Dataset Information

Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations.

ABSTRACT: Motivation:Metabolomics data generated from liquid chromatography-mass spectrometry platforms often contain missing values. Existing imputation methods do not consider underlying feature relations and the metabolic network information. As a result, the imputation results may not be optimal. Results:We proposed an imputation algorithm that incorporates the existing metabolic network, adduct ion relations even for unknown compounds, as well as linear and nonlinear associations between feature intensities to build a feature-level network. The algorithm uses support vector regression for missing value imputation based on features in the neighborhood on the network. We compared our proposed method with methods being widely used. As judged by the normalized root mean squared error in real data-based simulations, our proposed methods can achieve better accuracy. Availability and implementation:The R package is available at http://web1.sph.emory.edu/users/tyu8/MINMA. Contact:jiankang@umich.edu or tianwei.yu@emory.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Jin Z

PROVIDER: S-EPMC5925787 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations.

Jin Zhuxuan Z Kang Jian J Yu Tianwei T

Bioinformatics (Oxford, England) 20180501 9

<h4>Motivation</h4>Metabolomics data generated from liquid chromatography-mass spectrometry platforms often contain missing values. Existing imputation methods do not consider underlying feature relations and the metabolic network information. As a result, the imputation results may not be optimal.<h4>Results</h4>We proposed an imputation algorithm that incorporates the existing metabolic network, adduct ion relations even for unknown compounds, as well as linear and nonlinear associations betwe ...[more]

PMID: 29272352

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Project description:BackgroundEpistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based), that have been modified to work with symmetric pairwise data.ResultsWe identify different categories for the missing data based on their underlying cause, and show that values from the largest category can be imputed effectively. We compare local and global imputation approaches across a variety of distinct E-MAP datasets, showing that both are competitive and preferable to filling in with zeros. In addition we show that these methods are effective in an E-MAP from a different species, suggesting that pairwise imputation techniques will be increasingly useful as analogous epistasis mapping techniques are developed in different species. We show that strongly alleviating interactions are significantly more difficult to predict than strongly aggravating interactions. Finally we show that imputed interactions, generated using nearest neighbor methods, are enriched for annotations in the same manner as measured interactions. Therefore our method potentially expands the number of mapped epistatic interactions. In addition we make implementations of our algorithms available for use by other researchers.ConclusionsWe address the problem of missing value imputation for E-MAPs, and suggest the use of symmetric nearest neighbor based approaches as they offer consistently accurate imputations across multiple datasets in a tractable manner.

Dataset Information

Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations.

Publications

Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations.

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