A self-training interpretable cell type annotation framework using specific marker gene.

ABSTRACT:

Motivation

Recent advances in sequencing technology provide opportunities to study biological processes at a higher resolution. Cell type annotation is an important step in scRNA-seq analysis, which often relies on established marker genes. However, most of the previous methods divide the identification of cell types into two stages, clustering and assignment, whose performances are susceptible to the clustering algorithm, and the marker information cannot effectively guide the clustering process. Furthermore, their linear heuristic-based cell assignment process is often insufficient to capture potential dependencies between cells and types.

Results

Here, we present Interpretable Cell Type Annotation based on self-training (sICTA), a marker-based cell type annotation method

SUBMITTER: Chen H 

PROVIDER: S-EPMC11488977 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature