A neural network-based method for exhaustive cell label assignment using single cell RNA-seq data.

ABSTRACT: The fast-advancing single cell RNA sequencing (scRNA-seq) technology enables researchers to study the transcriptome of heterogeneous tissues at a single cell level. The initial important step of analyzing scRNA-seq data is usually to accurately annotate cells. The traditional approach of annotating cell types based on unsupervised clustering and marker genes is time-consuming and laborious. Taking advantage of the numerous existing scRNA-seq databases, many supervised label assignment methods have been developed. One feature that many label assignment methods shares is to label cells with low confidence as "unassigned." These unassigned cells can be the result of assignment difficulties due to highly similar cell types or caused by the presence of unknown cell types. However, when unknown

SUBMITTER: Li Z 

PROVIDER: S-EPMC8766435 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature