Recent advances in single-cell technologies have enabled high-throughput molecular
profiling of cells across modalities and locations. Single-cell transcriptomics data can now …

The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …

The recent boom in microfluidics and combinatorial indexing strategies, combined with low
sequencing costs, has empowered single-cell sequencing technology. Thousands—or even …

Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows
you to profile the whole transcriptome of a large number of individual cells. However, the …

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-
type-specific responses to perturbations such as disease, trauma, or experimental …

Advanced age is the main common risk factor for cancer, cardiovascular disease and
neurodegeneration. Yet, more is known about the molecular basis of any of these groups of …

Unsupervised clustering of single-cell RNA-sequencing data enables the identification of
distinct cell populations. However, the most widely used clustering algorithms are heuristic …

Lineage-tracing methods have enabled characterization of clonal dynamics in complex
populations, but generally lack the ability to integrate genomic, epigenomic and …

A wealth of specialized neuroendocrine command systems intercalated within the
hypothalamus control the most fundamental physiological needs in vertebrates …

Classical tests for a difference in means control the Type I error rate when the groups are
defined a priori. However, when the groups are instead defined via clustering, then applying …