scSemiPLC:一个半监督的学习框架,用于通过集群生成伪标签来注释单细胞RNA-Seq数据

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QianYi Ma¹, LinJie Wang¹, Wei Li²

¹School of Computer Science and Engineering, Northeastern University, Shenyang, China.

|December 8, 2025

概括

我们介绍了scSemiPLC,这是一个新的半监督学习框架,用于单细胞RNA测序 (scRNA-seq) 数据中的自动细胞注释. 这种方法通过有效利用未标记的单元格来提高注释的准确性和效率.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

单细胞RNA测序 (scRNA-seq) 提供了高分辨率的细胞异质性见解.
手动的细胞注释是耗时的,并且与大规模的scRNA-seq数据集作斗争.
自动化的细胞注释方法对于高效的生物研究至关重要.

研究的目的:

为了开发一个高效准确的半监督细胞注释框架,scSemiPLC.
在单元格注释过程中有效地利用未标记的数据.
加强通过聚类生成的伪标签的利用.

主要方法:

建议 scSemiPLC,一个半监督的注释培训框架.
采用聚类来生成未标记数据的伪标签.
使用一致性规范化和伪标签加权来改善注释.

主要成果:

与现有方法相比,scSemiPLC显示出更高的注释准确性和稳定性.
该框架有效地从scRNA-seq数据中提取生物学上有意义的表示.
scSemiPLC在不同数量的细胞标签中显示出强度.

结论:

scSemiPLC为半监督细胞注释提供了一种新且有效的方法.
该方法显著优于经典的自动化和主流的半监督技术.
这个框架在单细胞基因组学领域推进了自动化的细胞注释.

关键词:

单元格注释单元格注释这是一个伪标签.在 scRNA-seq 数据中.半监督学习半监督学习

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相关概念视频

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...