Data Dependent Peak Model Based Spectrum Deconvolution
for Analysis of High Resolution LC-MS Data
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Abstract
A data dependent peak model (DDPM)
based spectrum deconvolution
method was developed for analysis of high resolution LC-MS data. To
construct the selected ion chromatogram (XIC), a clustering method,
the density based spatial clustering of applications with noise (DBSCAN),
is applied to all <i>m</i>/<i>z</i> values of
an LC-MS data set to group the <i>m</i>/<i>z</i> values into each XIC. The DBSCAN constructs XICs without the need
for a user defined <i>m</i>/<i>z</i> variation
window. After the XIC construction, the peaks of molecular ions in
each XIC are detected using both the first and the second derivative
tests, followed by an optimized chromatographic peak model selection
method for peak deconvolution. A total of six chromatographic peak
models are considered, including Gaussian, log-normal, Poisson, gamma,
exponentially modified Gaussian, and hybrid of exponential and Gaussian
models. The abundant nonoverlapping peaks are chosen to find the optimal
peak models that are both data- and retention-time-dependent. Analysis
of 18 spiked-in LC-MS data demonstrates that the proposed DDPM spectrum
deconvolution method outperforms the traditional method. On average,
the DDPM approach not only detected 58 more chromatographic peaks
from each of the testing LC-MS data but also improved the retention
time and peak area 3% and 6%, respectively