Quantitative Data

Feature

In OpenMS, information about quantitative data is stored in a so-called Feature which we have previously discussed here. Each Feature represents a region in RT and m/z space use for quantitative analysis.

from pyopenms import *
feature = Feature()
feature.setMZ( 500.9 )
feature.setCharge(2)
feature.setRT( 1500.1 )
feature.setIntensity( 30500 )
feature.setOverallQuality( 10 )

Usually, the quantitative features would be produced by a so-called “FeatureFinder” algorithm, which we will discuss in the next chapter. The features can be stored in a FeatureMap and written to disk.

fm = FeatureMap()
fm.push_back(feature)
feature.setRT(1600.5 )
feature.setCharge(2)
feature.setMZ( 600.0 )
feature.setIntensity( 80500.0 )
fm.push_back(feature)
FeatureXMLFile().store("test.featureXML", fm)

Visualizing the resulting map in TOPPView allows detection of the two features stored in the FeatureMap with the visualization indicating charge state, m/z, RT and other properties:

Note that in this case only 2 features are present, but in a typical LC-MS/MS experiments, thousands of features are present.

FeatureMap

The resulting FeatureMap can be used in various ways to extract quantitative data directly and it supports direct iteration in Python:

from pyopenms import *
fmap = FeatureMap()
FeatureXMLFile().load("test.featureXML", fmap)
for feature in fmap:
   print("Feature: ", feature.getIntensity(), feature.getRT(), feature.getMZ())

ConsensusFeature

Often LC-MS/MS experiments are run to compare quantitative features across experiments. In OpenMS, linked features from individual experiments are represented by a ConsensusFeature

from pyopenms import *
feature = ConsensusFeature()
feature.setMZ( 500.9 )
feature.setCharge(2)
feature.setRT( 1500.1 )
feature.setIntensity( 80500 )

# Generate ConsensusFeature and features from two maps (with id 1 and 2)
### Feature 1
f_m1 = ConsensusFeature()
f_m1.setRT(500)
f_m1.setMZ(300.01)
f_m1.setIntensity(200)
f_m1.ensureUniqueId()
### Feature 2
f_m2 = ConsensusFeature()
f_m2.setRT(505)
f_m2.setMZ(299.99)
f_m2.setIntensity(600)
f_m2.ensureUniqueId()
feature.insert(1, f_m1 )
feature.insert(2, f_m2 )

We have thus added two features from two individual maps (which have the unique identifier 1 and 2) to the ConsensusFeature. Next, we inspect the consensus feature, compute a “consensus” m/z across the two maps and output the two linked features:

# The two features in map 1 and map 2 represent the same analyte at
# slightly different RT and m/z
for fh in feature.getFeatureList():
  print(fh.getMapIndex(), fh.getIntensity(), fh.getRT())

print(feature.getMZ())
feature.computeMonoisotopicConsensus()
print(feature.getMZ())

# Generate ConsensusMap and add two maps (with id 1 and 2)
cmap = ConsensusMap()
fds = { 1 : ColumnHeader(), 2 : ColumnHeader() }
fds[1].filename = "file1"
fds[2].filename = "file2"
cmap.setColumnHeaders(fds)

feature.ensureUniqueId()
cmap.push_back(feature)
ConsensusXMLFile().store("test.consensusXML", cmap)

Inspection of the generated test.consensusXML reveals that it contains references to two LC-MS/MS runs (file1 and file2) with their respective unique identifier. Note how the two features we added before have matching unique identifiers.

Visualization of the resulting output file reveals a single ConsensusFeature of size 2 that links to the two individual features at their respective positions in RT and m/z:

ConsensusMap

The resulting ConsensusMap can be used in various ways to extract quantitative data directly and it supports direct iteration in Python:

from pyopenms import *
cmap = ConsensusMap()
ConsensusXMLFile().load("test.consensusXML", cmap)
for cfeature in cmap:
   cfeature.computeConsensus()
   print("ConsensusFeature", cfeature.getIntensity(), cfeature.getRT(), cfeature.getMZ())
   # The two features in map 1 and map 2 represent the same analyte at
   # slightly different RT and m/z
   for fh in cfeature.getFeatureList():
     print(" -- Feature", fh.getMapIndex(), fh.getIntensity(), fh.getRT())