Exploring Metabolomic data with recursive partitioning Metabolomic Workshop NISS July 14-15, 2005.

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Presentation transcript:

Exploring Metabolomic data with recursive partitioning Metabolomic Workshop NISS July 14-15, 2005

University of North Carolina Wilmington 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

University of North Carolina Wilmington Challenges of metabolomic data Nonnormal distributions Outliers Informative missing values High correlation among metabolites n < p problem (n - number of biological samples and p - number of metabolites)

University of North Carolina Wilmington 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

University of North Carolina Wilmington 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

University of North Carolina Wilmington Example

University of North Carolina Wilmington 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

University of North Carolina Wilmington Software Helix Tree (Partitionator) 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)

University of North Carolina Wilmington 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)