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 RNA sequencing (scRNA-seq) has become a routinely used technique to
quantify the gene expression profile of thousands of single cells simultaneously. Analysis of …

To the Editor—Methods for analyzing single-cell data 1, 2, 3, 4 perform a core set of
computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state …

The paired measurement of RNA and surface proteins in single cells with cellular indexing
of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to …

The isocortex and hippocampal formation (HPF) in the mammalian brain play critical roles in
perception, cognition, emotion, and learning. We profiled∼ 1.3 million cells covering the …

The advent of single-cell RNA sequencing (scRNA-seq) technologies has revolutionized
transcriptomic studies. However, large-scale integrative analysis of scRNA-seq data remains …

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 …

Background Standard preprocessing of single-cell RNA-seq UMI data includes
normalization by sequencing depth to remove this technical variability, and nonlinear …

The increasing availability of large-scale single-cell atlases has enabled the detailed
description of cell states. In parallel, advances in deep learning allow rapid analysis of newly …

Deep learning architectures such as variational autoencoders have revolutionized the
analysis of transcriptomics data. However, the latent space of these variational …