Multiplexed single-cell RNA-seq analysis of multiple samples using pooling is a promising
experimental design, offering increased throughput while allowing to overcome batch …

A variety of methods have been developed to demultiplex pooled samples in a single cell
RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample …

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 …

Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for
differential gene expression and expression quantitative trait loci (eQTL) analyses. However …

One primary reason that makes single-cell RNA-seq analysis challenging is dropouts, where
the data only captures a small fraction of the transcriptome of each cell. Almost all …

Barcoding technology has greatly improved the throughput of cells and genes detected in
single‐cell RNA sequencing (scRNA‐seq) studies. Recently, increasing studies have paid …

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from
stochastic dropout and characteristic bimodal expression distributions in which expression is …

A growing number of single-cell sequencing platforms enable joint profiling of multiple omics
from the same cells. We present Cobolt, a novel method that not only allows for analyzing …

For heterogeneous tissues, measurements of gene expression through mRNA-Seq data are
confounded by relative proportions of cell types involved. In this note, we introduce an …

Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq)
data promises further biological insights that cannot be uncovered with individual datasets …