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R-mode hierarchical clustering is a method for forming hierarchical groups of mutually exclusive subsets of variables. This R-mode cluster method identifies interrelationships between variables which are useful for variable selection and dimension reduction. Importantly, the method is based on metric elements defined on the sample space of variables. Consequently, hierarchical clustering of compositional parts should respect the particular geometry of the simplex. In this work, the connections between concepts such as distance, cluster representative, compositional biplot, and log-ratio basis are explored within the framework of the most popular R-mode agglomerative hierarchical clustering methods. The approach is illustrated in a paleoecological study to identify groups of species sharing similar behavior., This research was supported by the Ministerio de Ciencia e Innovación under the projects CODA-GENERA (Ref. PID2021-123833OB-I00) and CONBACO (Ref. PID2021-125380OBI00); and by the Agència de Gestió d’Ajuts Universitaris i de Recerca of the Generalitat de Catalunya under the project COSDA (Ref. 2021SGR01197)., Peer Reviewed, Postprint (published version)

Introduction: Shallow hydrothermal systems share many characteristics with their deep-sea counterparts, but their accessibility facilitates their study. One of the most studied shallow hydrothermal vent fields lies at Paleochori Bay off the coast of Milos in the Aegean Sea (Greece). It has been studied through extensive mapping and its physical and chemical processes have been characterized over the past decades. However, a thorough description of the microbial communities inhabiting the bay is still missing. Methods: We present the first in-depth characterization of the prokaryotic communities of Paleochori Bay by sampling eight different seafloor types that are distributed along the entire gradient of hydrothermal influence. We used deep sequencing of the 16S rRNA marker gene and complemented the analysis with qPCR quantification of the 16S rRNA gene and several functional genes to gain insights into the metabolic potential of the communities. Results: We found that the microbiome of the bay is strongly influenced by the hydrothermal venting, with a succession of various groups dominating the sediments from the coldest to the warmest zones. Prokaryotic diversity and abundance decrease with increasing temperature, and thermophilic archaea overtake the community. Discussion: Relevant geochemical cycles of the Bay are discussed. This study expands our limited understanding of subsurface microbial communities in acidic shallow-sea hydrothermal systems and the contribution of their microbial activity to biogeochemical cycling., Funding for the Saganaki dataset and JPA and G-SL was provided by NSF grants OIA-0939564 to the Center for Dark Energy Biosphere Investigations (C-DEBI). The CarDHynAl project was partially funded by INSU-CNRS Tellus and Syster Projects to JEs (2016) and J-EM (2018), and with partial support by RAMONES, funded by the European Union’s Horizon 2020 Research and Innovation Program, under grant agreement number 101017808 (to JEs and PN). The laboratory work was supported by the Trond Mohn Foundation and the University of Bergen through the Centre for Deep Sea research (grant TMS2020TMT13). VP-G and JEg are supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund [grant numbers PID2021- 125380OB-I00 and PID2021-123833OB-I00 (MCIN/AEI/ FEDER)]., Peer Reviewed, Postprint (published version)

Chronic kidney disease (CKD), a collective term for many causes of progressive renal failure, is increasing worldwide due to ageing, obesity and diabetes. However, these factors cannot explain the many environmental clusters of renal disease that are known to occur globally. This study uses data from the UK Renal Registry (UKRR) including CKD of uncertain aetiology (CKDu) to investigate environmental factors in Belfast, UK. Urbanisation has been reported to have an increasing impact on soils. Using an urban soil geochemistry database of elemental concentrations of potentially toxic elements (PTEs), we investigated the association of the standardised incidence rates (SIRs) of both CKD and CKD of uncertain aetiology (CKDu) with environmental factors (PTEs), controlling for social deprivation. A compositional data analysis approach was used through balances (a special class of log contrasts) to identify elemental balances associated with CKDu. A statistically significant relationship was observed between CKD with the social deprivation measures of employment, income and education (significance levels of 0.001, 0.01 and 0.001, respectively), which have been used as a proxy for socio-economic factors such as smoking. Using three alternative regression methods (linear, generalised linear and Tweedie models), the elemental balances of Cr/Ni and As/Mo were found to produce the largest correlation with CKDu. Geogenic and atmospheric pollution deposition, traffic and brake wear emissions have been cited as sources for these PTEs which have been linked to kidney damage. This research, thus, sheds light on the increasing global burden of CKD and, in particular, the environmental and anthropogenic factors that may be linked to CKDu, particularly environmental PTEs linked to urbanisation., The Tellus Project was funded by the Department of Enterprise, Trade and Investment (NI) and by the ‘Building Sustainable Prosperity’ fund of the EU Regional Development Programme. Chemical analysis of the urban soil samples was co-financed by the Northern Ireland Environment Agency. The Northern Ireland Statistics and Research Agency (NISRA) is thanked for providing the multiple deprivation measures (MDMs). The study received ethics approval in March 2018, NHS National Research Ethics Committee REC reference: 15/EM/0366. We thank all the UK Renal Centres for providing data to the UK Renal Registry. The views and opinions expressed in this article are those of the authors and do not reflect the views of the UK Renal Registry or UK Renal Association. JJE and VPG received financial support through the project METhods for COmpositional analysis of DAta (CODAMET), Ministerio de Ciencia, Innovación y Universidades, Spain (Ref: RTI2018-095518-B-C21/C22, 2019-2021)., Peer Reviewed, Postprint (published version)

Compositional data analysis is concerned with the relative importance of positive variables, expressed through their log-ratios. The literature has proposed a range of manners to compute log-ratios, some of whose interrelationships have never been reported when used as explanatory variables in regression models. This article shows their similarities and differences in interpretation based on the notion that one log-ratio has to be interpreted keeping all others constant. The article shows that centred, additive, pivot, balance and pairwise log-ratios lead to simple reparametrizations of the same model which can be combined to provide useful tests and comparable effect size estimates., Peer Reviewed

Measurements in sequencing studies are mostly based on counts. There is a lack of theoretical developments for the analysis and modelling of this type of data. Some thoughts in this direction are presented, which might serve as a seed. The main issues addressed are the compositional character of multinomial probabilities and the corresponding representation in orthogonal (isometric) coordinates, and modelling distributions for sequencing data taking into account possible effects of amplification techniques., Peer Reviewed, Postprint (published version)