In this spotlight, we review technical issues that compromise single-cell analysis of tissue
macrophages, including limited and unrepresentative yields, fragmentation and generation …

The increasing generation of population-level single-cell atlases has the potential to link
sample metadata with cellular data. Constructing such references requires integration of …

Advances in sequencing technologies have empowered epitranscriptomic profiling at the
single-base resolution. Putative RNA modification sites identified from a single high …

The advent of high-throughput single-cell genomics technologies has fundamentally
transformed biological sciences. Currently, millions of cells from complex biological tissues …

Langerhans cells (LCs) are distinct among phagocytes, functioning both as embryo-derived,
tissue-resident macrophages in skin innervation and repair and as migrating professional …

We investigate a fundamental aspect of machine vision: the measurement of features by
revisiting clustering one of the most classic approaches in machine learning and data …

The analysis of single-cell RNA sequencing (scRNA-seq) data often involves fitting a latent
variable model to learn a low-dimensional representation for the cells. Validating such a …

In typical single-cell RNA-seq (scRNA-seq) data analysis, a clustering algorithm is applied to
find putative cell types as clusters, and then a statistical differential expression (DE) test is …

The development of single cell RNA sequencing (scRNA-seq) has provided new
perspectives to study biological problems at the single cell level. One of the key issues in …

Clustering is a critical step in the analysis of single-cell data, as it enables the discovery and
characterization of putative cell types and states. However, most popular clustering tools do …