W4M00001_Sacurine-statistics (doi:10.15454/1.4811121736910142E12)

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Document Description

Citation

Title:

W4M00001_Sacurine-statistics

Identification Number:

doi:10.15454/1.4811121736910142E12

Distributor:

Portail Data INRAE

Date of Distribution:

2018-01-25

Version:

2

Bibliographic Citation:

Etienne Thévenot, 2018, "W4M00001_Sacurine-statistics", https://doi.org/10.15454/1.4811121736910142E12, Portail Data INRAE, V2

Study Description

Citation

Title:

W4M00001_Sacurine-statistics

Identification Number:

doi:10.15454/1.4811121736910142E12

Identification Number:

W4M00001

Authoring Entity:

Etienne Thévenot

Date of Production:

2015-06-19

Distributor:

Portail Data INRAE

Access Authority:

pfem

Depositor:

pfem

Date of Distribution:

2015

Study Scope

Keywords:

homosapiens, urine, lcms, statistics, age, bmi, gender

Abstract:

Abstract:Study: Characterization of the physiological variations of the metabolome in biofluids is critical to understand human physiology and to avoid confounding effects in cohort studies aiming at biomarker discovery. Dataset: In this study conducted by the MetaboHUB French Infrastructure for Metabolomics, urine samples from 184 volunteers were analyzed by reversed-phase (C18) ultrahigh performance liquid chromatography (UPLC) coupled to high-resolution mass spectrometry (LTQ-Orbitrap). A total of 258 metabolites were identified at confidence levels provided by the metabolomics standards initiative (MSI) levels 1 or 2. Workflow: This history describes the statistical analysis of the data set from the negative ionization mode (113 identified metabolites at MSI levels 1 or 2): correction of signal drift (loess model built on QC pools) and batch effects (two batches), variable filtering (QC coefficent of variation < 30%), normalization by the sample osmolality, log10 transformation, sample filtering (Hotelling, decile and missing pvalues > 0.001) resulting in the HU_096 sample being discarded, univariate hypothesis testing of significant variations with age, BMI, or between genders (FDR < 0.05), and OPLS(-DA) modeling of age, BMI and gender. Comments: The ‘sacurine’ data set (after normalization and filtering) is also available in the ropls R package from the Bioconductor repository. For a comprehensive analysis of the dataset (starting from the preprocessing of the raw files and including all detected features in the subsequent steps), please see the companion ‘W4M00002_Sacurine-comprehensive’ reference history.

Kind of Data:

Workflow

Notes:

format:Workflow4Metabolomics Galaxy histories

Methodology and Processing

Sources Statement

Data Access

Notes:

CC BY

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