Workflow4Metabolomics Object Identifier: W4M00007
Digital Object Identifier: 10.15454/1.4985472277740251E12
Creator of the history: Cédric Delporte and Florence Souard
Maintainer: Cédric Delporte (cedric.delporte at ulb . ac . be) and Florence Souard (florence.souard at univ - grenoble - alpes.fr)
Creation|Updating date: 2017-06-27
Format: Workflow4Metabolomics Galaxy histories
Size: 6.23 Go
Keywords: coffee leaves, lcms, prepocessing, statistics, biosigner, heatmap
|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).|
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.
Rights: Creative Commons
- Souard F., Delporte C., Stoffelen P., Thévenot E., Noret N., Dauvergne B., Kauffmann J-M.,Van Antwerpen P., Stévigny C., Metabolomic fingerprint of coffee species determined by untargeted-profiling study using LC-HRMS. Food Chemistry, 2017 - Submitted
Raw data repository:
Please find more referenced W4M histories here.