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 …

Abstract scRNA-seq data analysis enables new possibilities for identification of novel cells,
specific characterization of known cells and study of cell heterogeneity. The performance of …

Motivation The transcriptomic diversity of the hundreds of cell types in the human body can
be analysed in unprecedented detail using single cell (SC) technologies. Though clustering …

Motivation A large number of studies have shown that clustering is a crucial step in scRNA-
seq analysis. Most existing methods are based on unsupervised learning without the prior …

scRNA-seq analysis relies heavily on single-cell clustering to perform many downstream
functions. Several machine learning methods have been proposed to improve the clustering …

Single-cell RNA-sequencing (scRNA-seq) technology allows us to explore cellular
heterogeneity in the transcriptome. Because most scRNA-seq data analyses begin with cell …

Motivation The rapid development of single-cell RNA sequencing (scRNA-seq) makes it
possible to study the heterogeneity of individual cell characteristics. Cell clustering is a vital …

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

Motivation While the workflow for primary analysis of single-cell RNA-seq (scRNA-seq) data
is well established, the secondary analysis of the feature-barcode matrix is usually done by …

Single-cell RNA sequencing (scRNA-seq) technologies have been driving the development
of algorithms of clustering heterogeneous cells. We introduce a novel clustering algorithm …