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Metabolomics 2019
Workshop Pages for Students
Informatics and Statistics for Metabolomics 2019
/site_images/CBW_metabolomics_icon.jpg
metabolome-focused experiments, analyzing metabolomic data, metabolome
Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases.
David Wishart, Jeff Xia
2 days

Welcome

Welcome to Informatics and Statistics for Metabolomics 2019.

The course schedule can be found here.

Meet your faculty here

CLASS MATERIALS: You can download the printed course manual here.

Pre-Workshop Materials

Laptop Setup Instructions

Instructions to setup your laptop can be found here.

Pre-workshop Tutorials

Pre-readings and pre-work can be found here here.
It is in your best interest to complete these before the workshop.

Class Photo

Coming soon

Day 1

Welcome

Michelle Brazas

Module 1: Introduction to Metabolomics

David Wishart

Lecture

Module 2: Metabolite Identification and Annotation

David Wishart

Lecture

Lab practical

Instructions for lab

  1. NMR and Bayesil.

  2. GC-MS and GCMS.

  3. LC-MS and XCMS in R. If R doesn't work, try XCMS online.

Data Set and Results Files:

NMR:
GC-Autofit:

Download this file

LC-MS:

Metadata

Raw datasets (raw.zip files)

CKD raw
Control raw

Converted datasets (mzxml.zip files)

CDK mzxml
Control mzxml

XCMS using R (results files)

Diffreport
Peak Table

XCMS online (results files)

Cleaned Diffreport
Normalized Results
Unnormalized Results

Links:

Module 3: Databases for Chemical, Spectral, and Biological Data

David Wishart

Lecture

Lab Practical - From Raw Spectra to Biological Insights using MetaboAnalystR 2.0

Links:

(Optional) From Raw Spectra to Biological Insights

Jeff Xia

Lecture

Lab practical

Day 2

Module 4: Backgrounder in Statistics

Jeff Xia

Lecture

Module 5: MetaboAnalyst

Jeff Xia

Lecture

Lab practical

Data Input:

Critical: Before uploading your data, perform a sanity check:

  • Verify that it is a data table separated by commas (.csv) or tabs (.txt);

  • For concentration/peak intensity tables: three types of labels should be present; feature names, sample names and group labels (must directly follow sample names);

  • All measurements should be numerical values (empty for missing values);

  • For details and screenshot instructions, click here

  • Data 1 Metabolomic concentration table of 77 urine samples from cancer patients and healthy controls.

  • Data 2 Peak intensity table of 48 fecal samples from pediatric inflammatory bowel disease patients and healthy controls.

  • Data 3 Peak list of 48 fecal samples from pediatric inflammatory bowel disease patients and healthy controls.

Module 6: Future of Metabolomics

David Wishart

Lecture

Thank you for attending the Analysis of Metagenomic Data workshop! Help us make this workshop better by filling out our survey