2 research outputs found
Data Dependent Peak Model Based Spectrum Deconvolution for Analysis of High Resolution LC-MS Data
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
Data Preprocessing Method for Liquid Chromatography–Mass Spectrometry Based Metabolomics
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