Bayesian inference articles from across Nature Portfolio

A global model compiling seabird surveys finds that 37 seabird species and around 25% of the world’s tropical seabird individuals nest on atolls, where they act as major nutrient pumps within tropical oceans.

Single-cell analysis of multi-condition cohorts requires modelling the interaction between sample variables and cell states. Here, authors develop GEDI to enable integration, cluster-free differential expression analysis and regulon analysis for both gene expression and alternative splicing modalities.

Studying the impact of SARS-CoV-2 on organelle dynamics may shed more light on the mechanisms of viral replication. Here, the authors combine label-free holotomographic microscopy with AI to study subcellular changes and organelle dynamics upon SARS-CoV-2 infection.

Long-term high-resolution data on social relationships, space use and microhabitat in a wild population of mice (Apodemus sylvaticus), accompanied by sampling of the gut microbiota, show that distinct sets of microorganisms dominate social and environmental transmission routes of microbiota. Microorganisms with low oxygen tolerance are more reliant on social transmission.

Inferring gene networks from discrete RNA counts across cells remains a complex problem. Following Bayesian non-parametrics, a computational framework is proposed to perform non-biased inference of transcription kinetics from single-cell RNA counting experiments.

Whole-genome sequencing (WGS) can be used to track the transmission of Neisseria gonorrhoeae in different sexual networks.

With the aid of informatics, microscopy is in the midst of a crucial evolution into a more quantitative and powerful technique.

To best interpret new sensory information, populations of sensory neurons must represent the lessons of past experience. How do they do this? The same solution to this problem is now reported in two very different sensory systems, providing a classic example of computational convergence.