Open Access
Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas
(Springer, 2020) Vicente Fuenzalida, Gonzalo; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
Multivariate models for spatial count data are currently receiving attention in disease mapping to model two or more diseases jointly. They have been thoroughly studied from a theoretical point of view, but their use in practice is still limited because they are computationally expensive and, in general, they are not implemented in standard software to be used routinely. Here, a new multivariate proposal, based on the recently derived M models for spatial data, is developed for spatio-temporal areal data. The model takes account of the correlation between the spatial and temporal patterns of the phenomena being studied, and it also includes spatio-temporal interactions. Though multivariate models have been traditionally fitted using Markov chain Monte Carlo techniques, here we propose to adopt integrated nested Laplace approximations to speed up computations as results obtained using both fitting techniques were nearly identical. The techniques are used to analyse two forms of crimes against women in India. In particular, we focus on the joint analysis of rapes and dowry deaths in Uttar Pradesh, the most populated Indian state, during the years 2001-2014.
Open Access
High-dimensional order-free multivariate spatial disease mapping
(Springer, 2023) Vicente Fuenzalida, Gonzalo; Adin Urtasun, Aritz; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA2001
Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains limited due to difficulties in implementation and computational burden. These problems are exacerbated when the number of areas is very large. In this paper, we introduce an order-free multivariate scalable Bayesian modelling approach to smooth mortality (or incidence) risks of several diseases simultaneously. The proposal partitions the spatial domain into smaller subregions, fits multivariate models in each subdivision and obtains the posterior distribution of the relative risks across the entire spatial domain. The approach also provides posterior correlations among the spatial patterns of the diseases in each partition that are combined through a consensus Monte Carlo algorithm to obtain correlations for the whole study region. We implement the proposal using integrated nested Laplace approximations (INLA) in the R package bigDM and use it to jointly analyse colorectal, lung, and stomach cancer mortality data in Spanish municipalities. The new proposal allows for the analysis of large datasets and yields superior results compared to fitting a single multivariate model. Additionally, it facilitates statistical inference through local homogeneous models, which may be more appropriate than a global homogeneous model when dealing with a large number of areas.
Open Access
Hybrid pine (Pinus attenuata × Pinus radiata) somatic embryogenesis: what do you prefer, mother or nurse?
(MDPI, 2021) Montalbán, Itziar A.; Castander Olarieta, Ander; Hargreaves, Cathy L.; Gough, Keiko; Reeves, Cathie B.; Ballekom, Shaf van; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Moncaleán, Paloma; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Development of hybrid pines of Pinus radiata D. Don for commercial forestry presents an opportunity to diversify the current resource of plant material. Climate change and different land uses pose challenges, making alternative species necessary to guarantee wood and non-wood products in the future. Pinus radiata var. cedrosensis × Pinus attenuata hybrid possesses different attributes, such as tolerance to drought conditions, better growth and resistance to snow damage at higher altitudes, and more importantly, different wood quality characteristics. Embryogenic cell lines were successfully initiated reciprocal hybrids using as initial explants megagametophytes, excised zygotic embryos and excised zygotic embryos plus nurse culture. However, the questions raised were: does the initiation environment affect the conversion to somatic plantlets months later? Does the mother tree or the cross have an effect on the conversion to somatic plantlets? In the present work we analysed the maturation rate, number of somatic embryos, germination rate, and the ex-vitro growth in cell lines derived from different initiation treatments, mother tree species, and crosses. Differences were not observed for in vitro parameters such as maturation and germination. However, significant differences were observed due to the mother tree species in relation with the ex-vitro growth rates observed, being higher those in which P. radiata acted as a mother. Moreover, embryogenic cell lines from these hybrids were stored at −80◦C and regenerated after one and five years.
Open Access
Alleviating confounding in spatio-temporal areal models with an application on crimes against women in India
(SAGE Publications, 2021) Adin Urtasun, Aritz; Goicoa Mangado, Tomás; Hodges, James S.; Schnell, Patrick M.; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
Assessing associations between a response of interest and a set of covariates in spatial areal models is the leitmotiv of ecological regression. However, the presence of spatially correlated random effects can mask or even bias estimates of such associations due to confounding effects if they are not carefully handled. Though potentially harmful, confounding issues have often been ignored in practice leading to wrong conclusions about the underlying associations between the response and the covariates. In spatio-temporal areal models, the temporal dimension may emerge as a new source of confounding, and the problem may be even worse. In this work, we propose two approaches to deal with confounding of fixed effects by spatial and temporal random effects, while obtaining good model predictions. In particular, restricted regression and an apparently—though in fact not—equivalent procedure using constraints are proposed within both fully Bayes and empirical Bayes approaches. The methods are compared in terms of fixed-effect estimates and model selection criteria. The techniques are used to assess the association between dowry deaths and certain socio-demographic covariates in the districts of Uttar Pradesh, India.
Open Access
Evaluating recent methods to overcome spatial confounding
(Springer, 2022) Urdangarin Iztueta, Arantxa; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The concept of spatial confounding is closely connected to spatial regression, although no general definition has been established. A generally accepted idea of spatial confounding in spatial regression models is the change in fixed effects estimates that may occur when spatially correlated random effects collinear with the covariate are included in the model. Different methods have been proposed to alleviate spatial confounding in spatial linear regression models, but it is not clear if they provide correct fixed effects estimates. In this article, we consider some of those proposals to alleviate spatial confounding such as restricted regression, the spatial+ model, and transformed Gaussian Markov random fields. The objective is to determine which one provides the best estimates of the fixed effects. Dowry death data in Uttar Pradesh in 2001, stomach cancer incidence data in Slovenia in the period 1995–2001 and lip cancer incidence data in Scotland between the years 1975–1980 are analyzed. Several simulation studies are conducted to evaluate the performance of the methods in different scenarios of spatial confounding. Results reflect that the spatial+ method seems to provide fixed effects estimates closest to the true value although standard errors could be inflated
Open Access
A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models
(Elsevier, 2024) Urdangarin Iztueta, Arantxa; Goicoa Mangado, Tomás; Kneib, Thomas; Ugarte Martínez, María Dolores; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2
Spatial areal models encounter the well-known and challenging problem of spatial confounding. This issue makes it arduous to distinguish between the impacts of observed covariates and spatial random effects. Despite previous research and various proposed methods to tackle this problem, finding a definitive solution remains elusive. In this paper, we propose a simplified version of the spatial+ approach that involves dividing the covariate into two components. One component captures large-scale spatial dependence, while the other accounts for short-scale dependence. This approach eliminates the need to separately fit spatial models for the covariates. We apply this method to analyse two forms of crimes against women, namely rapes and dowry deaths, in Uttar Pradesh, India, exploring their relationship with socio-demographic covariates. To evaluate the performance of the new approach, we conduct extensive simulation studies under different spatial confounding scenarios. The results demonstrate that the proposed method provides reliable estimates of fixed effects and posterior correlations between different responses.
Open Access
Induction of radiata pine somatic embryogenesis at high temperatures provokes a long-term decrease in DNA methylation/hydroxymethylation and differential expression of stress-related genes
(MDPI, 2020) Castander Olarieta, Ander; Pereira, Catia; Sales, Ester; Meijón, Mónica; Arrillaga, Isabel; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Cañal, María Jesús; Moncaleán, Paloma; Montalbán, Itziar A.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2
Based on the hypothesis that embryo development is a crucial stage for the formation of stable epigenetic marks that could modulate the behaviour of the resulting plants, in this study, radiata pine somatic embryogenesis was induced at high temperatures (23◦ C, eight weeks, control; 40◦ C, 4 h; 60◦ C, 5 min) and the global methylation and hydroxymethylation levels of emerging embryonal masses and somatic plants were analysed using LC-ESI-MS/ MS-MRM. In this context, the expression pattern of six genes previously described as stress-mediators was studied throughout the embryogenic process until plant level to assess whether the observed epigenetic changes could have provoked a sustained alteration of the transcriptome. Results indicated that the highest temperatures led to hypomethylation of both embryonal masses and somatic plants. Moreover, we detected for the first time in a pine species the presence of 5-hydroxymethylcytosine, and revealed its tissue specificity and potential involvement in heat-stress responses. Additionally, a heat shock protein-coding gene showed a down-regulation tendency along the process, with a special emphasis given to embryonal masses at first subculture and ex vitro somatic plants. Likewise, the transcripts of several proteins related with translation, oxidative stress response, and drought resilience were differentially expressed.
Open Access
Multivariate Bayesian spatio-temporal P-spline models to analyze crimes against women
(Oxford University Press, 2021) Vicente Fuenzalida, Gonzalo; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
Univariate spatio-temporal models for areal count data have received great attention in recent years for estimating risks. However, models for studying multivariate responses are less commonly used mainly due to the computational burden. In this article, multivariate spatio-temporal P-spline models are proposed to study different forms of violence against women. Modeling distinct crimes jointly improves the precision of estimates over univariate models and allows to compute correlations among them. The correlation between the spatial and the temporal patterns may suggest connections among the different crimes that will certainly benefit a thorough comprehension of this problem that affects millions of women around the world. The models are fitted using integrated nested Laplace approximations and are used to analyze four distinct crimes against women at district level in the Indian state of Maharashtra during the period 2001-2013.
Open Access
Cytokinins are involved in drought tolerance of Pinus radiata plants originating from embryonal masses induced at high temperatures
(Oxford University Press, 2021) Castander Olarieta, Ander; Moncaleán, Paloma; Pereira, Catia; Pěnčík, A.; Petřík, I.; Pavlović, I.; Novák, Ondrej; Strnad,Miroslav; Goicoa Mangado, Tomás; Ugarte Martínez, María Dolores; Montalbán, Itziar A.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Vegetative propagation through somatic embryogenesis is an effective method to produce elite varieties and can be applied as a tool to study the response of plants to different stresses. Several studies show that environmental changes during embryogenesis could determine future plant development. Moreover, we previously reported that physical and chemical conditions during somatic embryogenesis can determine the protein, hormone and metabolite profiles, as well as the micromorphological and ultrastructural organization of embryonal masses and somatic embryos. In this sense, phytohormones are key players throughout the somatic embryogenesis process as well as during numerous stress-adaptation responses. In this work, we first applied different higherature regimes (30 °C, 4 weeks; 40 °C, 4 days; 50 °C, 5 min) during induction of Pinus radiata D. Don somatic embryogenesis, together with control temperature (23 °C). Then, the somatic plants regenerated from initiated embryogenic cell lines and cultivated in greenhouse conditions were subjected to drought stress and control treatments to evaluate survival, growth and several physiological traits (relative water content, water potential, photosynthesis, stomatal conductance and transpiration). Based on those preliminary results, even more extreme higherature regimes were applied during induction (40 °C, 4 h; 50 °C, 30 min; 60 °C, 5 min) and the corresponding cytokinin profiles of initiated embryonal masses from different lines were analysed. The results showed that the temperature regime during induction had delayed negative effects on drought resilience of somatic plants as indicated by survival, photosynthetic activity and water- use efficiency. However, high temperatures for extended periods of time enhanced subsequent plant growth in well-watered conditions. Higherature regime treatments induced significant differences in the profile of total cytokinin bases, N6-isopentenyladenine, cis-zeatin riboside and trans-zeatin riboside. We concluded that phytohormones could be potential regulators of stress-response processes during initial steps of somatic embryogenesis and that they may have delayed implications in further developmental processes, determining the performance of the generated plants.
Open Access
Space-time interactions in bayesian disease mapping with recent tools: making things easier for practitioners
(Edward Arnold, 2022) Urdangarin Iztueta, Arantxa; Ugarte Martínez, María Dolores; Goicoa Mangado, Tomás; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
Spatio-temporal disease mapping studies the distribution of mortality or incidence risks in space and its evolution in time, and it usually relies on fitting hierarchical Poisson mixed models. These models are complex for practitioners as they generally require adding constraints to correctly identify and interpret the different model terms. However, including constraints may not be straightforward in some recent software packages. This paper focuses on NIMBLE, a library of algorithms that contains among others a configurable system for Markov chain Monte Carlo (MCMC) algorithms. In particular, we show how to fit different spatio-temporal disease mapping models with NIMBLE making emphasis on how to include sum-to-zero constraints to solve identifiability issues when including spatio-temporal interactions. Breast cancer mortality data in Spain during the period 1990-2010 is used for illustration purposes. A simulation study is also conducted to compare NIMBLE with R-INLA in terms of parameter estimates and relative risk estimation. The results are very similar but differences are observed in terms of computing time.