Metabolomics workshop

Learning Objectives: 1-to understand metabolite profiling data sets such as GC-MS, LC-MS, LC-MS/MS; 2- analyze and communicate with statistician during analysis; 3- interpret, and a prepare a draft manuscript.

Specific Activities:

  1. Understanding metabolite profiling datasets
    1. GC-MS based metabolite profiling data
      1. Introduction into GC-MS technology
      2. Chemical derivatization as one example of a hyphenated-MS based profiling method
      3. Processing GC-MS data and initial statistical analysis for metabolite identification
      4. Preparing the data for analysis
    2. LC-MS or LC-MS/MS profiling data
      1. Introduction into LC-MS technology
      2. Preparing the data for analysis
  2. Analysis and interpretation of the big data sets
    1. Decision on primary versus secondary data
      1. In this workshop, publicly available data sets (secondary data) will be used.
    2. Developing a research question
    3. Creating a theoretical framework to interpret the data
      1. The frameworks can be based on (but are not limited to) physiological, phenotypic, biochemical pathways, development, behavior, etc.
      2. Time course data sets
    4. Preparation of a manuscript outline
    5. Communicate with a statistician, a biostatistician, or a data scientist
    6. Application of multivariate statistics
      1. Principle component analysis
        1. Interpretation of the overall results
        2. Interpretation of the components
        3. Then how to communicate with a statistician for further analysis
      2. Cluster analysis
        1. How to decide how many clusters to have
        2. How to interpret
    7. Software tools: Microsoft Access Database
      1. Annotation of the data
      2. Combining large datasets
        1. Adding subcellular location
        2. Comparing known studies
      3. Making small or large tables
      4. Making queries based on our objectives and hypothesis
    8. Using with publicly available tools
      1. Comparative metabolomics
      2. Phylogenetic analysis
    9. Identifying marker or conserved metabolites
      1. Talk to a statistician
      2. Available web tools
    10. Deciding whether you need a new algorithm
    11. More analysis on the data and communication with a statistician

Copyright © 2013 Kaplan Schiller Research, LLC.