baredSC: Bayesian approach to retrieve expression distribution of single-cell data

Background The number of studies using single-cell RNA sequencing (scRNA-seq) is constantly growing. This powerful technique provides a sampling of the whole transcriptome of a cell. However, sparsity of the data can be a major hurdle when studying the distribution of the expression of a specific gene or the correlation between the expressions of two genes. Results We show that the main technical noise associated with these scRNA-seq experiments is due to the sampling, i.e., Poisson noise. We present a new tool named baredSC, for Bayesian Approach to Retrieve Expression Distribution of Single-Cell data, which infers the intrinsic expression distribution in scRNA-seq data using a Gaussian mixture model. baredSC can be used to obtain the distribution in one dimension for individual genes and in two dimensions for pairs of genes, in particular to estimate the correlation in the two genes’ expressions. We apply baredSC to simulated scRNA-seq data and show that the algorithm is able to uncover the expression distribution used to simulate the data, even in multi-modal cases with very sparse data. We also apply baredSC to two real biological data sets. First, we use it to measure the anti-correlation between Hoxd13 and Hoxa11, two genes with known genetic interaction in embryonic limb. Then, we study the expression of Pitx1 in embryonic hindlimb, for which a trimodal distribution has been identified through flow cytometry. While other methods to analyze scRNA-seq are too sensitive to sampling noise, baredSC reveals this trimodal distribution. Conclusion baredSC is a powerful tool which aims at retrieving the expression distribution of few genes of interest from scRNA-seq data.

Exceptional variation in the appearance of Common Murre eggs reveals their potential as identity signals

We studied the ground colors and maculations of 161 Common Murre (Uria aalge) eggs laid by 43 females in 3 small breeding groups on the cliffs of Skomer Island, Wales, in 2016–2018. Both the colors and maculations varied much more among than within females, providing quantitative evidence for the egg traits that might facilitate the parents’ ability to identify their own eggs on the crowded breeding ledges where the density is typically ~20 eggs m–2. Ground colors had a trimodal distribution of hue values (whitish to pale brown, pale blue, or vivid blue-green) and maculations ranged from none to complex squiggles and blotches. The eggs laid by each female in different years were similar to one another, and replacement eggs laid by females within years were also more similar to their first egg than to other eggs in the same breeding group. Egg appearance did not differ among the 3 breeding groups that we studied. Our findings thus support anecdotal observations that, within and between years, female Common Murres lay eggs that have similar ground colors and maculations. We do not, however, find evidence that there is much difference among the eggs laid in different parts of a colony.

baredSC: Bayesian Approach to Retrieve Expression Distribution of Single-Cell

The number of studies using single-cell RNA sequencing (scRNA-seq) is constantly growing. This powerful technique provides a sampling of the whole transcriptome of a cell. However, the commonly used droplet-based method often produces very sparse samples. Sparsity can be a major hurdle when studying the distribution of the expression of a specific gene or the correlation between the expressions of two genes. We show that the main technical noise associated with these scRNA-seq experiments is due to the sampling (i.e. Poisson noise). We developed a new tool named baredSC, for Bayesian Approach to Retrieve Expression Distribution of Single-Cell, which infers the intrinsic expression distribution in noisy single-cell data using a Gaussian mixture model (GMM). baredSC can be used to obtain the distribution in one dimension for individual genes and in two dimensions for pairs of genes, in particular to estimate the correlation in the two genes' expressions. We apply baredSC to simulated scRNA-seq data and show that the algorithm is able to uncover the expression distribution used to simulate the data, even in multi-modal cases with very sparse data. We also apply baredSC to two real biological data sets. First, we use it to measure the anti-correlation between Hoxd13 and Hoxa11, two genes with known genetic interaction in embryonic limb. Then, we study the expression of Pitx1 in embryonic hindlimb, for which a trimodal distribution has been identified through flow cytometry. While other methods to analyze scRNA-seq are too sensitive to sampling noise, baredSC reveals this trimodal distribution.

Application of NMR in Coal Reservoir Characterization

Nuclear Magnetic Resonance (NMR) can provide information about pore and fracture structures, porosity and permeability of reservoirs. It can deep into materials without destroying samples, with advantages such as rapid, accurate and high resolution. This paper introduced the experimental principles and carried out a series of NMR experiments based on high rank coal and low rank coal samples. Results show that: the T2 spectra of high rank coal samples have an independent trimodal distribution with the main peak located at the low T2 value section, indicating that high rank coal is dominated by micropores and transition pores; while the T2 spectrum of low rank coal samples show a continuous trimodal distribution with the main peak located at the high T2 value section, demonstrating the dominance of macropores, mesopores and fractures. The pore and fracture structures of low rank coals are significantly favorable than those of high rank coals.

An 18-kDa Translocator Protein (TSPO) Polymorphism Explains Differences in Binding Affinity of the PET Radioligand PBR28

[11C]PBR28 binds the 18-kDa Translocator Protein (TSPO) and is used in positron emission tomography (PET) to detect microglial activation. However, quantitative interpretations of signal are confounded by large interindividual variability in binding affinity, which displays a trimodal distribution compatible with a codominant genetic trait. Here, we tested directly for an underlying genetic mechanism to explain this. Binding affinity of PBR28 was measured in platelets isolated from 41 human subjects and tested for association with polymorphisms in TSPO and genes encoding other proteins in the TSPO complex. Complete agreement was observed between the TSPO Ala147Thr genotype and PBR28 binding affinity phenotype (P value = 3.1 times10−13). The TSPO Ala147Thr polymorphism predicts PBR28 binding affinity in human platelets. As all second-generation TSPO PET radioligands tested hitherto display a trimodal distribution in binding affinity analogous to PBR28, testing for this polymorphism may allow quantitative interpretation of TSPO PET studies with these radioligands.