Dataset Information

Assessing Public Metabolomics Metadata, Towards Improving Quality.

ABSTRACT: Public resources need to be appropriately annotated with metadata in order to make them discoverable, reproducible and traceable, further enabling them to be interoperable or integrated with other datasets. While data-sharing policies exist to promote the annotation process by data owners, these guidelines are still largely ignored. In this manuscript, we analyse automatic measures of metadata quality, and suggest their application as a mean to encourage data owners to increase the metadata quality of their resources and submissions, thereby contributing to higher quality data, improved data sharing, and the overall accountability of scientific publications. We analyse these metadata quality measures in the context of a real-world repository of metabolomics data (i.e. MetaboLights), including a manual validation of the measures, and an analysis of their evolution over time. Our findings suggest that the proposed measures can be used to mimic a manual assessment of metadata quality.

SUBMITTER: Ferreira JD

PROVIDER: S-EPMC6042808 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Assessing Public Metabolomics Metadata, Towards Improving Quality.

Ferreira João D JD Inácio Bruno B Salek Reza M RM Couto Francisco M FM

Journal of integrative bioinformatics 20171213 4

Public resources need to be appropriately annotated with metadata in order to make them discoverable, reproducible and traceable, further enabling them to be interoperable or integrated with other datasets. While data-sharing policies exist to promote the annotation process by data owners, these guidelines are still largely ignored. In this manuscript, we analyse automatic measures of metadata quality, and suggest their application as a mean to encourage data owners to increase the metadata qual ...[more]

PMID: 29236679

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Project description:BackgroundRepetitive transcranial magnetic stimulation (rTMS) has received empirical support as a viable treatment alternative for treatment-resistant major depressive disorder. Nevertheless, patients and the public-at-large may be hesitant to adopt rTMS. In three studies, we sought to (1) assess and (2) improve public perceptions of rTMS as a treatment for depression.MethodsIn Study 1 (N = 107), we administered questionnaires on Amazon's Mechanical Turk (MTurk) to individuals from the US and Canada in a cross-sectional design to assess perceptions of rTMS compared to psychopharmacology, electroconvulsive therapy (ECT), and talk therapy. In Study 2 (N = 106), we again used an MTurk sample and a cross-sectional design to assess perceptions of rTMS after providing participants with a relatively long description of rTMS. In Study 3 (N = 308), we conducted an experiment in undergraduate students. Participants were randomized to one of four experimental conditions manipulating participants' understanding of the causal mechanisms of depression prior to assessing their perceptions of rTMS.ResultsPublic perceptions of rTMS were more negative than pharmacotherapy and talk therapy but not ECT (Study 1). rTMS perceptions were notably better when participants were given thorough information about rTMS procedures, pain, and side-effects (Study 2), compared to the previous study when they were given a very brief description of rTMS. Finally, perceptions of rTMS were significantly better when participants were given a brain circuitry-based causal explanation of depression compared to when they were given a psychological explanation of the causes of depression (Study 3).ConclusionsPublic perceptions of rTMS are relatively poor. To improve rTMS acceptability, practitioners should carefully consider patients' prior attitudes and beliefs when explaining rTMS as a treatment alternative. Given that beliefs can have powerful effects on treatment outcome (e.g., placebo, nocebo), future research should explore whether rTMS effects on depression can be improved by facilitating less negative perceptions of rTMS.

Project description:BackgroundChromatographic peakpicking continues to represent a significant bottleneck in automated LC-MS workflows. Uncontrolled false discovery rates and the lack of manually-calibrated quality metrics require researchers to visually evaluate individual peaks, requiring large amounts of time and breaking replicability. This problem is exacerbated in noisy environmental datasets and for novel separation methods such as hydrophilic interaction columns in metabolomics, creating a demand for a simple, intuitive, and robust metric of peak quality.ResultsHere, we manually labeled four HILIC oceanographic particulate metabolite datasets to assess the performance of individual peak quality metrics. We used these datasets to construct a predictive model calibrated to the likelihood that visual inspection by an MS expert would include a given mass feature in the downstream analysis. We implemented two novel peak quality metrics, a custom signal-to-noise metric and a test of similarity to a bell curve, both calculated from the raw data in the extracted ion chromatogram, and found that these outperformed existing measurements of peak quality. A simple logistic regression model built on two metrics reduced the fraction of false positives in the analysis from 70-80% down to 1-5% and showed minimal overfitting when applied to novel datasets. We then explored the implications of this quality thresholding on the conclusions obtained by the downstream analysis and found that while only 10% of the variance in the dataset could be explained by depth in the default output from the peakpicker, approximately 40% of the variance was explained when restricted to high-quality peaks alone.ConclusionsWe conclude that the poor performance of peakpicking algorithms significantly reduces the power of both univariate and multivariate statistical analyses to detect environmental differences. We demonstrate that simple models built on intuitive metrics and derived from the raw data are more robust and can outperform more complex models when applied to new data. Finally, we show that in properly curated datasets, depth is a major driver of variability in the marine microbial metabolome and identify several interesting metabolite trends for future investigation.

Dataset Information

Assessing Public Metabolomics Metadata, Towards Improving Quality.

Publications

Assessing Public Metabolomics Metadata, Towards Improving Quality.

Similar Datasets

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