KPIC2: An Effective Framework for Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms

Distilling accurate
quantitation information on metabolites from
liquid chromatography coupled with mass spectrometry (LC-MS) data
sets is crucial for further statistical analysis and biomarker identification.
However, it is still challenging due to the complexity of biological
systems. The concept of pure ion chromatograms (PICs) is an effective
way of extracting meaningful ions, but few toolboxes provide a full
processing workflow for LC-MS data sets based on PICs. In this study,
an integrated framework, KPIC2, has been developed for metabolomics
studies, which can detect pure ions accurately, align PICs across
samples, group PICs to identify isotope and potential adducts, fill
missing peaks and do multivariate pattern recognition. To evaluate
its performance, MM48, metabolomics quantitation, and Soybean seeds
data sets have been analyzed using KPIC2, XCMS, and MZmine2. KPIC2
can extract more true ions with fewer detecting features, have good
quantification ability on a metabolomics quantitation data set, and
achieve satisfactory classification on a soybean seeds data set through
kernel-based OPLS-DA and random forest. It is implemented in R programming
language, and the software, user guide, as well as example scripts
and data sets are available as an open source package at https://github.com/hcji/KPIC2.