Julien Chiquet

Computational reproducibility, the possibility for independent researchers to exactly reproduce published empirical results, is fundamental to science. Despite its importance, the proportion of research articles aiming for reproducibility remains low and uneven across disciplines. Barriers include a perceived lack of incentives for researchers and journals, practical challenges in preparing reproducible materials, and the absence of harmonized standards of reproducibility processes and requirements by journals. Existing guidance is often highly technical, reaching mainly those already engaged with reproducible research. In this paper, we first synthesize evidence on the benefits of reproducibility for both authors and journals. Drawing on our extensive experience in reproducibility checking at various journals, we then put forward concise, pragmatic guidelines and the REPRO Checklist for creating reproducible analyses across disciplines. We further review current reproducibility policies of selected journals, illustrating the substantial heterogeneity in requirements and procedures. Motivated by the latter, we propose conceptual foundations for a harmonized multi-tier system of reproducibility standards that could support transparent, consistent assessment across journals and research communities. Our goal as journal (reproducibility) editors and contributors to the Mathematical Research Data Initiative is to encourage broader adoption of reproducibility practices, in particular by lowering practical barriers for authors and journals.

Deep generative models open new avenues for simulating realistic genomic data while preserving privacy and addressing data accessibility constraints. While previous studies have primarily focused on generating gene expression or haplotype data, this study explores generating genotype data in both unconditioned and phenotype-conditioned settings, which is inherently more challenging due to the discrete nature of genotype data.We developed and evaluated commonly used generative models, including Variational Autoencoders (VAEs), Diffusion Models, and Generative Adversarial Networks (GANs), and proposed adaptation tailored to discrete genotype data. We conducted extensive experiments on large-scale datasets, including all chromosomes from cow and multiple chromosomes from human. Model performance was assessed using a well-established set of metrics drawn from both deep learning and quantitative genetics literature. Our results show that these models can effectively capture genetic patterns and preserve genotype-phenotype association.As deep generative models are able to reproduce key characteristics of genotype data, they can serve as direct tools for genotype-phenotype simulation, while also enabling privacy-preserving data sharing. Our findings provide a comprehensive evaluation of these models and offer practical guidance for future research in genotype simulation.

High dimensional Gaussian graphical models provide a rigorous framework to describe a network of statistical dependencies between variables, such as genes in genomic regulation studies or species in ecology. Penalized methods, including the standard Graphical-Lasso, are well-known approaches to infer the parameters of these models. As the number of variables in the model grow, the network inference and interpretation become more complex. The Normal-Block model is discussed, a model that clusters variables and considers a network at the cluster level. This both adds structure to the network and reduces the number of parameters at stake, thereby easing the inference and interpretation of the underlying network. The approach builds on Graphical-Lasso to add a penalty on the network’s edges and limit the detection of spurious dependencies. A zero-inflated version of the model is also proposed to account for real-world data properties. For the inference procedure, two approaches are introduced, a two-step method based on existing approaches and an original, more rigorous method that simultaneously infers the clustering of variables and the association network between clusters, using a penalized variational Expectation-Maximization approach. An implementation of the model in R, in a package called normalblockr, is available on github (https://github.com/jeannetous/normalblockr). The results of the models in terms of clustering and network inference are presented, using both simulated data and various types of real-world data (proteomics and words occurrences on webpages).