Historical records suggest that horses inhabiting the island of Cheju in Korea are descendants of Mongolian horses introduced in 1276. Other studies, however, suggest that horses may have been present on the island prior to the Mongolian introduction. To determine the origin of the Cheju horses we used a phylogenetic analysis of sequences of the mitochondrial DNA (mtDNA) D-loop region, including tRNA Pro and parts of tRNA thr and tRNA Phe sequences (1102-bp excluding the tandem repeat region). Maximum parsimony and neighbor-joining trees were constructed using sequences determined for seven Cheju, four Mongolian, one Przewalskii and two Chinese Yunnan horses, and published sequences for one Swedish and three Thoroughbred horses. Donkey mtDNA was used as an outgroup. We found that the mtDNA D-loop sequence varies considerably within Mongolian, Cheju and Thoroughbred horse breeds, and that Cheju horses clustered with Mongolian horses as well as with horses from other distantly related breeds. On the basis of these findings we propose that horses on Cheju Island are of mixed origin in their maternal lineage, and that horses may have existed and been traded on the island before the Mongolian introduction.
Single-cell RNA sequencing (scRNA-seq) is a rapidly developing technology for studying gene expression at the individual cell level and is often used to identify subpopulations of cells. Although the use of scRNA-seq is steadily increasing in basic and translational research, there is currently no statistical model for calculating the optimal number of cells for use in experiments that seek to identify cell subpopulations. Here, we have developed a statistical method ncells for calculating the number of cells required to detect a rare subpopulation in a homogeneous cell population for the given type I and II error. ncells defines power as the probability of separation of subpopulations which is calculated from three user-defined parameters: the proportion of rare subpopulation, proportion of up-regulated marker genes of the subpopulation, and levels of differential expression of the marker genes. We applied ncells to the scRNA-seq data on dendritic cells and monocytes isolated from healthy blood donor to show its efficacy in calculating the optimal number of cells in identifying a novel subpopulation. -Pierre. (2013). Calculating sample size estimates for rna sequencing data. Journal of computational biology 20(12), 970-978. JeanKharchenko, Peter V, Silberstein, Lev and Scadden, David T. (2014). Bayesian approach to single-cell differential expression analysis. Nature methods 11 (7), 740-742.Law, Charity W, Chen, Yunshun, Shi, Wei and Smyth, Gordon K. (2014). Voom: precision weights unlock linear model analysis tools for rna-seq read counts. Genome biology 15(2), R29.Li, Wei Vivian and Li, Jingyi Jessica. (2018). An accurate and robust imputation method scimpute for single-cell rna-seq data. Nature communications 9(1), 997.
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