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Background This work is aimed at improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis by generating a multi-omic disease signature. Methods/results We combined classic plasma biochemistry and plasma biomarkers with the transcriptional and epigenetic characterisation of cell types involved in thrombosis, obtained from two extreme phenotype groups (morbidly obese and lipodystrophy) and lean individuals to identify the molecular mechanisms at play, highlighting patterns of abnormal activation in innate immune phagocytic cells. Our analyses showed that extreme phenotype groups could be distinguished from lean individuals, and from each other, across all data layers. The characterisation of the same obese group, 6 months after bariatric surgery, revealed the loss of the abnormal activation of innate immune cells previously observed. However, rather than reverting to the gene expression landscape of lean individuals, this occurred via the establishment of novel gene expression landscapes. NETosis and its control mechanisms emerge amongst the pathways that show an improvement after surgical intervention. Conclusions We showed that the morbidly obese and lipodystrophy groups, despite some differences, shared a common cardiometabolic syndrome signature. We also showed that this could be used to discriminate, amongst the normal population, those individuals with a higher likelihood of presenting with the disease, even when not displaying the classic features.

Diverse applications - particularly in tumour subtyping - have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources. Cluster-Of-Clusters Analysis (COCA) is one such approach that has been widely applied in the context of tumour subtyping. However, the properties of COCA have never been systematically explored, and its robustness to the inclusion of noisy datasets, or datasets that define conflicting clustering structures, is unclear. We rigorously benchmark COCA, and present Kernel Learning Integrative Clustering (KLIC) as an alternative strategy. KLIC frames the challenge of combining clustering structures as a multiple kernel learning problem, in which different datasets each provide a weighted contribution to the final clustering. This allows the contribution of noisy datasets to be down-weighted relative to more informative datasets. We compare the performances of KLIC and COCA in a variety of situations through simulation studies. We also present the output of KLIC and COCA in real data applications to cancer subtyping and transcriptional module discovery. R packages \"klic\" and \"coca\" are available on the Comprehensive R Archive Network.

In this paper, we generalize the metric-based permutation test for the equality of covariance operators proposed by Pigoli et al. (2014) to the case of multiple samples of functional data. To this end, the non-parametric combination methodology of Pesarin and Salmaso (2010) is used to combine all the pairwise comparisons between samples into a global test. Different combining functions and permutation strategies are reviewed and analyzed in detail. The resulting test allows to make inference on the equality of the covariance operators of multiple groups and, if there is evidence to reject the null hypothesis, to identify the pairs of groups having different covariances. It is shown that, for some combining functions, step-down adjusting procedures are available to control for the multiple testing problem in this setting. The empirical power of this new test is then explored via simulations and compared with those of existing alternative approaches in different scenarios. Finally, the proposed methodology is applied to data from wheel running activity experiments, that used selective breeding to study the evolution of locomotor behavior in mice.

When using Markov chain Monte Carlo (MCMC) algorithms to perform inference for Bayesian clustering models, such as mixture models, the output is typically a sample of clusterings (partitions) drawn from the posterior distribution. In practice, a key challenge is how to summarise this output. Here we build upon the notion of the posterior similarity matrix (PSM) in order to suggest new approaches for summarising the output of MCMC algorithms for Bayesian clustering models. A key contribution of our work is the observation that PSMs are positive semi-definite, and hence can be used to define probabilistically-motivated kernel matrices that capture the clustering structure present in the data. This observation enables us to employ a range of kernel methods to obtain summary clusterings, and otherwise exploit the information summarised by PSMs. For example, if we have multiple PSMs, each corresponding to a different dataset on a common set of statistical units, we may use standard methods for combining kernels in order to perform integrative clustering. We may moreover embed PSMs within predictive kernel models in order to perform outcome-guided data integration. We demonstrate the performances of the proposed methods through a range of simulation studies as well as two real data applications. R code is available at https://github.com/acabassi/combine-psms.

Building classification models that predict a binary class label on the basis of high dimensional multi-omics datasets poses several challenges, due to the typically widely differing characteristics of the data layers in terms of number of predictors, type of data, and levels of noise. Previous research has shown that applying classical logistic regression with elastic-net penalty to these datasets can lead to poor results (Liu et al., 2018). We implement a two-step approach to multi-omic logistic regression in which variable selection is performed on each layer separately and a predictive model is then built using the variables selected in the first step. Here, our approach is compared to other methods that have been developed for the same purpose, and we adapt existing software for multi-omic linear regression (Zhao and Zucknick, 2020) to the logistic regression setting. Extensive simulation studies show that our approach should be preferred if the goal is to select as many relevant predictors as possible, as well as achieving prediction performances comparable to those of the best competitors. Our motivating example is a cardiometabolic syndrome dataset comprising eight 'omic data types for 2 extreme phenotype groups (10 obese and 10 lipodystrophy individuals) and 185 blood donors. Our proposed approach allows us to identify features that characterise cardiometabolic syndrome at the molecular level. R code is available at https://github.com/acabassi/logistic-regression-for-multi-omic-data.

Abstract Improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis are ongoing healthcare challenges. Using plasma biomarkers analysis coupled with the transcriptional and epigenetic characterisation of cell types involved in thrombosis, obtained from two extreme phenotype groups (obese and lipodystrophy) and comparing these to lean individuals and blood donors, the present study identifies the molecular mechanisms at play, highlighting patterns of abnormal activation in innate immune phagocytic cells and shows that extreme phenotype groups could be distinguished from lean individuals, and from each other, across all data layers. The characterisation of the same obese group, six months after bariatric surgery shows the loss of the patterns of abnormal activation of innate immune cells previously observed. However, rather than reverting to the gene expression landscape of lean individuals, this occurs via the establishment of novel gene expression landscapes. Netosis and its control mechanisms emerge amongst the pathways that show an improvement after surgical intervention. Taken together, by integrating across data layers, the observed molecular and metabolic differences form a disease signature that is able to discriminate, amongst the blood donors, those individuals with a higher likelihood of having cardiometabolic syndrome, even when not presenting with the classic features. Competing Interest Statement The authors have declared no competing interest. Footnotes * Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Mattia Frontini(m.frontini{at}exeter.ac.uk). * Text was updated for clarity, added new main figure 5.

Sette Fasce Tampone, realizzate secondo le indicazioni tecniche contenute nello Standard di condizionalità 5.2, in diversi ambiti e contesti climatici, sono state monitorate per un periodo biennale, al fine di quantificare la loro efficienza nella rimozione di azoto inorganico disciolto. Tale azoto è costituito per lo più da molecole di azoto nitrico che vengono veicolate principalmente tramite deflussi sub-superficiali da zone soggette a diverse pratiche colturali verso i corpi idrici superficiali adiacenti. Ad eccezione di due casi: i siti di Lodi e Metaponto, in tutti i sistemi monitorati è stata confermata la presenza di deflussi trasversali ai sistemi tampone, permanenti o temporanei, in grado di veicolare inquinanti e con portate variabili fra 919 e 8.590 m3/anno per 100 m lineari di FT. Le differenze di portata sono imputabili principalmente alla diversa superficie dei bacini agricoli afferenti ai sistemi tampone, che nei casi analizzati occupano superfici variabili fra il 3,6 ed il 33,3% del bacino agricolo. Sulla base dei bilanci di massa è emerso che dai campi coltivati giungono ai sistemi tampone percentuali variabili fra l’1,6 ed il 29,4% dell’azoto inorganico applicato. Ad eccezione dei sistemi in cui i maggiori deflussi non hanno alcuna interazione con la rizosfera (deflussi profondi) oppure non attraversano la Fascia Tampone, in tutti gli altri siti si registra un effetto di riduzione dell’azoto fra entrata ed uscita, con percentuali variabili fra il 33 ed il 62 %. Percentuali di abbattimento non elevate sono giustificate dallo scarso grado di maturazione dei siti monitorati, in molti casi recentemente convertiti a Fascia Tampone. Ancora una volta si conferma l’estrema eterogeneità delle risposte di questi sistemi ed il ruolo prioritario delle forzanti idrologiche nel determinarne l’efficacia. Seven buffer strips (BS) adjacent to fresh water bodies, realized according to the technical data contained in the Standard 5.2 of Cross-compliance, located in different areas and climate contexts, were monitored for a period of two years. It was done in order to quantify their effectiveness in removing dissolved inorganic nitrogen conveyed through sub- surface flow from field crops with different cultural practices. Except for two case studies (sites: Lodi and Metaponto) in all monitored systems has been confirmed an outflow, permanent or temporary, through the buffer systems, with flow rates ranging from 919 to 8590 m3y-1 every 100 meters of buffer stip. The differences in flow rate were mainly due to different sizes of agricultural basins related to buffer systems, which in the case studies ranging from 3.6 to 33.3%. Based on the mass balance, was found percentages of applied inorganic nitrogen, flowing from cultivated fields to the buffer systems, varied between 1.6 and 29.4%. In most of the sites was estimated nitrogen reduction between inlet and outlet of BS, with percentages ranging from 33 to 61.9%. The exceptions were the systems with groundwater that: or have no interaction with the rhizosphere (deep flow) or not crossing the buffer zone. Low percentages of removal shall be justified by the young stage of the monitored sites, being in many cases recently converted to buffer strip. This study confirms the extreme variability of these systems efficiency and the key role of hydrology drives its effectiveness.