Bayesian independent component analysis recovers pathway signatures from blood metabolomics data

Jan Krumsiek, Karsten Suhre, Thomas Illig, Jerzy Adamski, Fabian J. Theis

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the source matrix of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a mixing matrix, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field.

Original languageEnglish
Pages (from-to)4120-4131
Number of pages12
JournalJournal of Proteome Research
Volume11
Issue number8
DOIs
Publication statusPublished - 3 Aug 2012
Externally publishedYes

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Keywords

  • Bayesian
  • bioinformatics
  • blood serum
  • independent component analysis
  • metabolomics
  • population cohorts
  • systems biology

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

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