Exploring Metabolomic data with recursive partitioning

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Exploring Metabolomic data with recursive
partitioning

• Metabolomic Workshop
• NISS
• July 14-15, 2005

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Why study metabolites?

• Metabolomics the global study of all small
  molecules produced in the human body
• Biochemical consequences of environment, drugs,
  and mutations can be observed directly through
  metabolites
• Understand how drugs work, interactions and
  possible side effects
• 2500 metabolites

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Challenges of metabolomic data

• Nonnormal distributions
• Outliers
• Informative missing values
• High correlation among metabolites
• n lt p problem
• (n - number of biological samples and
• p - number of metabolites)

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Why recursive partitioning?

• Is fairly robust to non-normal data
• Missing values is not an issue
• Correlation among variables is not an issue
• Useful for discovering outliers
• Is efficient at handling large p, small n data
  sets

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How recursive partitioning works

• Recursive partitioning efficiently searches
  through all of the variables and finds the one
  with the best split (most significant)
• Once data is split or partitioned on this
  variable, the resulting daughter nodes are more
  homogeneous
• Now each daughter node is explored to find the
  best split
• This process is continued until no significant
  split remains

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Example
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Multiple Trees

• All effects are not necessarily found in a single
  tree
• In any node, there may be more than one
  significant variable
• Creating multiple trees may reveal a number of
  possible effects
• Gain an understanding of interactions/correlations
  among metabolites

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Software

• Helix Tree (Partitionator)
• www.goldenhelix.com
• Uses Formal Inference-based Recursive Modeling
  (FIRM) developed by Douglas Hawkins
• Anyone can download free 7 day trial (webinars to
  assist in using the software)

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Illustration of Software

• Data
• 317 metabolites
• LC/MS and GC/MS
• 63 biological samples
• Want to discover which metabolites differentiate
  between the diseased group and the healthy
  individuals (within the diseased group there is a
  subset of individuals currently taking drugs)