Bayesian independent component analysis recovers pathway signatures from blood metabolomics data.
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.
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Germany.
Jan
J
Krumsiek
Karsten
K
Suhre
Thomas
T
Illig
Jerzy
J
Adamski
Fabian J
FJ
Theis
D000465:Algorithms; D000597:Amino Acids, Branched-Chain / Q000097:blood; D001499:Bayes Theorem; D001774:Blood Chemical Analysis; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D008075:Lipoproteins, HDL / Q000097:blood; D055432:Metabolomics; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D025341:Principal Component Analysis
D000597:Amino Acids, Branched-Chain; D008075:Lipoproteins, HDL