The rapid progress of protocols for sequencing single-cell transcriptomes over the past
decade has been accompanied by equally impressive advances in the computational …

Single-cell genomics has now made it possible to create a comprehensive atlas of human
cells. At the same time, it has reopened definitions of a cell's identity and of the ways in …

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

To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile
the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial …

Dimensionality reduction is standard practice for filtering noise and identifying relevant
features in large-scale data analyses. In biology, single-cell genomics studies typically begin …

Subpopulation identification, usually via some form of unsupervised clustering, is a
fundamental step in the analysis of many single-cell RNA-seq data sets. This has motivated …

Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-
transcriptome profiling of each individual single cell in a cell population. A core analysis of …

Rapid cost drops and advancements in next-generation sequencing have made profiling of
cells at individual level a conventional practice in scientific laboratories worldwide. Single …

We introduce alevin, a fast end-to-end pipeline to process droplet-based single-cell RNA
sequencing data, performing cell barcode detection, read map**, unique molecular …

During microbial infection, responding CD8+ T lymphocytes differentiate into heterogeneous
subsets that together provide immediate and durable protection. To elucidate the dynamic …