Cell atlases serve as vital references for automating cell labeling in new samples, yet
existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable …

Large single-cell RNA datasets have contributed to unprecedented biological insight. Often,
these take the form of cell atlases and serve as a reference for automating cell labeling of …

Single-cell RNA sequencing (scRNA-seq) is an emerging technology for profiling the gene
expression of thousands of cells at the single cell resolution. Currently, the labeling of cells …

Single-cell RNA-sequencing (scRNA-seq) techniques provide unprecedented opportunities
to investigate phenotypic and molecular heterogeneity in complex biological systems …

Motivation Single-cell RNA sequencing (scRNA-seq) technology has been widely applied to
capture the heterogeneity of different cell types within complex tissues. An essential step in …

Background Automatic cell type identification is essential to alleviate a key bottleneck in
scRNA-seq data analysis. While most existing classification tools show good sensitivity and …

Motivation Single-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful
in a variety of downstream biological applications, such as profiling gene expression at the …

Motivated by the growing number of single cell RNA sequencing datasets (scRNAseq)
revealing the cellular heterogeneity in complex tissues, particularly in brain and induced …

Cell type identification is a key computational challenge in single-cell RNA-sequencing
(scRNA-seq) data. To capitalize on the large collections of well-annotated scRNA-seq …

Single cell RNA sequencing (scRNA-seq) enables detailed examination of a cell's
underlying regulatory networks and the molecular factors contributing to its identity. We …