Motivation Single cell RNA-seq (scRNA-seq) data contains a wealth of information which
has to be inferred computationally from the observed sequencing reads. As the ability to …

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 …

The advent of scRNA-seq technologies enables us to quantitatively characterize gene
expression at each single-cell level. The high resolution has thus far transformed many …

In the majority of downstream analysis pipelines for single-cell RNA sequencing (scRNA-
seq), techniques like dimensionality reduction and feature selection are employed to …

Standard single-cell RNA-sequencing analysis (scRNA-seq) workflows consist of converting
raw read data into cell-gene count matrices through sequence alignment, followed by …

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 RNA-Seq is a powerful technology that enables the transcriptomic profiling of the
different cell populations that make up complex tissues. However, the noisy and high …

Motivation Single-cell RNA sequencing (scRNA-seq) technology has revolutionized the way
research is done in biomedical sciences. It provides an unprecedented level of resolution …

Background Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity
with higher resolution than bulk RNA sequencing. Clustering analysis is critical to …

With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets
increasing exponentially and concurrent increased sparsity due to more zero counts being …