Motivation Single-cell RNA sequencing (scRNA-seq) has become a valuable tool for
studying cellular heterogeneity. However, the analysis of scRNA-seq data is challenging …

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing the cell-to-cell
variation and cellular dynamics in populations which appear homogeneous otherwise in …

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 sequencing (scRNA-seq) is a powerful approach which generates genome-
wide gene expression profiles at single cell resolution. Among its many applications, it …

A key challenge in single-cell RNA-sequencing (scRNA-seq) data analysis is batch effects
that can obscure the biological signal of interest. Although there are various tools and …

Single cell RNA-seq (scRNA-seq) experiments can provide a wealth of information about
heterogeneous, multi-cellular systems. However, this information has to be inferred …

Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can
aggregate complementary biological information from different datasets. However, most …

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

Single-cell RNA sequencing (scRNA-seq) has been widely used to characterize cell types
based on their average gene expression profiles. However, most studies do not consider …

Unsupervised clustering to identify distinct cell types is a crucial step in the analysis of
scRNA-seq data. Current clustering methods are dependent on a number of parameters …