MS instruments typically allow storing spectra in profile mode (several data points per m/z peak) or in the more codensed centroid mode (one data point per m/z peak). The process of converting a profile spectrum into a centroided one is called peak centroiding or peak picking.
Note: The term peak picking is ambiguous as it is also used for feature detection (i.e. 3D “peak” finding).
First, we load some profile data:
from urllib.request import urlretrieve from pyopenms import * import matplotlib.pyplot as plt gh = "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master" urlretrieve (gh +"/src/data/PeakPickerHiRes_input.mzML", "tutorial.mzML") profile_spectra = MSExperiment() MzMLFile().load("tutorial.mzML", profile_spectra)
Let’s zoom in on an isotopic pattern in profile mode and plot it.
plt.xlim(771.8, 774) # zoom into isotopic pattern plt.plot(profile_spectra.get_peaks(), profile_spectra.get_peaks()) # plot the first spectrum
Because of the limited resolution of MS instruments m/z measurements are not of unlimited precision. Consequently, peak shapes spreads in the m/z dimension and resemble a gaussian distribution. Using the PeakPickerHiRes algorithm, we can convert data from profile to centroided mode. Usually, not much information is lost by storing only centroided data. Thus, many algorithms and tools assume that centroided data is provided.
centroided_spectra = MSExperiment() # input, output, chec_spectrum_type (if set, checks spectrum type and throws an exception if a centroided spectrum is passed) PeakPickerHiRes().pickExperiment(profile_spectra, centroided_spectra, True) # pick all spectra plt.xlim(771.8,774) # zoom into isotopic pattern plt.stem(centroided_spectra.get_peaks(), centroided_spectra.get_peaks()) # plot as vertical lines
After centroding, a single m/z value for every isotopic peak is retained. By plotting the centroided data as stem plot we discover that (in addition to the isotopic peaks) some low intensity peaks (intensity at approx. 4k) were present in the profile data.