W4M00007_Coffea-leaves (ICPSR doi:10.15454/1.4985472277740251E12)

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Document Description

Citation

Title:

W4M00007_Coffea-leaves

Identification Number:

doi:10.15454/1.4985472277740251E12

Distributor:

Portail Data INRAE

Date of Distribution:

2018-01-26

Version:

2

Bibliographic Citation:

Cédric Delporte; Florence Souard, 2018, "W4M00007_Coffea-leaves", https://doi.org/10.15454/1.4985472277740251E12, Portail Data INRAE, V2

Study Description

Citation

Title:

W4M00007_Coffea-leaves

Identification Number:

doi:10.15454/1.4985472277740251E12

Identification Number:

W4M00007

Authoring Entity:

Cédric Delporte

Florence Souard

Date of Production:

2017-06-27

Distributor:

Portail Data INRAE

Access Authority:

pfem

Depositor:

pfem

Date of Distribution:

2017

Study Scope

Keywords:

coffea leaves, LCMS, prepocessing, statistics, biosigner, heatmap

Abstract:

Abstract:Study: Characterization of the coffee leaves metabolome composition in 9 species of Coffea (Rubiaceae) collected at 5 dates in 2016 (January – March - July- September – December) after aqueous extraction. A total of 169 samples (including 147 individual samples, 8 blanks and 14 QC pools) were analysed by reversed-phase (C18) liquid chromatography (LCMS) coupled to high-resolution mass. Dataset: In this study, a metabolomics analysis was conducted on 9 species of Coffea leaves using LC-HRMS in electrospray (ESI) positive mode. LC was carried out using reversed phase mode (C18 Poroshell column, Agilent Technologies) and 6520 ESI-QTOF high-resolution mass spectrometer (Agilent Technologies). A total of 1637 features were found and used for the statistical approach study. Workflow: The workflow consists of the following steps: preprocessing with XCMS, pre-annotation with CAMERA, variable filtering (sample mean over blank mean ratio), correction of signal drift (loess model built on QC pools), variable filtering (QC coefficent of variation < 30%), , log10 transformation, sample filtering (Hotelling, decile and missing pvalues > 0.001), univariate hypothesis testing (FDR < 0.05), OPLS(-DA) modelling of species and date of collection, feature selection, clustering of samples and variables (heatmap). Comments: 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 ‘W4M00007_Coffea_leaves’ reference history.

Kind of Data:

Workflow

Notes:

size:6Go format:Workflow4Metabolomics Galaxy histories

Methodology and Processing

Sources Statement

Data Access

Notes:

CC BY

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