No abstract

Multi-omics data, that is, datasets containing different types of high-dimensional molecular variables, are increasingly often generated for the investigation of various diseases. Nevertheless, questions remain regarding the usefulness of multi-omics data for the prediction of disease outcomes such as survival time. It is also unclear which methods are most appropriate to derive such prediction models. We aim to give some answers to these questions through a large-scale benchmark study using real data. Different prediction methods from machine learning and statistics were applied on 18 multi-omics cancer datasets (35 to 1000 observations, up to 100 000 variables) from the database ‘The Cancer Genome Atlas’ (TCGA). The considered outcome was the (censored) survival time. Eleven methods based on boosting, penalized regression and random forest were compared, comprising both methods that do and that do not take the group structure of the omics variables into account. The Kaplan–Meier estimate and a Cox model using only clinical variables were used as reference methods. The methods were compared using several repetitions of 5-fold cross-validation. Uno’s C-index and the integrated Brier score served as performance metrics. The results indicate that methods taking into account the multi-omics structure have a slightly better prediction performance. Taking this structure into account can protect the predictive information in low-dimensional groups—especially clinical variables—from not being exploited during prediction. Moreover, only the block forest method outperformed the Cox model on average, and only slightly. This indicates, as a by-product of our study, that in the considered TCGA studies the utility of multi-omics data for prediction purposes was limited. Contact:moritz.herrmann@stat.uni-muenchen.de, +49 89 2180 3198 Supplementary information: Supplementary data are available at Briefings in Bioinformatics online. All analyses are reproducible using R code freely available on Github.

We established a bacterial membrane model with monolayers of bacterial lipopolysaccharides (LPS Re and LPS Ra) and quantified their viscoelastic properties by using an interfacial stress rheometer coupled to a Langmuir film balance. LPS Re monolayers exhibited purely viscous behaviour in the absence of calcium ions, while the same monolayers underwent a viscous-to-elastic transition upon compression in the presence of Ca(2+). Our results demonstrated for the first time that LPSs in bacterial outer membranes can form two-dimensional elastic networks in the presence of Ca(2+). Different from LPS Re monolayers, the LPS Ra monolayers showed a very similar rheological transition both in the presence and absence of Ca(2+), suggesting that longer saccharide chains can form 2D physical gels even in the absence of Ca(2+). By exposure of the monolayers to the antimicrobial peptide protamine, we could directly monitor the differences in resistance of bacterial membranes according to the presence of calcium.