Differential polyadenylation sites (PAs) critically regulate gene expression, but their cell type–specific usage and spatial distribution in the brain have not been systematically characterized. Here, we present Infernape, which infers and quantifies …
Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic …
Replicability is a fundamental quality of scientific discoveries: we are interested in those signals that are detectable in different laboratories, different populations, across time etc. Unlike meta-analysis which accounts for experimental …
Recent genetic data can offer important insights into the roles of lipoprotein subfractions and particle sizes in preventing coronary artery disease (CAD), as previous observational studies have often reported conflicting results. We used the LD …
In the mammalian neocortex, projection neuron types are sequentially generated by the same pool of neural progenitors. How neuron type specification is related to developmental timing remains unclear. To determine whether temporal gene expression in …
Single cell sequencing technologies are transforming biomedical research. However, due to the inherent nature of the data, single cell RNA sequencing analysis poses new computational and statistical challenges. We begin with a survey of a selection …
Mendelian randomization (MR) is a method of exploiting genetic variation to unbiasedly estimate a causal effect in presence of unmeasured confounding. MR is being widely used in epidemiology and other related areas of population science. In this …
While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention. Here we propose a transfer …
We develop an empirical partially Bayes statistical analysis approach where instruments are weighted according to their strength; thus weak instruments bring less variation to the estimator. The estimator is highly efficient with many weak genetic …