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 observ...
Jan Krumsiek; Karsten Suhre; Thomas Illig; Jerzy Adamski; Fabian J. Theis
The successful applicability of gene therapy approaches will heavily rely on the development of efficient and safe nonviral gene delivery vectors, for example, cell-penetrating peptides (CPPs). CPPs can condense oligonucleotides and plasmid DNA (pDNA) into nanoparticles, thus allowing the t ... mehr
Dendrimers feature a defined number of terminal groups that may bind RNA or be functionalized with bioactive molecules. These competing uses of terminal groups may create an impasse if the requisite density of ligands depletes the number of terminal groups for binding sufficient RNA, or vic ... mehr
The development of clay bionanocomposites requires processing routes with nanostructural control. Moreover, moisture durability is a concern with water-soluble biopolymers. Here, oriented bionanocomposite coatings with strong in-plane orientation of clay platelets are for the first time pre ... mehr