Single-cell RNA-sequencing (scRNA-seq) has become a widely used tool for both basic and translational biomedical research. In scRNA-seq data analysis, cell type annotation is an essential but challenging step. In the past few years, several annotation tools have been developed. These methods require either labeled training/reference datasets, which are not always available, or a list of predefined cell subset markers, which are subject to biases. Thus, a user-friendly and precise annotation tool is still critically needed. We curated a comprehensive cell marker database named and developed a companion R package , an easy-to-use single cell annotation tool, to provide fast and accurate cell type annotation. The effectiveness of was demonstrated in 48 independent scRNA-seq datasets across different platforms and tissues. performs better than the currently available annotation tools on all the datasets tested. Additionally, the can be integrated with other tools and further improve their performance. and will help investigators to define the cell types in their scRNA-seq data in a streamlined and user-friendly way.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187171PMC
http://dx.doi.org/10.1101/2023.05.03.538463DOI Listing

Publication Analysis

Top Keywords

cell type
12
type annotation
12
single-cell rna-sequencing
8
data analysis
8
scrna-seq data
8
annotation tools
8
annotation tool
8
cell
7
annotation
7
single-cell mayo
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!