ScTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings.

ABSTRACT: Single-cell transcriptomics is rapidly advancing our understanding of the composition of complex tissues and biological cells, and single-cell RNA sequencing (scRNA-seq) holds great potential for identifying and characterizing the cell composition of complex tissues. Cell type identification by analyzing scRNA-seq data is mostly limited by time-consuming and irreproducible manual annotation. As scRNA-seq technology scales to thousands of cells per experiment, the exponential increase in the number of cell samples makes manual annotation more difficult. On the other hand, the sparsity of gene transcriptome data remains a major challenge. This paper applied the idea of the transformer to single-cell classification tasks based on scRNA-seq data. We propose scTransSort, a cell-type annotation

SUBMITTER: Jiao L 

PROVIDER: S-EPMC10136153 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature