legend=False).ax
charge_separating_plot.set_title(
"Charge-Separated\nExtracted Ion Chromatogram", fontsize=24)
charge_separation = svguri_plot(charge_separating_plot,
bbox_inches='tight',
height=5,
width=9,
svg_width="100%")
figures.append(charge_separation)
return figures
def draw_glycoforms(self, glycoprotein):
ax = figax()
layout = GlycoformLayout(glycoprotein,
glycoprotein.identified_glycopeptides,
ax=ax)
layout.draw()
svg = layout.to_svg(scale=2.0, height_padding_scale=1.1)
return svg
def chromatogram_plot(self, glycopeptide):
ax = figax()
try:
SmoothingChromatogramArtist(
glycopeptide, ax=ax, label_peaks=False,
colorizer=lambda x: "#48afd0").draw(legend=False)
ax.set_xlabel("Time (Minutes)", fontsize=16)
ax.set_ylabel("Relative Abundance", fontsize=16)
return png_plot(ax, bbox_inches='tight', img_height='100%')
except ValueError:
return "<div style='text-align:center;'>No Chromatogram Found</div>"
def draw_glycoforms(self, glycoprotein):
ax = figax()
layout = GlycoformLayout(glycoprotein,
glycoprotein.identified_glycopeptides,
ax=ax)
layout.draw()
svg = layout.to_svg(scale=2.0, height_padding_scale=1.1)
# svg = plot_glycoforms_svg(
# glycoprotein, glycoprotein.identified_glycopeptides, ax=ax,
# margin_left=85, margin_top=0, height_padding_scale=1.1)
return svg
def render_chromatograms(reader):
acc = []
for scan_id in reader.extended_index.ms1_ids:
header = reader.get_scan_header_by_id(scan_id)
acc.extend(header.arrays[1])
threshold = np.percentile(acc, 90)
ex = ChromatogramExtractor(reader,
minimum_intensity=threshold,
minimum_mass=300)
chroma = ex.run()
window_width = 0.01
ax = figax()
a = SmoothingChromatogramArtist(list(chroma) + [ex.total_ion_chromatogram],
ax=ax,
colorizer=lambda *a, **k: 'lightblue')
a.draw(label_function=lambda *a, **kw: "")
rt, intens = ex.total_ion_chromatogram.as_arrays()
a.draw_generic_chromatogram("TIC", rt, intens, 'lightblue')
a.ax.set_ylim(0, max(intens) * 1.1)
if reader.extended_index.msn_ids:
ox_time = []
ox_current = []
for scan_id in reader.extended_index.msn_ids:
scan = reader.get_scan_header_by_id(scan_id)
mz, intens = scan.arrays
total = 0
for ion in standard_oxonium_ions:
coords = sweep(mz, ion.mass() + 1.007, window_width)
total += intens[coords].sum()
ox_time.append(scan.scan_time)
ox_current.append(total)
oxonium_axis = ax.twinx()
stub = SimpleChromatogram(ex.total_ion_chromatogram.time_converter)
for key in ex.total_ion_chromatogram:
stub[key] = 0
oxonium_artist = SmoothingChromatogramArtist(
[stub], ax=oxonium_axis).draw(label_function=lambda *a, **kw: "")
rt, intens = ox_time, ox_current
oxonium_axis.set_ylim(0, max(intens) * 1.1)
oxonium_axis.yaxis.tick_right()
oxonium_axis.axes.spines['right'].set_visible(True)
oxonium_axis.set_ylabel("Oxonium Abundance", fontsize=18)
oxonium_artist.draw_generic_chromatogram("Oxonium Ions", rt, intens,
'green')
fig = a.ax.get_figure()
fig.set_figwidth(10)
return png_plot(ax, patchless=True, bbox_inches='tight')
def draw_lcms_map(self):
if self.abundance_threshold is None:
self._estimate_threshold()
ax = figax()
artist = LCMSMapArtist.from_peak_loader(
self.reader, threshold=self.abundance_threshold / 2., ax=ax)
artist.draw()
return png_plot(ax,
patchless=True,
bbox_inches='tight',
width=10,
height=10)
def chromatogram_plot(self, glycopeptide):
ax = figax()
try:
SmoothingChromatogramArtist(
glycopeptide,
ax=ax,
label_peaks=False,
colorizer=lambda x: "#48afd0").draw(legend=False)
ax.set_xlabel("Time (Minutes)", fontsize=16)
ax.set_ylabel("Relative Abundance", fontsize=16)
return png_plot(ax, bbox_inches='tight', img_height='100%')
except ValueError:
return "<div style='text-align:center;'>No Chromatogram Found</div>"
def chromatogram_figures(chroma):
figures = []
plot = SmoothingChromatogramArtist(
[chroma], colorizer=lambda *a, **k: 'green', ax=figax()).draw(
label_function=lambda *a, **k: "", legend=False).ax
plot.set_title("Aggregated\nExtracted Ion Chromatogram", fontsize=24)
chroma_svg = svguri_plot(
plot, bbox_inches='tight', height=5, width=9, svg_width="100%")
figures.append(chroma_svg)
if len(chroma.mass_shifts) > 1:
mass_shifts = list(chroma.mass_shifts)
labels = {}
rest = chroma
for mass_shift in mass_shifts:
with_mass_shift, rest = rest.bisect_mass_shift(mass_shift)
labels[mass_shift] = with_mass_shift
mass_shift_plot = SmoothingChromatogramArtist(
labels.values(),
colorizer=lambda *a, **k: 'green', ax=figax()).draw(
label_function=lambda *a, **k: tuple(a[0].mass_shifts)[0].name,
legend=False).ax
mass_shift_plot.set_title(
"mass_shift-Separated\nExtracted Ion Chromatogram", fontsize=24)
mass_shift_separation = svguri_plot(
mass_shift_plot, bbox_inches='tight', height=5, width=9, svg_width="100%")
figures.append(mass_shift_separation)
if len(chroma.charge_states) > 1:
charge_separating_plot = ChargeSeparatingSmoothingChromatogramArtist(
[chroma], ax=figax()).draw(
label_function=lambda x, *a, **kw: str(
tuple(x.charge_states)[0]), legend=False).ax
charge_separating_plot.set_title(
"Charge-Separated\nExtracted Ion Chromatogram", fontsize=24)
charge_separation = svguri_plot(
charge_separating_plot, bbox_inches='tight', height=5, width=9,
svg_width="100%")
figures.append(charge_separation)
return figures
def draw_chromatograms(self):
if self.chromatograms is None:
self.build_chromatograms()
ax = figax()
chromatograms = list(self.chromatograms)
if len(chromatograms):
chromatograms.append(self.total_ion_chromatogram)
chromatograms = [
chrom for chrom in chromatograms if len(chrom) > 0
]
a = SmoothingChromatogramArtist(
chromatograms, ax=ax, colorizer=lambda *a, **k: 'lightblue')
a.draw(label_function=lambda *a, **kw: "")
rt, intens = self.total_ion_chromatogram.as_arrays()
a.draw_generic_chromatogram("TIC", rt, intens, 'lightblue')
a.ax.set_ylim(0, max(intens) * 1.1)
chromatogram_artist = a
fig = chromatogram_artist.ax.get_figure()
fig.set_figwidth(10)
fig.set_figheight(5)
# if self.reader.extended_index.msn_ids:
# oxonium_axis = ax.twinx()
# stub = SimpleChromatogram(
# self.total_ion_chromatogram.time_converter)
# for key in self.total_ion_chromatogram:
# stub[key] = 0
# oxonium_ion_artist = SmoothingChromatogramArtist(
# [stub],
# ax=oxonium_axis).draw(
# label_function=lambda *a, **kw: "")
# rt, intens = self.oxonium_ion_chromatogram
# oxonium_axis.set_ylim(0, max(intens) * 1.1)
# oxonium_axis.yaxis.tick_right()
# oxonium_axis.axes.spines['right'].set_visible(True)
# oxonium_axis.set_ylabel("Oxonium Abundance", fontsize=18)
# oxonium_ion_artist.draw_generic_chromatogram(
# "Oxonium Ions", rt, intens, 'green')
else:
ax.text(0.5,
0.5,
"No chromatograms extracted",
ha='center',
fontsize=16)
ax.axis('off')
return png_plot(ax,
patchless=True,
bbox_inches='tight',
width=12,
height=8)
def site_specific_abundance_plots(self, glycoprotein):
axes = OrderedDict()
for glyco_type in glycoprotein.glycosylation_types:
for site in sorted(glycoprotein.glycosylation_sites_for(glyco_type)):
spanning_site = glycoprotein.site_map[glyco_type][site]
if len(spanning_site) == 0:
continue
bundle = BundledGlycanComposition.aggregate(spanning_site)
if len(bundle) == 0:
continue
ax = figax()
AggregatedAbundanceArtist(
bundle, ax=ax, colorizer=glycan_colorizer_type_map[glyco_type]).draw()
ax.set_title("%s Glycans\nat Site %d" % (glyco_type.name, site + 1,), fontsize=18)
axes[site, glyco_type] = svguri_plot(ax, bbox_inches='tight')
return axes
def site_specific_abundance_plots(self, glycoprotein):
axes = OrderedDict()
for glyco_type in glycoprotein.glycosylation_types:
for site in sorted(
glycoprotein.glycosylation_sites_for(glyco_type)):
spanning_site = glycoprotein.site_map[glyco_type][site]
if len(spanning_site) == 0:
continue
bundle = BundledGlycanComposition.aggregate(spanning_site)
ax = figax()
AggregatedAbundanceArtist(
bundle,
ax=ax,
colorizer=glycan_colorizer_type_map[glyco_type]).draw()
ax.set_title("%s Glycans\nat Site %d" % (
glyco_type.name,
site,
),
fontsize=18)
axes[site, glyco_type] = svguri_plot(ax, bbox_inches='tight')
return axes
def spectrum_match_info(self, glycopeptide):
matched_scans = []
for solution_set in glycopeptide.spectrum_matches:
best_solution = solution_set.best_solution()
try:
selected_solution = solution_set.solution_for(glycopeptide.structure)
except KeyError:
continue
pass_threshold = abs(selected_solution.score - best_solution.score) < 1e-6
if not pass_threshold:
continue
if isinstance(selected_solution.scan, SpectrumReference):
scan = self.session.query(MSScan).filter(
MSScan.scan_id == selected_solution.scan.id,
MSScan.sample_run_id == self.analysis.sample_run_id).first().convert()
else:
scan = selected_solution.scan
scan.score = selected_solution.score
matched_scans.append(scan)
spectrum_match_ref = max(glycopeptide.spectrum_matches, key=lambda x: x.score)
scan_id = spectrum_match_ref.scan.scan_id
scan = self.scan_loader.get_scan_by_id(scan_id)
try:
mass_shift = spectrum_match_ref[0].mass_shift
except Exception:
mass_shift = Unmodified
if mass_shift.name != Unmodified.name:
mass_shift = mass_shift.convert()
else:
mass_shift = Unmodified
match = CoverageWeightedBinomialScorer.evaluate(
scan, glycopeptide.structure.convert(),
error_tolerance=self.analysis.parameters["fragment_error_tolerance"],
mass_shift=mass_shift)
specmatch_artist = TidySpectrumMatchAnnotator(match, ax=figax())
specmatch_artist.draw(fontsize=10, pretty=True)
annotated_match_ax = specmatch_artist.ax
scan_title = scan.id
if len(scan_title) > 60:
scan_title = '\n'.join(textwrap.wrap(scan_title, 60))
annotated_match_ax.set_title(scan_title, fontsize=18)
annotated_match_ax.set_ylabel(annotated_match_ax.get_ylabel(), fontsize=16)
annotated_match_ax.set_xlabel(annotated_match_ax.get_xlabel(), fontsize=16)
sequence_logo_plot = glycopeptide_match_logo(match, ax=figax())
xlim = list(sequence_logo_plot.get_xlim())
xlim[0] += 1
sequence_logo_plot.set_xlim(xlim[0], xlim[1])
spectrum_plot = png_plot(
annotated_match_ax, svg_width="100%", bbox_inches='tight', height=3 * 1.5,
width=8 * 1.5,
img_width="100%",
patchless=True)
logo_plot = png_plot(
sequence_logo_plot,
svg_width="100%",
img_width="100%",
xml_transform=scale_fix_xml_transform,
bbox_inches='tight',
height=2, width=6 * 1.5, patchless=True)
return dict(
spectrum_plot=spectrum_plot, logo_plot=logo_plot,
precursor_mass_accuracy=match.precursor_mass_accuracy(),
spectrum_match=match)
def draw_glycoforms(self, glycoprotein):
ax = figax()
layout = GlycoformLayout(glycoprotein, glycoprotein.identified_glycopeptides, ax=ax)
layout.draw()
svg = layout.to_svg(scale=2.0, height_padding_scale=1.1)
return svg
def spectrum_match_info(self, glycopeptide):
matched_scans = []
for solution_set in glycopeptide.spectrum_matches:
best_solution = solution_set.best_solution()
try:
selected_solution = solution_set.solution_for(
glycopeptide.structure)
except KeyError:
continue
pass_threshold = abs(selected_solution.score -
best_solution.score) < 1e-6
if not pass_threshold:
continue
if isinstance(selected_solution.scan, SpectrumReference):
scan = self.session.query(MSScan).filter(
MSScan.scan_id == selected_solution.scan.id,
MSScan.sample_run_id ==
self.analysis.sample_run_id).first().convert()
else:
scan = selected_solution.scan
scan.score = selected_solution.score
matched_scans.append(scan)
spectrum_match_ref = max(glycopeptide.spectrum_matches,
key=lambda x: x.score)
scan_id = spectrum_match_ref.scan.scan_id
scan = self.scan_loader.get_scan_by_id(scan_id)
match = CoverageWeightedBinomialScorer.evaluate(
scan,
glycopeptide.structure.convert(),
error_tolerance=self.analysis.
parameters["fragment_error_tolerance"])
specmatch_artist = SpectrumMatchAnnotator(match, ax=figax())
specmatch_artist.draw(fontsize=10, pretty=True)
annotated_match_ax = specmatch_artist.ax
annotated_match_ax.set_title("%s\n" % (scan.id, ), fontsize=18)
annotated_match_ax.set_ylabel(annotated_match_ax.get_ylabel(),
fontsize=16)
annotated_match_ax.set_xlabel(annotated_match_ax.get_xlabel(),
fontsize=16)
sequence_logo_plot = glycopeptide_match_logo(match, ax=figax())
xlim = list(sequence_logo_plot.get_xlim())
xlim[0] += 1
sequence_logo_plot.set_xlim(xlim[0], xlim[1])
spectrum_plot = png_plot(annotated_match_ax,
svg_width="100%",
bbox_inches='tight',
height=3 * 1.5,
width=8 * 1.5,
img_width="100%",
patchless=True)
logo_plot = png_plot(sequence_logo_plot,
svg_width="100%",
img_width="100%",
xml_transform=scale_fix_xml_transform,
bbox_inches='tight',
height=2,
width=6 * 1.5,
patchless=True)
return dict(spectrum_plot=spectrum_plot,
logo_plot=logo_plot,
precursor_mass_accuracy=match.precursor_mass_accuracy(),
spectrum_match=match)
