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

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

Recent technological advancements have enabled the profiling of a large number of
genome-wide features in individual cells. However, single-cell data present unique …

Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile
the transcriptomes of individual cells on a large scale. Early analyses of differential …

Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as
biological signals representing no or low gene expression, while others regard zeros as …

We present SCENIC, a computational method for simultaneous gene regulatory network
reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic …

In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts
present in each cell are sequenced. This leads to unreliable quantification of genes with low …

Many methods have been used to determine differential gene expression from single-cell
RNA (scRNA)-seq data. We evaluated 36 approaches using experimental and synthetic …

Motivation Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene
expression at the level of individual cells. However, preparing raw sequence data for further …

Cells from the same individual share common genetic and environmental backgrounds and
are not statistically independent; therefore, they are subsamples or pseudoreplicates. Thus …