Dataset Information

Analytic Correlation Filtration: A New Tool to Reduce Analytical Complexity of Metabolomic Datasets.

ABSTRACT: Metabolomics generates massive and complex data. Redundant different analytical species and the high degree of correlation in datasets is a constraint for the use of data mining/statistical methods and interpretation. In this context, we developed a new tool to detect analytical correlation into datasets without confounding them with biological correlations. Based on several parameters, such as a similarity measure, retention time, and mass information from known isotopes, adducts, or fragments, the algorithm principle is used to group features coming from the same analyte, and to propose one single representative per group. To illustrate the functionalities and added-value of this tool, it was applied to published datasets and compared to one of the most commonly used free packages proposing a grouping method for metabolomics data: 'CAMERA'. This tool was developed to be included in Galaxy and will be available in Workflow4Metabolomics (http://workflow4metabolomics.org). Source code is freely available for download under CeCILL 2.1 license at https://services.pfem.clermont.inra.fr/gitlab/grandpa /tool-acf and implement in Perl.

SUBMITTER: Monnerie S

PROVIDER: S-EPMC6918187 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Analytic Correlation Filtration: A New Tool to Reduce Analytical Complexity of Metabolomic Datasets.

Monnerie Stephanie S Petera Melanie M Lyan Bernard B Gaudreau Pierrette P Comte Blandine B Pujos-Guillot Estelle E

Metabolites 20191024 11

Metabolomics generates massive and complex data. Redundant different analytical species and the high degree of correlation in datasets is a constraint for the use of data mining/statistical methods and interpretation. In this context, we developed a new tool to detect analytical correlation into datasets without confounding them with biological correlations. Based on several parameters, such as a similarity measure, retention time, and mass information from known isotopes, adducts, or fragments, ...[more]

PMID: 31653057

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Project description:BackgroundDiagnosis and management of depression occurs frequently in the primary care setting. Current diagnostic and management of treatment practices across clinical populations focus on eliminating signs and symptoms of depression. However, there is debate that some interventions may pathologize normal, adaptive responses to stressors. Analytical rumination (AR) is an example of an adaptive response of depression that is characterized by enhanced cognitive function to help an individual focus on, analyze, and solve problems. To date, research on AR has been hampered by the lack of theoretically-derived and psychometrically sound instruments. This study developed and tested a clinically meaningful measure of AR.MethodsUsing expert panels and an extensive literature review, we developed a conceptual framework for AR and 22 candidate items. Items were field tested to 579 young adults; 140 of whom completed the items at a second time point. We used Rasch measurement methods to construct and test the item set; and traditional psychometric analyses to compare items to existing rating scales.ResultsData were high quality (<1% missing; high reliability: Cronbach's alpha = 0.92, test-retest intraclass correlations >0.81; evidence for divergent validity). Evidence of misfit for 2 items suggested that a 20-item scale with 4-point response categories best captured the concept of AR, fitting the Rasch model (χ2 = 95.26; df = 76, p = 0.07), with high reliability (rp = 0.86), ordered response scale structure, and no item bias (gender, age, time).ConclusionOur study provides evidence for a 20-item Analytical Rumination Questionnaire (ARQ) that can be used to quantify AR in adults who experience symptoms of depression. The ARQ is psychometrically robust and a clinically useful tool for the assessment and improvement of depression in the primary care setting. Future work is needed to establish the validity of this measure in people with major depression.

Dataset Information

Analytic Correlation Filtration: A New Tool to Reduce Analytical Complexity of Metabolomic Datasets.

Publications

Analytic Correlation Filtration: A New Tool to Reduce Analytical Complexity of Metabolomic Datasets.

Similar Datasets

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