Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments

MOTIVATION

The negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modelling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics.

RESULTS

The GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing better performance to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic.

AVAILABILITY AND IMPLEMENTATION

GPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. The version used for this paper is archived at https://doi.org/10.5281/zenodo.5027066.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

We introduce a computational framework that is able to describe general many-body coarse-grained (CG) interactions of molecules and use it to model the free energy surface of molecular liquids as a cluster expansion in terms of monomer, dimer, and trimer terms. The contributions to the free energy due to these terms are inferred from all-atom molecular dynamics (MD) data using Gaussian Approximation Potentials, a type of machine-learning model that employs Gaussian process regression. The resulting CG model is much more accurate than those possible using pair potentials. Though slower than the latter, our model can still be faster than all-atom simulations for solvent-free CG models commonly used in biomolecular simulations.

Vitamin D and type 2 diabetes mellitus

WHAT IS KNOWN AND OBJECTIVE

The deleterious effect of vitamin D deficiency on bone health has long been known. More recent studies suggest a deleterious effect of low vitamin D (hypovitaminosis D) on general health. And specific studies propose an association between hypovitaminosis D and the aetiology and progression of type 2 diabetes (T2DM). Given a commonly assumed lack of toxicity of vitamin D, routine measurement of plasma vitamin D and supplementation is rapidly becoming accepted general practice.

COMMENT

Authoritative practice guidelines have raised the level of vitamin D that is to be considered minimal for optimum health. This recommendation was based on a wealth of information and definitive evidence for skeletal benefits of vitamin D, but there was a lack of compelling evidence that hypovitaminosis D is causally related to extra-skeletal health outcomes such as diabetes. Hence, vitamin D supplementation for the purpose of achieving a level consistent with good health is evidence based, but measurement and supplementation for the purpose of preventing or treating T2DM is not.

WHAT IS NEW AND CONCLUSION

Although the maintenance of adequate vitamin D levels is desirable for all patients, we conclude that routine measurement of vitamin D level in every patient or initiating high-dose supplementation for the purpose of preventing or treating T2DM is not evidence based.