Cell type identification from single-cell transcriptomes in melanoma.

ABSTRACT:

Background

Single-cell sequencing approaches allow gene expression to be measured at the single-cell level, providing opportunities and challenges to study the aetiology of complex diseases, including cancer.

Methods

Based on single-cell gene and lncRNA expression levels, we proposed a computational framework for cell type identification that fully considers cell dropout characteristics. First, we defined the dropout features of the cells and identified the dropout clusters. Second, we constructed a differential co-expression network and identified differential modules. Finally, we identified cell types based on the differential modules.

Results

The method was applied to single-cell melanoma data, and eight cell types were identified. Enrichment analysis of the candid

SUBMITTER: Huo Q 

PROVIDER: S-EPMC8596920 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature