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SummaryGenetic changes in human pluripotent stem cells (hPSCs) gained during culture can confound experimental results and potentially jeopardize the outcome of clinical therapies. Particularly common changes in hPSCs are trisomies of chromosomes 1, 12, 17, and 20. Thus, hPSCs should be regularly screened for such aberrations. Although a number of methods are used to assess hPSC genotypes, there has been no systematic evaluation of the sensitivity of the commonly used techniques in detecting low-level mosaicism in hPSC cultures. We have performed mixing experiments to mimic the naturally occurring mosaicism and have assessed the sensitivity of chromosome banding, qPCR, fluorescence in situ hybridization, and digital droplet PCR in detecting variants. Our analysis highlights the limits of mosaicism detection by the commonly employed methods, a pivotal requirement for interpreting the genetic status of hPSCs and for setting standards for safe applications of hPSCs in regenerative medicine.

We propose a model-based method to cluster units within a panel. The underlying model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units are clustered according to their dynamic behaviour, equilibrium level and the effect of covariates. Inference is addressed from a Bayesian perspective and model comparison is conducted using Bayes factors. Particular attention is paid to prior elicitation and posterior propriety. We suggest priors that require little subjective input and possess hierarchical structures that enhance inference robustness. We apply our methodology to GDP growth of European regions and to employment growth of Spanish firms.

Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research domain. Some regulatory agencies recently started to consider evidences of safety and efficacy on new medical products obtained using computer modelling and simulation (which is referred to as In Silico Trials); this has raised the attention in the computational medicine research community on the regulatory science aspects of this emerging discipline. But this poses a foundational problem: in the domain of biomedical research the use of computer modelling is relatively recent, without a widely accepted epistemic framing for model credibility. Also, because of the inherent complexity of living organisms, biomedical modellers tend to use a variety of modelling methods, sometimes mixing them in the solution of a single problem. In such context merely adopting credibility approaches developed within other research communities might not be appropriate. In this paper we propose a theoretical framing for assessing the credibility of a predictive models for In Silico Trials, which accounts for the epistemic specificity of this research field and is general enough to be used for different type of models.

We propose a semiparametric Bayesian model, based on penalized splines, for the recovery of the time-invariant topology of a causal interaction network from longitudinal data. Our motivation is inference of gene regulatory networks from low-resolution microarray time series, where existence of nonlinear interactions is well known. Parenthood relations are mapped by augmenting the model with kinship indicators and providing these with either an overall or gene-wise hierarchical structure. Appropriate specification of the prior is crucial to control the flexibility of the splines, especially under circumstances of scarce data; thus, we provide an informative, proper prior. Substantive improvement in network inference over a linear model is demonstrated using synthetic data drawn from ordinary differential equation models and gene expression from an experimental data set of the Arabidopsis thaliana circadian rhythm.

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Matlab code and data used in the article are available from http://go.warwick.ac.uk/majuarez/home/materials.

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