Spectrum Normalization

Another very basic mass spectrum processing step is normalization by base peak intensity (the maximum intensity of a mass spectrum).

Let’s first load the raw data.

from urllib.request import urlretrieve
import pyopenms as oms
import matplotlib.pyplot as plt

gh = "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master"
urlretrieve(
    gh + "/src/data/peakpicker_tutorial_1_baseline_filtered.mzML",
    "tutorial.mzML",
)
exp = oms.MSExperiment()
oms.MzMLFile().load("tutorial.mzML", exp)
plt.bar(
    exp.getSpectrum(0).get_peaks()[0],
    exp.getSpectrum(0).get_peaks()[1],
    snap=False,
)

Now we apply the normalization.

normalizer = oms.Normalizer()
param = normalizer.getParameters()
param.setValue("method", "to_one")
normalizer.setParameters(param)

normalizer.filterPeakMap(exp)
plt.bar(
    exp.getSpectrum(0).get_peaks()[0],
    exp.getSpectrum(0).get_peaks()[1],
    snap=False,
)

Another way of normalizing is by TIC (total ion count) of the mass spectrum, which scales intensities so they add up to \(1.0\) in each mass spectrum. Try it out for yourself by setting: param.setValue("method", "to_TIC").