Single-cell RNA-sequencing allows unbiased transcriptome profiling of individual cells,
enabling the analysis of genes expression at the cellular level. By uncovering cell …

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 …

Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in
single cells. It has enabled discovery of new cell types and established cell type atlases of …

Motivation Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of
thousands of individual cells, enabling the molecular exploration of tissues at the cellular …

Background A key challenge in the emerging field of single-cell RNA-Seq is to characterize
phenotypic diversity between cells and visualize this information in an informative manner. A …

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression
at the individual cell level, unraveling unprecedented insights into cellular heterogeneity …

Single-cell transcriptomics techniques, such as scRNA-seq, attempt to characterize gene
expression profiles in each cell of a heterogeneous sample individually. Due to growing …

Motivation Bioinformatic analysis of single-cell gene expression data is a rapidly evolving
field. Hundreds of bespoke methods have been developed in the past few years to deal with …

Visualization approaches transform high-dimensional data from single cell RNA sequencing
(scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell …

Normalization is an important step in the analysis of single-cell RNA-seq data. While no
single method outperforms all others in all datasets, the choice of normalization can have …