Data Preprocessing Method
for Liquid Chromatography–Mass
Spectrometry Based Metabolomics
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Abstract
A set of data preprocessing algorithms for peak detection
and peak
list alignment are reported for analysis of liquid chromatography–mass
spectrometry (LC–MS)-based metabolomics data. For spectrum
deconvolution, peak picking is achieved at the selected ion chromatogram
(XIC) level. To estimate and remove the noise in XICs, each XIC is
first segmented into several peak groups based on the continuity of
scan number, and the noise level is estimated by all the XIC signals,
except the regions potentially with presence of metabolite ion peaks.
After removing noise, the peaks of molecular ions are detected using
both the first and the second derivatives, followed by an efficient
exponentially modified Gaussian-based peak deconvolution method for
peak fitting. A two-stage alignment algorithm is also developed, where
the retention times of all peaks are first transferred into the <i>z</i>-score domain and the peaks are aligned based on the measure
of their mixture scores after retention time correction using a partial
linear regression. Analysis of a set of spike-in LC–MS data
from three groups of samples containing 16 metabolite standards mixed
with metabolite extract from mouse livers demonstrates that the developed
data preprocessing method performs better than two of the existing
popular data analysis packages, MZmine2.6 and XCMS<sup>2</sup>, for
peak picking, peak list alignment, and quantification