Liquid (or gas) chromatography coupled with mass spectrometry (LC-MS or GC-MS) allows quantitative comparison of biomolecular abundance in biological samples to help with the discovery of candidate biomarkers for complex diseases such as cancer. A fundamental challenge in using quantitative analysis of biomolecules by LC-MS or GC-MS for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Various contaminations present in cancerous tissues or adjacent non-cancerous constituents confound the characterization of molecular expression profiles and thus hinder the discovery of reliable biomarkers. We previously applied probabilistic purification model on a relatively small sample-size metabolomic data. In this study, we further apply probabilistic purification models on larger sample-size and multi-group metabolomic datasets acquired by analysis of liver tissues using both LC-MS and GC-MS. We demonstrate the advantages of incorporating purification models in retrieving underlying sources and reduce noise in metabolomic data. Furthermore, we investigate the benefit of the proposed models in improving our ability to detect changes in the level of metabolites among liver tissue from multiple groups (tumor, liver cirrhosis, and normal).

BACKGROUND: Gas chromatography coupled with mass spectrometry (GC-MS) is one of the technologies widely used for qualitative and quantitative analysis of small molecules. In particular, GC coupled to single quadrupole MS can be utilized for targeted analysis by selected ion monitoring (SIM). However, to our knowledge, there are no software tools specifically designed for analysis of GC-SIM-MS data. In this paper, we introduce a new R/Bioconductor package called SIMAT for quantitative analysis of the levels of targeted analytes. SIMAT provides guidance in choosing fragments for a list of targets. This is accomplished through an optimization algorithm that has the capability to select the most appropriate fragments from overlapping chromatographic peaks based on a pre-specified library of background analytes. The tool also allows visualization of the total ion chromatograms (TIC) of runs and extracted ion chromatograms (EIC) of analytes of interest. Moreover, retention index (RI) calibration can be performed and raw GC-SIM-MS data can be imported in netCDF or NIST mass spectral library (MSL) formats. RESULTS: We evaluated the performance of SIMAT using two GC-SIM-MS datasets obtained by targeted analysis of: (1) plasma samples from 86 patients in a targeted metabolomic experiment; and (2) mixtures of internal standards spiked in plasma samples at varying concentrations in a method development study. Our results demonstrate that SIMAT offers alternative solutions to AMDIS and MetaboliteDetector to achieve accurate detection of targets and estimation of their relative intensities by analysis of GC-SIM-MS data. CONCLUSIONS: We introduce a new R package called SIMAT that allows the selection of the optimal set of fragments and retention time windows for target analytes in GC-SIM-MS based analysis. Also, various functions and algorithms are implemented in the tool to: (1) read and import raw data and spectral libraries; (2) perform GC-SIM-MS data preprocessing; and (3) plot and visualize EICs and TICs.

MOTIVATION: Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various '-omic' studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data. RESULTS: We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information. AVAILABILITY AND IMPLEMENTATION: MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.