Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts
known as" doublets," which limit cell throughput and lead to spurious biological conclusions …

Motivation Single-cell RNA sequencing (scRNA-seq) technologies enable the study of
transcriptional heterogeneity at the resolution of individual cells and have an increasing …

In single-cell RNA sequencing (scRNA-seq), doublets form when two cells are encapsulated
into one reaction volume. The existence of doublets, which appear to be—but are not—real …

Methods for single-cell RNA sequencing (scRNA-seq) have greatly advanced in recent
years. While droplet-and well-based methods have increased the capture frequency of cells …

Single-cell RNA sequencing (scRNA-seq) measurements of gene expression enable an
unprecedented high-resolution view into cellular state. However, current methods often …

Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell
populations. However, these methods often generate multiplet artifacts, where two or more …

Single-cell computational pipelines involve two critical steps: organizing cells (clustering)
and identifying the markers driving this organization (differential expression analysis). State …

Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for
samples containing a mixture of genotypes, whether they are natural or experimentally …

Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in
different laboratories and at different times contain batch effects that may compromise the …

Identifying and removing multiplets are essential to improving the scalability and the
reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in …