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CRiSM Seminar

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Location: D1.07

Korbinian Strimmer (Imperial)

An entropy approach for integrative genomics and network modeling

Multivariate regression approaches such as Seemingly Unrelated Regression (SUR) or Partial Least Squares (PLS) are commonly used in vertical data integration to jointly analyse different types of omics data measured on the same samples, such as SNP and gene expression data (eQTL) or proteomic and transcriptomic data.  However, these approaches may be difficult to apply and to interpret for computational and conceptual reasons.

Here we present a simple alternative approach to integrative genomics based on using relative entropy to characterise the overall association between two (or more) sets of omic data, and to infer the underlying corresponding association network among the individual covariates.  This approach is computationally inexpensive and can be applied to large-dimensional data sets.  A key and novel feature of our method is decomposition of the total strength between two or more groups of variables based on optimal whitening of the individual data sets.  Correspondingly, it may also be viewed as a special form of a latent-variable multivariate regression model.

We illustrate this approach by analysing metabolomic and transcriptomic data from the DILGOM study.


References:

A. Kessy, A. Lewin, and K. Strimmer. 2017. Optimal whitening and decorrelation. The American Statistician, to appear. http://dx.doi.org/10.1080/00031305.2016.1277159
T. Jendoubi and K. Strimmer. 2017. Data integration and network modeling: an entropy approach.  In prep.
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