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Merge pull request #87 from WangHong007/main
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Enable Sage search engine scores
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@ypriverol
ypriverol authored Sep 30, 2023
2 parents 008355e + cde2dac commit fda6118
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Showing 2 changed files with 53 additions and 9 deletions.
60 changes: 52 additions & 8 deletions pmultiqc/modules/quantms/quantms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,6 +110,7 @@ def __init__(self):
self.mzml_table = OrderedDict()
self.search_engine = OrderedDict()
self.XCORR_HIST_RANGE = {'start': 0, 'end': 5, 'step': 0.1}
self.HYPER_HIST_RANGE = {'start': 0, 'end': 5, 'step': 0.1}
self.SPECEVALUE_HIST_RANGE = {'start': 0, 'end': 20, 'step': 0.4}
self.PEP_HIST_RANGE = {'start': 0, 'end': 1, 'step': 0.02}
self.total_ms2_spectra = 0
Expand Down Expand Up @@ -584,6 +585,11 @@ def draw_mzml_ms(self):
'description': 'Number of spectra identified by Comet search engine',
'color': "#ffffff"
}
if any(['Sage' in v for k,v in self.mzml_table.items()]):
headers['Sage'] = {
'description': 'Number of spectra identified by Sage search engine',
'color': "#ffffff"
}
headers['num_quant_psms'] = {
'title': '#PSMs from quant. peptides',
'description': 'Number of reliable PSMs from peptides IDs used in quantification',
Expand All @@ -610,6 +616,7 @@ def draw_mzml_ms(self):
* MS2_Num: The number of MS2 spectra extracted from mzMLs
* MSGF: The Number of spectra identified by MSGF search engine
* Comet: The Number of spectra identified by Comet search engine
* Sage: The Number of spectra identified by Sage search engine
* PSMs from quant. peptides: extracted from PSM table in mzTab file
* Peptides quantified: extracted from PSM table in mzTab file
''',
Expand Down Expand Up @@ -800,7 +807,7 @@ def draw_search_engine(self):
'''
)
# Create scores summary plot
[MSGF_labels, Comet_labels] = self.search_engine['data_label']['score_label']
[MSGF_labels, Comet_labels, Sage_labels] = self.search_engine['data_label']['score_label']
SpecE_pconfig = {
'id': 'search_scores_summary', # ID used for the table
'cpswitch': False,
Expand All @@ -825,24 +832,39 @@ def draw_search_engine(self):
'data_labels': Comet_labels,
}

hyper_pconfig = {
'id': 'search_scores_summary', # ID used for the table
'cpswitch': False,
'title': 'Summary of Hyperscore',
'xlab': 'Sage hyperscore ranges',
'stacking': True,
'height': 550,
'tt_percentages': True,
'tt_decimals': 0,
'data_labels': Sage_labels,
}

bar_cats = OrderedDict()
bar_cats['target'] = {'name': 'target', 'color': '#2b908f'}
bar_cats['decoy'] = {'name': 'decoy', 'color': '#90ed7d'}
bar_cats['target+decoy'] = {'name': 'target+decoy', 'color': '#434348'}

SpecE_cats = [bar_cats] * len(self.search_engine['SpecE'])
xcorr_cats = [bar_cats] * len(self.search_engine['xcorr'])
hyper_cats = [bar_cats] * len(self.search_engine['hyper'])
PEP_cats = [bar_cats] * len(self.search_engine['PEPs'])

xcorr_bar_html = bargraph.plot(list(self.search_engine['xcorr'].values()), xcorr_cats, xcorr_pconfig) if self.Comet_label else ''
SpecE_bar_html = bargraph.plot(list(self.search_engine['SpecE'].values()), SpecE_cats, SpecE_pconfig) if self.MSGF_label else ''

hyper_bar_html = bargraph.plot(list(self.search_engine['hyper'].values()), hyper_cats, hyper_pconfig) if self.Sage_label else ''

self.add_section(
description='''#### Summary of Search Scores
* SpecEvalue : Spectral E-values, the search score of MSGF. The value used for plotting is -lg(SpecEvalue).
* xcorr : cross-correlation scores, the search score of Comet. The value used for plotting is xcorr.
* hyperscore : Hyperscore, the search score of Sage. The value used for plotting is hyperscore.
''',
plot=xcorr_bar_html + SpecE_bar_html
plot=xcorr_bar_html + SpecE_bar_html + hyper_bar_html
)
# Create PEPs summary plot
PEP_pconfig = {
Expand Down Expand Up @@ -895,7 +917,7 @@ def draw_search_engine(self):
else:
self.add_section(
description='''#### Summary of consensus PSMs
No Consensus PSMs data because of single search engine！
No Consensus PSMs data because of single search engine!
'''
)

Expand Down Expand Up @@ -1181,10 +1203,10 @@ def parse_idxml(self, mzml_table):
consensus_paths.append(raw_id)
self.idx_paths.remove(raw_id)

self.MSGF_label, self.Comet_label = False, False
self.search_engine = {'SpecE': OrderedDict(), 'xcorr': OrderedDict(), 'PEPs': OrderedDict(),
self.MSGF_label, self.Comet_label, self.Sage_label = False, False, False
self.search_engine = {'SpecE': OrderedDict(), 'xcorr': OrderedDict(), 'hyper': OrderedDict(), 'PEPs': OrderedDict(),
'consensus_support': OrderedDict(), 'data_label': OrderedDict()}
SpecE_label, xcorr_label, PEPs_label, consensus_label = [], [], [], []
SpecE_label, xcorr_label, hyper_label, PEPs_label, consensus_label = [], [], [], [], []

for raw_id in self.idx_paths:
log.info("Parsing search result file {}...".format(raw_id))
Expand All @@ -1205,10 +1227,14 @@ def parse_idxml(self, mzml_table):

self.search_engine['SpecE'][raw_id] = OrderedDict()
self.search_engine['xcorr'][raw_id] = OrderedDict()
self.search_engine['hyper'][raw_id] = OrderedDict()
self.search_engine['PEPs'][raw_id] = OrderedDict()

xcorr_breaks = list(np.arange(
self.XCORR_HIST_RANGE['start'], self.XCORR_HIST_RANGE['end'] + self.XCORR_HIST_RANGE['step'], self.XCORR_HIST_RANGE['step']).round(2))

hyper_breaks = list(np.arange(
self.HYPER_HIST_RANGE['start'], self.HYPER_HIST_RANGE['end'] + self.HYPER_HIST_RANGE['step'], self.HYPER_HIST_RANGE['step']).round(2))

SpecE_breaks = list(np.arange(
self.SPECEVALUE_HIST_RANGE['start'], self.SPECEVALUE_HIST_RANGE['end'] + self.SPECEVALUE_HIST_RANGE['step'], self.SPECEVALUE_HIST_RANGE['step']).round(2))
Expand All @@ -1220,6 +1246,7 @@ def parse_idxml(self, mzml_table):

bar_stacks = ['target', 'decoy', 'target+decoy']
Xcorr = Histogram('Comet cross-correlation score', plot_category = 'range', stacks = bar_stacks, breaks = xcorr_breaks)
Hyper = Histogram('Sage hyperscore', plot_category = 'range', stacks = bar_stacks, breaks = hyper_breaks)
SpecE = Histogram('MSGF spectral E-value', plot_category = 'range', stacks = bar_stacks, breaks = SpecE_breaks)
PEP = Histogram('Posterior error probability', plot_category = 'range', stacks = bar_stacks, breaks = PEP_breaks)
Consensus_support = Histogram('Consensus PSM number', plot_category = 'frequency', stacks = bar_stacks)
Expand Down Expand Up @@ -1259,6 +1286,23 @@ def parse_idxml(self, mzml_table):
self.search_engine['xcorr'][raw_id] = Xcorr.dict['data']
self.search_engine['PEPs'][raw_id] = PEP.dict['data']

elif search_engine == "Sage" or "sage" in raw_id:
self.Sage_label = True
mzml_table[ms_name]['Sage'] = identified_num
hyper_label.append({'name': raw_id, 'ylab': 'Counts'})
PEPs_label.append({'name': raw_id, 'ylab': 'Counts'})
for peptide_id in peptide_ids:
for hit in peptide_id.getHits():
hyper = hit.getMetaValue("hyperscore")
pep = hit.getMetaValue("MS:1001493") if hit.getMetaValue("MS:1001493") else hit.getScore()
Hyper.addValue(hyper, stack = hit.getMetaValue("target_decoy"))
PEP.addValue(pep, stack = hit.getMetaValue("target_decoy"))

Hyper.to_dict()
PEP.to_dict()
self.search_engine['hyper'][raw_id] = Hyper.dict['data']
self.search_engine['PEPs'][raw_id] = PEP.dict['data']

else:
mzml_table[ms_name][search_engine] = identified_num

Expand All @@ -1283,7 +1327,7 @@ def parse_idxml(self, mzml_table):
for i in Consensus_support.dict['data'].keys():
self.search_engine['consensus_support'][raw_id][i] = Consensus_support.dict['data'][i]

self.search_engine['data_label'] = {'score_label': [SpecE_label, xcorr_label], 'PEPs_label': PEPs_label, 'consensus_label': consensus_label}
self.search_engine['data_label'] = {'score_label': [SpecE_label, xcorr_label, hyper_label], 'PEPs_label': PEPs_label, 'consensus_label': consensus_label}

# mass spectrum files sorted based on experimental file
for spectrum_name in self.exp_design_table.keys():
Expand Down
2 changes: 1 addition & 1 deletion setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@

from setuptools import setup, find_packages

version = '0.0.21'
version = '0.0.22'


def readme():
Expand Down

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