ScSemiPLC: a semi-supervised learning framework for annotating single-cell RNA-Seq data by generating pseudo-labels through clustering.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) technology enables researchers to explore heterogeneity of diverse cell types within complex tissues at the single-cell resolution. Cell annotation, as a crucial step in scRNA-seq data analysis, provides biologically meaningful cell identity information for biological research. With the proliferation of publicly available datasets and the expansion of sequencing data scale, traditional annotation methods reliant on manual marker gene matching have become increasingly cumbersome and time-consuming. Consequently, efficient and convenient automated cell annotation methods are gradually becoming mainstream. In this paper, we propose a single-cell semi-supervised annotation training framework called scSemiPLC, which generates pseudo-labels through clusteri

SUBMITTER: Ma Q 

PROVIDER: S-EPMC12817951 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature