Selective Paired Ion Contrast Analysis: A Novel Algorithm for Analyzing Postprocessed LC-MS Metabolomics Data Possessing High Experimental Noise

Abstract

One of the consequences in analyzing biological data from noisy sources, such as human subjects, is the sheer variability of experimentally irrelevant factors that cannot be controlled for. This holds true especially in metabolomics, the global study of small molecules in a particular system. While metabolomics can offer deep quantitative insight into the metabolome via easy-to-acquire biofluid samples such as urine and blood, the aforementioned confounding factors can easily overwhelm attempts to extract relevant information. This can mar potentially crucial applications such as biomarker discovery. As such, a new algorithm, called Selective Paired Ion Contrast (SPICA), has been developed with the intent of extracting potentially biologically relevant information from the noisiest of metabolomic datasets. The basic idea of SPICA is built upon redefining the fundamental unit of statistical analysis. Whereas the vast majority of algorithms analyze metabolomics data on a single-ion basis, SPICA relies on analyzing ion-pairs. A standard metabolomic data set is reinterpreted by exhaustively considering all possible ion-pair combinations. Statistical comparisons between sample groups are made only by analyzing the differences in these pairs, which may be crucial in situations where no single metabolite can be used for normalization. With SPICA, human urine data sets from patients undergoing total body irradiation (TBI), and from a colorectal cancer (CRC) relapse study were analyzed in a statistically rigorous manner not possible with conventional methods. In the TBI study, 3530 statistically significant ion-pairs were identified, from which numerous putative radiation specific metabolite-pair biomarkers that mapped to potentially perturbed metabolic pathways were elucidated. In the CRC study, SPICA identified 6461 statistically significant ion-pairs, several of which putatively mapped to folic acid biosynthesis, a key pathway in colorectal cancer. Utilizing support vector machines (SVMs), SPICA was also able to unequivocally outperform binary classifiers built from classical single-ion feature based SVMs.

Metabolomic applications in radiation biodosimetry: exploring radiation effects through small molecules

“…Liang et al investigated basal metabolomic differences in control populations to putatively identify steroid metabolites as the primary discriminatory molecules (Liang et al, 2015), and therefore caution should be taken when utilizing such metabolites as radiation specific. Given the heterogeneity in the available human radiation cohorts due not only to genetic differences but also to underlying disease, it has been proposed that perhaps pairs of ions instead of single biomarkers and patterns of similar or dissimilar regulation may provide more power in distinguishing exposed from non-exposed individuals (Mak et al, 2015a). This study also further identified perturbations in lysine biosynthesis/degradation and folate biosynthesis.…”

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

“…Moreover, unlike transcriptomics, correlation among metabolites identified from metabolomics data may not indicate a common biological function27. Apart from the above differences, there are significant similarities between two OMICs data: (1) right-skewed distribution23, (2) great data sparsity28, (3) substantial amount of noise2930 and (4) significantly varied sample sizes3132. Due to these similarities, it is feasible to apply some of the normalization methods used in transcriptomics data analysis to the metabolomics one.…”

Rapid and High-Throughput Detection and Quantitation of Radiation Biomarkers in Human and Nonhuman Primates by Differential Mobility Spectrometry-Mass Spectrometry

“…Continued methods development will be based on more complete species and time-course response data, further refinement of sample storage and extraction methods, introducing an automated flow injection device, standardization and automation of mass-spectrometric techniques, and continued attention to new bioinformatic methods [50, 51]. The gains in sample throughput are to a large extent specific to the analytes described in this paper.…”