Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to
experiments representing a single condition, technology, or species to discover and define …

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep
biological understanding requires more than a taxonomic listing of clusters. As new methods …

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to
technical factors, including the number of molecules detected in each cell, which can confound …

The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided
with transformative new methods to profile genetic, epigenetic, spatial, proteomic and …

The simultaneous measurement of multiple modalities represents an exciting frontier for
single-cell genomics and necessitates computational methods that can define cellular states …

Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially
resolved, transcriptome-wide gene expression profiling across complex tissues are lacking…

INTRODUCTION Dendritic cells (DCs) and monocytes consist of multiple specialized
subtypes that play a central role in pathogen sensing, phagocytosis, and antigen presentation. …

The recent development of experimental methods for measuring chromatin state at single-cell
resolution has created a need for computational tools capable of analyzing these datasets. …

Cells, the basic units of biological structure and function, vary broadly in type and state.
Single-cell genomics can characterize cell identity and function, but limitations of ease and scale …

Advances in single-cell RNA sequencing (scRNA-seq) have allowed for comprehensive
analysis of the immune system. In this Review, we briefly describe the available scRNA-seq …