Error Analysis and Propagation in Metabolomics Data Analysis

Moseley, Hunter N. B.

doi:10.5936/csbj.201301006

Cited by 47 publications

(52 citation statements)

References 90 publications

(103 reference statements)

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“…However, prior to analyzing the untargeted metabolite profile of the hydrophilic extracts, a validation of the analytical method was completed. Analytical variance arises from the spread of measured values (e.g., sample preparation retention times, peak intensities) observed from multiple measurements of the same biological sample (Moseley, 2013), and such a variance can skew successive technical steps such as data processing. The reproducibility and stability of the LC-MS/MS method were evaluated by examining the distribution of the relative standard deviations (%RSD) of the peak areas (of all the detected features) from the pooled QC sample that was repetitively analyzed after every five injections throughout the analytical run.…”

Section: Resultsmentioning

confidence: 99%

Metabolomic analysis to define and compare the effects of PAHs and oxygenated PAHs in developing zebrafish

Elie

Choi

Nkrumah‐Elie

et al. 2015

Environmental Research

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Polycyclic aromatic hydrocarbons (PAHs) and their oxygenated derivatives are ubiquitously present in diesel exhaust, atmospheric particulate matter and soils sampled in urban areas. Therefore, inhalation or non-dietary ingestion of both PAHs and oxy-PAHs are major routes of exposure for people; especially young children living in these localities. While there has been extensive research on the parent PAHs, limited studies exist on the biological effects of oxy-PAHs which have been shown to be more soluble and more mobile in the environment. Additionally, investigations comparing the metabolic responses resulting from parent PAHs and oxy-PAHs exposures have not been reported. To address these current gaps, an untargeted metabolomics approach was conducted to examine the in vivo metabolomic profiles of developing zebrafish (Danio rerio) exposed to 4 µM of benz[a]anthracene (BAA) or benz[a]anthracene-7, 12-dione (BAQ). By integrating multivariate, univariate and pathway analyses, a total of 62 metabolites were significantly altered after 5 days of exposure. The marked perturbations revealed that both BAA and BAQ affect protein biosynthesis, mitochondrial function, neural development, vascular development and cardiac function. Our previous transcriptomic and genomic data were incorporated in this metabolomics study to provide a more comprehensive view of the relationship between PAH and oxy-PAH exposures on vertebrate development.

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Section: Resultsmentioning

confidence: 99%

Metabolomic analysis to define and compare the effects of PAHs and oxygenated PAHs in developing zebrafish

Elie

Choi

Nkrumah‐Elie

et al. 2015

Environmental Research

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show abstract

“…Biological variance arises from the spread of metabolic signals detected from various biological samples57, while technical error results from machine drift58. In particular, biological variances (e.g., varying concentration levels of bio-fluid, different cell sizes, varying sample measurements) are commonly encountered in metabolomics data13, while technical errors (e.g., a sudden drop in peak intensities or measurements on different instruments) are the major issues in large-scale metabolomics studies58.…”

Section: Methodsmentioning

confidence: 99%

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

Tang

Yang

et al. 2016

Sci Rep

129

124

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In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

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“…Hence, a careful experimental design is mandatory, and statistical rigor, quality assurance, and proper scientific procedures must be followed and applied at every stage of the workflow so as to generate data that actually contain “objectively true” information about the biological question under investigation [13,28,34,35]. Furthermore, these metabolomics workflow steps are not always to be followed linearly.…”

Section: Resultsmentioning

confidence: 99%

A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps

et al. 2016

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Untargeted metabolomic studies generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit. Despite the remarkable progress in the development of tools and algorithms, the “exhaustive” extraction of information from these metabolomic datasets is still a non-trivial undertaking. A conversation on data mining strategies for a maximal information extraction from metabolomic data is needed. Using a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomic dataset, this study explored the influence of collection parameters in the data pre-processing step, scaling and data transformation on the statistical models generated, and feature selection, thereafter. Data obtained in positive mode generated from a LC-MS-based untargeted metabolomic study (sorghum plants responding dynamically to infection by a fungal pathogen) were used. Raw data were pre-processed with MarkerLynxTM software (Waters Corporation, Manchester, UK). Here, two parameters were varied: the intensity threshold (50–100 counts) and the mass tolerance (0.005–0.01 Da). After the pre-processing, the datasets were imported into SIMCA (Umetrics, Umea, Sweden) for more data cleaning and statistical modeling. In addition, different scaling (unit variance, Pareto, etc.) and data transformation (log and power) methods were explored. The results showed that the pre-processing parameters (or algorithms) influence the output dataset with regard to the number of defined features. Furthermore, the study demonstrates that the pre-treatment of data prior to statistical modeling affects the subspace approximation outcome: e.g., the amount of variation in X-data that the model can explain and predict. The pre-processing and pre-treatment steps subsequently influence the number of statistically significant extracted/selected features (variables). Thus, as informed by the results, to maximize the value of untargeted metabolomic data, understanding of the data structures and exploration of different algorithms and methods (at different steps of the data analysis pipeline) might be the best trade-off, currently, and possibly an epistemological imperative.

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Error Analysis and Propagation in Metabolomics Data Analysis

Cited by 47 publications

References 90 publications

Metabolomic analysis to define and compare the effects of PAHs and oxygenated PAHs in developing zebrafish

Metabolomic analysis to define and compare the effects of PAHs and oxygenated PAHs in developing zebrafish

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

A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps

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