Recent technical advances have enabled RNA sequencing (RNA-seq) in single cells.
Exploratory studies have already led to insights into the dynamics of differentiation, cellular …

Motivation Cell type identification is one of the major goals in single cell RNA sequencing
(scRNA-seq). Current methods for assigning cell types typically involve the use of …

Single-cell transcriptomics (SCT) is a tour de force in the era of big omics data that has led to
the accumulation of massive cellular transcription data at an astounding resolution of single …

While only recently developed, the ability to profile expression data in single cells (scRNA-
Seq) has already led to several important studies and findings. However, this technology …

The abundance of new computational methods for processing and interpreting
transcriptomes at a single cell level raises the need for in silico platforms for evaluation and …

High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene
expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing …

Abstract Analysis of single-cell RNA-seq data begins with pre-processing of sequencing
reads to generate count matrices. We investigate algorithm choices for the challenges of pre …

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 …

Emerging methods for the accurate quantification of gene expression in individual cells hold
promise for revealing the extent, function and origins of cell-to-cell variability. Different high …

Here we introduce a mostly natural sequencing-by-synthesis (mnSBS) method for single-cell
RNA sequencing (scRNA-seq), adapted to the Ultima genomics platform, and systematically …