Merge pull request #12 from jsaintvanne/sd_update

added sacurine dataset

Workflow4Metabolomics Galaxy Documentation/W4M_datasets/sacurine.qmd

@@ -0,0 +1,24 @@
+---
+title: "Sacurine"
+doi: "[publication](https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00354)"
+# history: "[W4M00001_Sacurine-statistics](https://workflow4metabolomics.usegalaxy.fr/published/history?id=3052e053b71f3ff5)"
+uthor: "Thevenot et al."
+description: "Analysis of the human adult urinary metabolome"
+bibliography: "../references.bib"
+galaxyref: "W4M00001"
+link: "[MTBLS404](https://www.ebi.ac.uk/metabolights/editor/MTBLS404/descriptors)"
+---
+
+## Description
+
+**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.

Workflow4Metabolomics Galaxy Documentation/references.bib

@@ -40,5 +40,19 @@ @article{HILTEMANN202301
 	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
 	editor = {Francis Ouellette},
 	title = {Galaxy Training: A powerful framework for teaching!},
-	journal = {PLoS Comput Biol} Computational Biology}
+	journal = {PLoS Comput Biol Computational Biology}
 }
+
+@article{THEVENOT_2015,
+    author = {Thévenot, Etienne A. and Roux, Aurélie and Xu, Ying and Ezan, Eric and Junot, Christophe},
+    title = {Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses},
+    journal = {Journal of Proteome Research},
+    volume = {14},
+    number = {8},
+    pages = {3322-3335},
+    year = {2015},
+    doi = {10.1021/acs.jproteome.5b00354},
+        note ={PMID: 26088811},
+    URL = {https://doi.org/10.1021/acs.jproteome.5b00354},
+    eprint = {https://doi.org/10.1021/acs.jproteome.5b00354}
+}

Workflow4Metabolomics Galaxy Documentation/tooluser.qmd

@@ -1,9 +1,14 @@
 ---
   title: "User guide"
+  listing: 
+    - id: list_W4M_datasets
+      contents: "W4M_datasets/*.qmd"
+      type: table
+      sort: "date"
+      max-items: 100
+      fields: [galaxyref, link, title, author, history, doi]
 ---
 
-## User Guide
-
 The most effective way to master the use of our tools is by enrolling in our comprehensive training school ![](/images/W4E_icon.svg){height=1em}. Below you will find in-depth learning guides, providing you with the skills you need to leverage our tools seamlessly.
 
 ## Galaxy Training Network on Metabolomics
@@ -23,3 +28,14 @@ Access complete and interactive step-by-step guides by visiting the [![](https:/
 ## Galaxy ![](/images/W4M_icon.svg){height=1em} Instance
 
 Explore our [![](https://galaxyproject.org/images/galaxy-logos/galaxy_project_logo_square.png){height=2em} Galaxy instance](https://workflow4metabolomics.usegalaxy.fr/) were each tool is accompanied by comprehensive support, including helpful documentation, examples, and references, ensuring that you have the resources you need.
+
+## Workflows examples
+
+
+
+## Datasets examples
+
+
+:::{#list_W4M_datasets}
+
+:::