Benjamin J. Auerbach

TL;DR: It was found that SMCs transitioned to an intermediate cell state during atherosclerosis, which was also found in human atherosclerotic plaques of carotid and coronary arteries, and RA signaling was dysregulated in symptomatic human Atherosclerosis.

TL;DR: In this paper, the authors focus on the statistical and machine learning aspects for spatially resolved transcriptomics (SRT) data analysis and discuss how spatial location and histology information can be integrated with gene expression to advance our understanding of the transcriptional complexity.

TL;DR: A review of single-cell methods in human disease studies can be found in this paper, where the authors describe how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell.

TL;DR: Tempo as mentioned in this paper is a Bayesian variational inference approach that incorporates domain knowledge of the clock and quantifies phase estimation uncertainty, which has been shown to yield more accurate estimates of circadian phase than existing methods.

TL;DR: Baymer is an accurate polymorphism probability estimation algorithm that automatically adapts to data sparsity at different sequence context levels, thereby making efficient use of the available data.