The transcriptome of single cells can reveal important information about cellular states and
heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has …

Background Single-cell transcriptomics is rapidly advancing our understanding of the
cellular composition of complex tissues and organisms. A major limitation in most analysis …

Abstract We present the Single-Cell Clustering Assessment Framework, a method for the
automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) …

Automated cell type identification is a key computational challenge in single‐cell RNA‐
sequencing (sc RNA‐seq) data. To capitalise on the large collection of well‐annotated sc …

Advances in single-cell RNA-sequencing technology have resulted in a wealth of studies
aiming to identify transcriptomic cell types in various biological systems. There are multiple …

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for scientists of many
research disciplines due to its ability to elucidate the heterogeneous and complex cell-type …

Fast, robust and technology-independent computational methods are needed for supervised
cell type annotation of single-cell RNA sequencing data. We present SciBet, a supervised …

The cell type identification is among the most important tasks in single-cell RNA-sequencing
(scRNA-seq) analysis. Many in silico methods have been developed and can be roughly …

Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied
in biological and medical research. Identification of cell types in scRNA-seq data sets is an …

Currently most methods take manual strategies to annotate cell types after clustering the
single-cell RNA sequencing (scRNA-seq) data. Such methods are labor-intensive and …