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Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments.

ABSTRACT: Oxidation of organic compounds in the atmosphere produces an immensely complex mixture of product species, posing a challenge for both their measurement in laboratory studies and their inclusion in air quality and climate models. Mass spectrometry techniques can measure thousands of these species, giving insight into these chemical processes, but the datasets themselves are highly complex. Data reduction techniques that group compounds in a chemically and kinetically meaningful way provide a route to simplify the chemistry of these systems but have not been systematically investigated. Here we evaluate three approaches to reducing the dimensionality of oxidation systems measured in an environmental chamber: positive matrix factorization (PMF), hierarchical clustering analysis (HCA), and a parameterization to describe kinetics in terms of multigenerational chemistry (gamma kinetics parameterization, GKP). The evaluation is implemented by means of two datasets: synthetic data consisting of a three-generation oxidation system with known rate constants, generation numbers, and chemical pathways; and the measured products of OH-initiated oxidation of a substituted aromatic compound in a chamber experiment. We find that PMF accounts for changes in the average composition of all products during specific periods of time but does not sort compounds into generations or by another reproducible chemical process. HCA, on the other hand, can identify major groups of ions and patterns of behavior and maintains bulk chemical properties like carbon oxidation state that can be useful for modeling. The continuum of kinetic behavior observed in a typical chamber experiment can be parameterized by fitting species' time traces to the GKP, which approximates the chemistry as a linear, first-order kinetic system. The fitted parameters for each species are the number of reaction steps with OH needed to produce the species (the generation) and an effective kinetic rate constant that describes the formation and loss rates of the species. The thousands of species detected in a typical laboratory chamber experiment can be organized into a much smaller number (10-30) of groups, each of which has a characteristic chemical composition and kinetic behavior. This quantitative relationship between chemical and kinetic characteristics, and the significant reduction in the complexity of the system, provides an approach to understanding broad patterns of behavior in oxidation systems and could be exploited for mechanism development and atmospheric chemistry modeling.

SUBMITTER: Koss AR

PROVIDER: S-EPMC7995644 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIt is hypothesized that common, complex diseases may be due to complex interactions between genetic and environmental factors, which are difficult to detect in high-dimensional data using traditional statistical approaches. Multifactor Dimensionality Reduction (MDR) is the most commonly used data-mining method to detect epistatic interactions. In all data-mining methods, it is important to consider internal validation procedures to obtain prediction estimates to prevent model over-fitting and reduce potential false positive findings. Currently, MDR utilizes cross-validation for internal validation. In this study, we incorporate the use of a three-way split (3WS) of the data in combination with a post-hoc pruning procedure as an alternative to cross-validation for internal model validation to reduce computation time without impairing performance. We compare the power to detect true disease causing loci using MDR with both 5- and 10-fold cross-validation to MDR with 3WS for a range of single-locus and epistatic disease models. Additionally, we analyze a dataset in HIV immunogenetics to demonstrate the results of the two strategies on real data.ResultsMDR with 3WS is computationally approximately five times faster than 5-fold cross-validation. The power to find the exact true disease loci without detecting false positive loci is higher with 5-fold cross-validation than with 3WS before pruning. However, the power to find the true disease causing loci in addition to false positive loci is equivalent to the 3WS. With the incorporation of a pruning procedure after the 3WS, the power of the 3WS approach to detect only the exact disease loci is equivalent to that of MDR with cross-validation. In the real data application, the cross-validation and 3WS analyses indicate the same two-locus model.ConclusionsOur results reveal that the performance of the two internal validation methods is equivalent with the use of pruning procedures. The specific pruning procedure should be chosen understanding the trade-off between identifying all relevant genetic effects but including false positives and missing important genetic factors. This implies 3WS may be a powerful and computationally efficient approach to screen for epistatic effects, and could be used to identify candidate interactions in large-scale genetic studies.

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Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments.

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