Quantitative Data

features

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.

import pyopenms as oms

feature = oms.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 = oms.FeatureMap()
fm.push_back(feature)
feature.setRT(1600.5)
feature.setCharge(2)
feature.setMZ(600.0)
feature.setIntensity(80500.0)
fm.push_back(feature)
oms.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 two features are present, but in a typical LC-MS/MS experiments, thousands of features are present.

Feature Maps

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

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

Consensus Features

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

feature = oms.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 = oms.ConsensusFeature()
f_m1.setRT(500)
f_m1.setMZ(300.01)
f_m1.setIntensity(200)
f_m1.ensureUniqueId()
### Feature 2
f_m2 = oms.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 = oms.ConsensusMap()
fds = {1: oms.ColumnHeader(), 2: oms.ColumnHeader()}
fds[1].filename = "file1"
fds[2].filename = "file2"
cmap.setColumnHeaders(fds)

feature.ensureUniqueId()
cmap.push_back(feature)
oms.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:

Consensus Maps

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

cmap = oms.ConsensusMap()
oms.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())