Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses.

Background: Single cell RNA sequencing technology (scRNA-seq) has been proven useful in understanding cell-specific disease mechanisms. However, identifying genes of interest remains a key challenge. Pseudo-bulk methods that pool scRNA-seq counts in the same biological replicates have been commonly used to identify differentially expressed genes. However, such methods may lack power due to the limited sample size of scRNA-seq datasets, which can be prohibitively expensive. Results: Motivated by this, we proposed to use the Bayesian-frequentist hybrid (BFH) framework to increase the power. Conclusion: In our idiopathic pulmonary fibrosis (IPF) case study, we demonstrated that with a proper informative prior, the BFH approach identified more genes of interest. Furthermore, these genes were reasonable based on the current knowledge of IPF. Thus, the BFH offers a unique and flexible framework for future scRNA-seq analyses.

scRAA: the development of a robust and automatic annotation procedure for single-cell RNA sequencing data.

A critical task in single-cell RNA sequencing (scRNA-Seq) data analysis is to identify cell types from heterogeneous tissues. While the majority of classification methods demonstrated high performance in scRNA-Seq annotation problems, a robust and accurate solution is desired to generate reliable outcomes for downstream analyses, for instance, marker genes identification, differentially expressed genes, and pathway analysis. It is hard to establish a universally good metric. Thus, a universally good classification method for all kinds of scenarios does not exist. In addition, reference and query data in cell classification are usually from different experimental batches, and failure to consider batch effects may result in misleading conclusions. To overcome this bottleneck, we propose a robust ensemble approach to classify cells and utilize a batch correction method between reference and query data. We simulated four scenarios that comprise simple to complex batch effect and account for varying cell-type proportions. We further tested our approach on both lung and pancreas data. We found improved prediction accuracy and robust performance across simulation scenarios and real data. The incorporation of batch effect correction between reference and query, and the ensemble approach improve cell-type prediction accuracy while maintaining robustness. We demonstrated these through simulated and real scRNA-Seq data.

Yiqi Huoxue Recipe inhibits cardiomyocyte apoptosis caused by heart failure through Keap1/Nrf2/HIF-1α signaling pathway.

Yiqi Huoxue Recipe (YHR) is commonly used in China to treat diseases such as heart failure (HF). It has been reported that YHR can treat HF and has a certain protective effect on myocardial cell damage. The purpose of this study is to determine the cardioprotective effects of YHR on HF-induced apoptosis and to clarify its mechanism of action. Oxygen glucose deprivation/recovery (OGD/R) induces H9C2 cell apoptosis model. Ligation of the left anterior descending artery (LAD) coronary artery can induce an animal model of HF. We found that YHR protected H9C2 cells from OGD/R-induced apoptosis, reduced the level of reactive oxygen species (ROS) in H9C2 cells, and increased the mitochondrial membrane potential in H9C2 cells. The results of in vivo animal experiments showed that in the HF model, YHR could reduce infarct area of heart tissue and cardiomyocyte apoptosis rate. YHR regulated the expression of key apoptotic molecules, including increasing the ratio of Bcl-2 and Bax, and reducing the expression of Kelch-like ECH-associated protein 1 (Keap1) and caspase-3. Interestingly, YHR also regulates the expression of NF-E2-related factor 2 (Nrf2) in the nucleus. In summary, YHR may provide cardioprotective effects in heart failure through inhibiting the Keap1/Nrf2/HIF-1α apoptosis pathway.

Effects of multi-walled carbon nanotubes on the enantioselective toxicity of the chiral insecticide indoxacarb toward zebrafish (Danio rerio).

The mass production and usage of carbon nanotubes (CNTs) have led to the inevitable release into the environment, and the effects of CNTs on the toxicity of co-existing pollutants have been well documented. However, knowledge of the effects of CNTs on the enantioselective toxicity of chiral compounds is limited. Using zebrafish as an experimental model, the enantioselective expression of the apoptosis, CYP3C and EAAT-related genes were analyzed following exposure to multi-walled carbon nanotubes (MWCNTs) (0.05 and 0.5 mg/L), rac-/R-/S-indoxacarb (0.01 mg/L), or the combination of rac-/R-/S-indoxacarb mixed with MWCNTs for 28d. Sex-specific differences were observed in both the liver and brain of zebrafish. The expression of apoptosis and CYP3C-related genes was 16.55-44.29 times higher in the livers of males treated with R-indoxacarb than in S-indoxacarb treated groups. The EAAT-related genes were expressed at 1.38-2.56 times higher levels in the brain of females treated with R-indoxacarb than in S-indoxacarb-treated groups. In the presence of MWCNTs, the expression of caspase-3, cyp3c3, cyp3c4, eaat1a, eaat1b and eaat2 in the livers of males and brains of females treated with S-indoxacarb were 1.65-15.33 times higher than in fish treated with R-indoxacarb. Based on these results, MWCNTs affected the enantioselective toxicity of indoxacarb toward zebrafish.

A Bayesian approach to benefit-risk assessment in clinical studies with longitudinal data.

Chuang-Stein et al. proposed a method for benefit-risk assessment by formulating a five-category multinomial random variable with the first four categories as a combination of benefit and risk, and the fifth category to include subjects who withdraw from study. In this article, we subdivide the single withdrawal category into four sub-categories to consider withdrawal for different reasons. To analyze eight-category data, we propose a two-level multivariate-Dirichlet Model to identify benefit-risk measures at the population level. For individual benefit-risk, we use a log-odds ratio model with Dirichlet process prior. Two methods are applied to a hypothetical clinical trial data for illustration.